Health technology

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Health_technology 

Health technology is defined by the World Health Organization as the "application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures, and systems developed to solve a health problem and improve quality of lives". This includes pharmaceuticals, devices, procedures, and organizational systems used in the healthcare industry, as well as computer-supported information systems. In the United States, these technologies involve standardized physical objects, as well as traditional and designed social means and methods to treat or care for patients.

Development

Pre-digital Era

During a pre-digital era, patients suffered from inefficient and faulty clinical systems, processes, and conditions. Many medical errors happened in the past due to undeveloped health technologies. Some examples of these medical errors included adverse drug events and alarm fatigue. Alarm fatigue is caused when an alarm is repeatedly triggered or activated and one becomes desensitized to them. As the alarms were sometimes triggered by unimportant events in the past, nurses thought the alarm was not significant. Alarm fatigue is dangerous because it could lead to death and dangerous situations. With technological development, an intelligent program of integration and physiologic sense-making was developed and helped reduce the number of false alarms.

Also, with greater investment in health technologies, fewer medical errors happened. Outdated paper records were replaced in many healthcare organizations by electronic health records (EHR). According to studies, this change has brought a lot of changes to healthcare. Drug administration has improved, healthcare providers can now access medical information easier, provide better treatments and faster results, and save more costs.

Improvement

To help promote and expand the adoption of health information technology, Congress passed the HITECH act as part of the American Recovery and Reinvestment Act of 2009. HITECH stands for Health Information Technology for Economic and Clinical Health Act. It gave the department of health and human services the authority to improve healthcare quality and efficiency through the promotion of health IT. The act provided financial incentives or penalties to organizations to motivate healthcare providers to improve healthcare. The purpose of the act was to improve quality, safety, efficiency, and ultimately to reduce health disparities.

One of the main parts of the HITECH act was setting the meaningful use requirement, which required EHRs to allow for the electronic exchange of health information and to submit clinical information. The purpose of HITECH is to ensure the sharing of electronic information with patients and other clinicians are secure. HITECH also aimed to help healthcare providers have more efficient operations and reduce medical errors. The program consisted of three phases. Phase one aimed to improve healthcare quality, safety and efficiency. Phase two expanded on phase one and focused on clinical processes and ensuring the meaningful use of EHRs. Lastly, phase three focused on using Certified Electronic Health Record Technology (CEHRT) to improve health outcomes.

In 2014, the implementation of electronic records in US hospitals rose from a low percentage of 10% to a high percentage of 70%.

At the beginning of 2018, healthcare providers who participated in the Medicare Promoting Interoperability Program needed to report on Quality Payment Program requirements. The program focused more on interoperability and aimed to improve patient access to health information.

Privacy of Health Data

Phones that can track one's whereabouts, steps and more can serve as medical devices, and medical devices have much the same effect as these phones. In the research article, Privacy Attitudes among Early Adopters of Emerging Health Technologies by Cynthia Cheung, Matthew Bietz, Kevin Patrick and Cinnamon Bloss discovered people were willing to share personal data for scientific advancements, although they still expressed uncertainty about who would have access to their data. People are naturally cautious about giving out sensitive personal information. Phones add an extra level of threat according to the research article Security in Cloud-Computing-Based Mobile Health. Mobile devices continue to increase in popularity each year. The addition of mobile devices serving as medical devices increases the chances for an attacker to gain unauthorized information.

In 2015 the Medical Access and CHIP Reauthorization Act (MACRA) was passed which will be put into play in 2018 pushing towards electronic health records. Health Information Technology: Integration, Patient Empowerment, and Security by K. Marvin provided multiple different polls based on people's views on different types of technology entering the medical field most answers were responded with somewhat likely and very few completely disagreed on the technology being used in medicine. Marvin discusses the maintenance required to protect medical data and technology against cyber attacks as well as providing a proper data backup system for the information.

Patient Protection and Affordable Care Act (ACA) also known as Obamacare and health information technology health care is entering the digital era. Although with this development it needs to be protected. Both health information and financial information now made digital within the health industry might become a larger target for cyber-crime. Even with multiple different types of safeguards hackers somehow still find their way in so the security that is in place needs to constantly be updated to prevent these breaches.

Policy

With the increased use of IT systems, privacy violations were increasing rapidly due to the easier access and poor management. As such, the concern of privacy has become an important topic in healthcare. Privacy breaches happen when organizations do not protect the privacy of people's data. There are four types of privacy breaches, which include unintended disclosure by authorized personnel, intended disclosure by authorized personnel, privacy data loss or theft, and virtual hacking. It became more important to protect the privacy and security of patients' data because of the high negative impact on both individuals and organizations. Stolen personal information can be used to open credit cards or other unethical behaviors. Also, individuals have to spend a large amount of money to rectify the issue. The exposure of sensitive health information also can cause negative impacts on individuals' relationships, jobs, or other personal areas. For the organization, the privacy breach can cause loss of trust, customers, legal actions, and monetary fines.

Health Insurance Portability and Accountability Act of 1996

HIPAA stands for the Health Insurance Portability and Accountability Act of 1996. It is a U.S. healthcare legislation to direct how patient data is used and includes two major rules which are privacy and security of data. The privacy rule protects people's rights to privacy and security rule determines how to protect people's privacy.

According to the HIPAA Security Rule, it ensures that protected health information has three characteristics. They are confidentiality, availability, and integrity. Confidentiality indicates keeping the data confidential to prevent data loss or individuals who are unauthorized to access that protected health information. Availability allows people who are authorized to access the systems and networks when and where that information is in fact needed, such as natural disasters. In cases like this, protected health information is mostly backed up on to a separate server or printed out in paper copies, so people can access it. Lastly, Integrity ensures not using inaccurate information and improperly modified data due to a bad design system or process to protect the permanence of the patient data. The consequences of using inaccurate or improperly modified data could become useless or even dangerous.

Health Organizations of HIPAA also created administrative safeguards, physical safeguards, technical safeguards, to help protect the privacy of patients. Administrative safeguards typically include security management process, security personnel, information access management, workforce training and management, and evaluation of security policies and procedures. Security management processes are one of the important administrative safeguards' examples. It is essential to reduce the risks and vulnerabilities of the system. The processes are mostly the standard operating procedures written out as training manuals. The purpose is to educate people on how to handle protected health information in proper behavior.

Physical safeguards include lock and key, card swipe, positioning of screens, confidential envelopes, and shredding of paper copies. Lock and key are common examples of physical safeguards. They can limit physical access to facilities. Lock and key are simple, but they can prevent individuals from stealing medical records. Individuals must have an actual key to access to the lock.

Lastly, technical safeguards include access control, audit controls, integrity controls, and transmission security. The access control mechanism is a common example of technical safeguards. It allows the access of authorized personnel. The technology includes authentication and authorization. Authentication is the proof of identity that handles confidential information like username and password, while authorization is the act of determining whether a particular user is allowed to access certain data and perform activities in a system like add and delete.

Assessment

The concept of health technology assessment (HTA) was first coined in 1967 by the U.S. Congress in response to the increasing need to address the unintended and potential consequences of health technology, along with its prominent role in society. It was further institutionalized with the establishment of the congressional Office of Technology Assessment (OTA) in 1972–1973. HTA is defined as a comprehensive form of policy research that examines short- and long-term consequences of the application of technology, including benefits, costs, and risks. Due to the broad scope of technology assessment, it requires the participation of individuals besides scientists and health care practitioners such as managers and even the consumers.

Several American organizations provide health technology assessments and these include the Centers for Medicare and Medicaid Services (CMS) and the Veterans Administration through its VA Technology Assessment Program (VATAP). The models adopted by these institutions vary, although they focus on whether a medical technology being offered is therapeutically relevant. A study conducted in 2007 noted that the assessments still did not use formal economic analyses.

Aside from its development, however, assessment in the health technology industry has been viewed as sporadic and fragmented Issues such as the determination of products that needed to be developed, cost, and access, among others, also emerged. These - some argue - need to be included in the assessment since health technology is never purely a matter of science but also of beliefs, values, and ideologies. One of the mechanisms being suggested – either as an element of- or an alternative to the current TAs is bioethics, which is also referred to as the "fourth-generation" evaluation framework. There are at least two dimensions to an ethical HTA. The first involves the incorporation of ethics in the methodological standards employed to assess technologies while the second is concerned with the use of ethical framework in research and judgment on the part of the researchers who produce information used in the industry.

Health technology in the future

Fort Belvoir Community Hospital astounds with groundbreaking technology and devotion to patient care

The practice of medicine in the United States is currently in a major transition. This transition is due to many factors, but primarily because of the implementation and integration of health technologies into healthcare. In recent years, the widespread adoption of electronic health records (EHR) has caused a big impact on healthcare. "The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine's Computer Age," by Robert Wachter, aims to inform readers about this transition. Dr. Wachter has reviewed and made points about the future of health technologies in the book. He states that there will be fewer hospitals in the future. Due to the advancement of technologies, people will be more likely to go to hospitals for major surgeries or critical illness. In the future, nurse call buttons will not be needed in hospitals. Instead, robots will deliver medication, take care of patients, and administer the system. In the future, the electronic health record will look different. Healthcare providers will be able to enter the notes via speech-to-text transcriptions in real-time.

Dr. Wachter stated that information will be edited collaboratively across the patient-care team to improve the quality. Also, natural language processing will be more developed to help parse out keywords. In the future, patient data will reside in the cloud. Patients will be able to access their data from any device or location. The data is also accessible for authorized providers and individuals. In the future, big data analysis will constantly be improving. Artificial Intelligence and machine learning will be constantly improving and developing as it receives new data. Alerts will also be more intelligent and efficient than the current systems.

Medical technology

Medical technology, or "Medtech", encompasses a wide range of healthcare products and is used to treat diseases and medical conditions affecting humans. Such technologies are intended to improve the quality of healthcare delivered through earlier diagnosis, less invasive treatment options and reduction in hospital stays and rehabilitation times. Recent advances in medical technology have also focused on cost reduction. Medical technology may broadly include medical devices, information technology, biotech, and healthcare services.

The impacts of medical technology involve social and ethical issues. For example, physicians can seek objective information from technology rather than read subjective patient reports.

A major driver of the sector's growth is the consumerization of Medtech. Supported by the widespread availability of smartphones and tablets, providers can reach a large audience at low cost, a trend that stands to be consolidated as wearable technologies spread throughout the market.

In the years 2010–2015, venture funding has grown 200%, allowing US$11.7 billion to flow into health tech businesses from over 30,000 investors in the space.

Types of Technology

Medical technology has evolved into smaller portable devices, for instance, smartphones, touchscreens, tablets, laptops, digital ink, voice and face recognition and more. With this technology, innovations like electronic health records (EHR), health information exchange (HIE), Nationwide Health Information Network (NwHIN), personal health records (PHRs), patient portals, nanomedicine, genome-based personalized medicine, Geographical Positioning System (GPS), radio frequency identification (RFID), telemedicine, clinical decision support (CDS), mobile home health care and cloud computing came to exist.

Medical imaging and Magnetic resonance imaging (MRI) have been long used and proven Medical Technologies for medical research, patient reviewing, and treatment analyzing. With the advancement of imagining technologies, including the use of faster and more data, higher resolution images, and specialist automation software, the capabilities of medical imaging technology are growing and yielding better results. As the imaging hardware and software evolve this means that patients will need to use less contrasting agents, and also spend less time and money.

3D printing is another major development in healthcare. It can be used to produce specialized splints, prostheses, parts for medical devices and inert implants. The end goal of 3D printing is being able to print out customized replaceable body parts. In the following section, it will explain more about 3D printing in healthcare. New types of technologies also include artificial intelligence and robots.

3D printing

3D-printing Sliperiet

3D printing is the use of specialized machines, software programs and materials to automate the process of building certain objects. It is having a rapid growth in the prosthesis, medical implants, novel drug formulations and the bioprinting of human tissues and organs.

Companies such as Surgical Theater, provide new technology that is capable of capturing 3D virtual images of patients' brains to use as practice for operations. 3D printing allows medical companies to produce prototypes to practice before an operation created with artificial tissue.

3D printing technologies are great for bio-medicine because the materials that are used to make allow the fabrication with control over many design features. 3D printing also has the benefits of affordable customization, more efficient designs, and saving more time. 3D printing is precise to design pills to house several drugs due to different release times. The technology allows the pills to transport to the targeted area and degrade safely in the body. As such, pills can be designed more efficiently and conveniently. In the future, doctors might be giving a digital file of printing instructions instead of a prescription.

Besides, 3D printing will be more useful in medical implants. An example includes a surgical team that has designed a tracheal splint made by 3D printing to improve the respiration of a patient. This example shows the potential of 3D printing, which allows physicians to develop new implant and instrument designs easily.

Overall, in the future of medicine, 3D printing will be crucial as it can be used in surgical planning, artificial and prosthetic devices, drugs, and medical implants.

Artificial Intelligence

Main article: Artificial intelligence in healthcare

Artificial Intelligence (AI) is a program that enables computers to sense, reason, act and adapt. AI is not new, but it is growing rapidly and tremendously. AI can now deal with large data sets, solve problems, and provide more efficient operation. AI will be more potential in healthcare because it provides easier accessibility of information, improves healthcare, and reduce cost. There are different factors that drive AI in healthcare, but the two most important are economics and the advent of big data analytics. Costs, new payment options, and people's desire to improve health outcomes are the primary economic drivers of the AI. Based on the reading, AI can save $150 million annually in the US by 2026. Also, AI growth is expected to reach $6.6 million by 2021. Big data analytics is another big driver because we are in the age of big data. The data is extremely helpful to assist the integration of AI in healthcare because it ensures the execution of complex tasks, quality, and efficiency.

Applications of Artificial Intelligence

AI brings many benefits to the healthcare industry. AI helps to detect diseases, administer chronic conditions, deliver health services, and discover the drug. Also, AI has the potential to address important health challenges. In healthcare organizations, AI is able to plan and relocate resources. AI is able to match patients with healthcare providers that meet their needs. AI also helps improve the healthcare experience by using an app to identify patients' anxieties. In medical research, AI helps to analyze and evaluate the patterns and complex data. For instance, AI is important in drug discovery because it can search relevant studies and analyze different kinds of data. In clinical care, AI helps to detect diseases, analyze clinical data, publications, and guidelines. As such, AI aids to find the best treatments for the patients. Other uses of AI in clinical care include medical imaging, echocardiography, screening, and surgery.

Education

Medical virtual reality provides doctors multiple surgical scenarios that could happen and allows them to practice and prepare themselves for these situations. It also permits medical students a hands-on experience of different procedures without the consequences of making potential mistakes. ORamaVR is one of the leading companies that employ such medical virtual reality technologies to transform medical education (knowledge) and training (skills) to improve patient outcomes, reduce surgical errors and training time and democratize medical education and training.

Robots

Modern robotics have made huge progress and contribution to healthcare. Robots can help doctors in performing variety tasks. Robotics adoption is increasing tremendously in hospitals . The following are different ways to improve healthcare by using robots:

Robotic Spinal Surgery

Surgical robots are one of the robotic systems, which allows a surgeon to bend and rotate tissues in a more flexible and efficient way. The system is equipped with a 3D magnification vision system that can translate the hand movements of the surgeon to be precise in-order to perform a surgery with minimal incisions. Other robotics systems include the ability to diagnose and treat cancers. Many scientists began working on creating a next-generation robot system to assist the surgeon in performing knee and other bone replacement surgeries.

Assistant robots will also be important to help reduce the workload for regular medical staff. They can help nurses with simple and time-consuming tasks like carrying multiple racks of medicines, lab specimen or other sensitive materials.

Shortly, robotic pills are expected to reduce the number of surgeries. They can be moved inside a patient and delivered to the desired area. In addition, they can conduct biopsies, film the area and clear clogged arteries.

Overall, medical robots are extremely useful in assisting physicians; however, it might take time to be professionally trained working with medical robots and for the robots to respond to a clinician's instructions. As such, many researchers and startups were working constantly to provide solutions to these challenges.

Assistive Technologies

Assistive technologies are products designed to provide accessibility to individuals who have physical or cognitive problems or disabilities. They aim to improve the quality of life with assistive technologies. The range of assistive technologies is broad, ranging from low-tech solutions to physical hardware, to technical devices. There are four areas of assistive technologies, which include visual impairment, hearing impairment, physical limitations, cognitive limitations. There are many benefits of assistive technologies. They enable individuals to care for themselves, work, study, access information easily, improve independence and communication, and lastly participate fully in community life.

Consumer-driven healthcare software

As part of an ongoing trend towards consumer-driven healthcare, websites or apps which provide more information on health care quality and price to help patients choose their providers have grown. As of 2017, the sites with the most number of reviews in descending order included Healthgrades, Vitals.com, and RateMDs.com. Yelp, Google, and Facebook also host reviews with a large amount of traffic, although as of 2017 they had fewer medical reviews per doctor. Disputes around online reviews can lead to websites by health professionals alleging defamation.

Patient safety organizations and government programs which have historically assessed quality have made their data more accessible over the internet; notable examples include the HospitalCompare by CMS and the LeapFrog Group's hospitalsafetygrade.org.

Patient-oriented software may also help in other ways, including general education and appointments.

Disclosure of legal disputes including medical license complaints or malpractice lawsuits has also been made easier. Every state discloses license status and at least some disciplinary action to the public, but as of 2018, this was not accessible via the internet for a few states. Consumers can look up medical licenses in a national database, DocInfo.org, maintained by the medical licensing organizations which contains limited details. Other tools include DocFinder at docfinder.docboard.org and certificationmatters.org from the American Board of Medical Specialties. In some cases more information is available from a mailed or walk-in request than the internet; for example, the Medical Board of California removes dismissed accusations from website profiles, but these are still available from a written or walk-in request, or a lookup in a separate database. The trend to disclosure is controversial and generate significant public debate, particularly about opening up the National Practitioner Data Bank. In 1996, Massachusetts became the first state to require detailed disclosure of malpractice claims.

Self-Monitoring

Smartphones, tablets, and wearable computers have allowed people to monitor their health. These devices run numerous applications that are designed to provide simple health services and the monitoring of one's health with finding as critical problems to health as possible . An example of this is Fitbit, a fitness tracker that is worn on the user's wrist. This wearable technology allows people to track their steps, heart rate, floors climbed, miles walked, active minutes, and even sleep patterns. The data collected and analyzed allow users not just to keep track of their health but also help manage it, particularly through its capability to identify health risk factors.

There is also the case of the Internet, which serves as a repository of information and expert content that can be used to "self-diagnose" instead of going to their doctor. For instance, one need only enumerate symptoms as search parameters at Google and the search engine could identify the illness from the list of contents uploaded to the World Wide Web, particularly those provided by expert/medical sources. These advances may eventually have some effect on doctor visits from patients and change the role of the health professionals from "gatekeeper to secondary care to facilitator of information interpretation and decision-making." Apart from basic services provided by Google in Search, there are also companies such as WebMD that already offer dedicated symptom-checking apps.

Technology testing

All medical equipment introduced commercially must meet both United States and international regulations. The devices are tested on their material, effects on the human body, all components including devices that have other devices included with them, and the mechanical aspects.

The Medical Device User Fee and Modernization Act of 2002 was created to speed up the FDA's approval process of medical technology by introducing sponsor user fees for a faster review time with predetermined performance targets for review time. In addition, 36 devices and apps were approved by the FDA in 2016.

Careers

There are numerous careers to choose from in health technology in the USA. Listed below are some job titles and average salaries.

• Athletic Trainer,Mean Salary: $41,340. Athletic trainers treat athletes and other individuals who have sustained injuries. They also teach people how to prevent injuries. They perform their job under the supervision of physicians.
• Dental Hygienist, Mean Salary: $67,340. Dental hygienists provide preventive dental care and teach patients how to maintain good oral health. They usually work under dentists' supervision.
• Clinical Laboratory Scientists, Technicians, and Technologists, Mean Salary: $51,770. Lab technicians and technologists perform laboratory tests and procedures. Technicians work under the supervision of a laboratory technologist or laboratory manager.
• Nuclear Medicine Technologist, Mean Salary: $67,910. Nuclear medicine technologists prepare and administer radiopharmaceuticals, radioactive drugs, to patients to treat or diagnose diseases.
• Pharmacy Technician, Mean Salary: $28,070. Pharmacy technicians assist pharmacists with the preparation of prescription medications for customers.

Allied Professions

The term medical technology may also refer to the duties performed by clinical laboratory professionals or medical technologists in various settings within the public and private sectors. The work of these professionals encompasses clinical applications of chemistry, genetics, hematology, immunohematology (blood banking), immunology, microbiology, serology, urinalysis, and miscellaneous body fluid analysis. Depending on location, educational level, and certifying body, these professionals may be referred to as biomedical scientists, medical laboratory scientists (MLS), medical technologists (MT), medical laboratory technologists and medical laboratory technicians.

Definitions of knowledge

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Definitions_of_knowledge 

Definitions of knowledge try to determine the essential features of knowledge. Closely related terms are conception of knowledge, theory of knowledge, and analysis of knowledge. Some general features of knowledge are widely accepted among philosophers, for example, that it constitutes a cognitive success or an epistemic contact with reality and that propositional knowledge involves true belief. Most definitions of knowledge in analytic philosophy focus on propositional knowledge or knowledge-that, as in knowing that Dave is at home, in contrast to knowledge-how (know-how) expressing practical competence. However, despite the intense study of knowledge in epistemology, the disagreements about its precise nature are still both numerous and deep. Some of those disagreements arise from the fact that different theorists have different goals in mind: some try to provide a practically useful definition by delineating its most salient feature or features, while others aim at a theoretically precise definition of its necessary and sufficient conditions. Further disputes are caused by methodological differences: some theorists start from abstract and general intuitions or hypotheses, others from concrete and specific cases, and still others from linguistic usage. Additional disagreements arise concerning the standards of knowledge: whether knowledge is something rare that demands very high standards, like infallibility, or whether it is something common that requires only the possession of some evidence.

One definition that many philosophers consider to be standard, and that has been discussed since ancient Greek philosophy, is justified true belief (JTB). This implies that knowledge is a mental state and that it is not possible to know something false. There is widespread agreement among analytic philosophers that knowledge is a form of true belief. The idea that justification is an additionally required component is due to the intuition that true beliefs based on superstition, lucky guesses, or erroneous reasoning do not constitute knowledge. In this regard, knowledge is more than just being right about something. The source of most disagreements regarding the nature of knowledge concerns what more is needed. According to the standard philosophical definition, it is justification. The original account understands justification internalistically as another mental state of the person, like a perceptual experience, a memory, or a second belief. This additional mental state supports the known proposition and constitutes a reason or evidence for it. However, some modern versions of the standard philosophical definition use an externalistic conception of justification instead. Many such views affirm that a belief is justified if it was produced in the right way, for example, by a reliable cognitive process.

The justified-true-belief definition of knowledge came under severe criticism in the second half of the 20th century, mainly due to a series of counterexamples given by Edmund Gettier. Most of these examples aim to illustrate cases in which a justified true belief does not amount to knowledge because its justification is not relevant to its truth. This is often termed epistemic luck since it is just a fortuitous coincidence that the justified belief is also true. A few epistemologists have concluded from these counterexamples that the JTB definition of knowledge is deeply flawed and have sought a radical reconception of knowledge. However, many theorists still agree that the JTB definition is on the right track and have proposed more moderate responses to deal with the suggested counterexamples. Some hold that modifying one's conception of justification is sufficient to avoid them. Another approach is to include an additional requirement besides justification. On this view, being a justified true belief is a necessary but not a sufficient condition of knowledge. A great variety of such criteria has been suggested. They usually manage to avoid many of the known counterexamples but they often fall prey to newly proposed cases. It has been argued that, in order to circumvent all Gettier cases, the additional criterion needs to exclude epistemic luck altogether. However, this may require the stipulation of a very high standard of knowledge: that nothing less than infallibility is needed to exclude all forms of luck. The defeasibility theory of knowledge is one example of a definition based on a fourth criterion besides justified true belief. The additional requirement is that there is no truth that would constitute a defeating reason of the belief if the person knew about it. Other alternatives to the JTB definition are reliabilism, which holds that knowledge has to be produced by reliable processes, causal theories, which require that the known fact caused the knowledge, and virtue theories, which identify knowledge with the manifestation of intellectual virtues.

Not all forms of knowledge are propositional, and various definitions of different forms of non-propositional knowledge have also been proposed. But among analytic philosophers this field of inquiry is less active and characterized by less controversy. Someone has practical knowledge or know-how if they possess the corresponding competence or ability. Knowledge by acquaintance constitutes a relation not to a proposition but to an object. It is defined as familiarity with its object based on direct perceptual experience of it.

General characteristics and disagreements

Definitions of knowledge try to describe the essential features of knowledge. This includes clarifying the distinction between knowing something and not knowing it, for example, pointing out what is the difference between knowing that smoking causes cancer and not knowing this. Sometimes the expressions "conception of knowledge", "theory of knowledge", and "analysis of knowledge" are used as synonyms. Various general features of knowledge are widely accepted. For example, it can be understood as a form of cognitive success or epistemic contact with reality, and propositional knowledge may be characterized as "believing a true proposition in a good way". However, such descriptions are too vague to be very useful without further clarifications of what "cognitive success" means, what type of success is involved, or what constitutes "good ways of believing".

The disagreements about the nature of knowledge are both numerous and deep. Some of these disagreements stem from the fact that there are different ways of defining a term, both in relation to the goal one intends to achieve and concerning the method used to achieve it. These difficulties are further exacerbated by the fact that the term "knowledge" has historically been used for a great range of diverse phenomena. These phenomena include theoretical know-that, as in knowing that Paris is in France, practical know-how, as in knowing how to swim, and knowledge by acquaintance, as in personally knowing a celebrity. It is not clear that there is one underlying essence to all of these forms. For this reason, most definitions restrict themselves either explicitly or implicitly to knowledge-that, also termed "propositional knowledge", which is seen as the most paradigmatic type of knowledge.

Even when restricted to propositional knowledge, the differences between the various definitions are usually substantial. For this reason, the choice of one's conception of knowledge matters for questions like whether a particular mental state constitutes knowledge, whether knowledge is fairly common or quite rare, and whether there is knowledge at all. The problem of the definition and analysis of knowledge has been a subject of intense discussion within epistemology both in the 20th and the 21st century. The branch of philosophy studying knowledge is called epistemology.

Goals

An important reason for these disagreements is that different theorists often have very different goals in mind when trying to define knowledge. Some definitions are based mainly on the practical concern of being able to find instances of knowledge. For such definitions to be successful, it is not required that they identify all and only its necessary features. In many cases, easily identifiable contingent features can even be more helpful for the search than precise but complicated formulas. On the theoretical side, on the other hand, there are so-called real definitions that aim to grasp the term's essence in order to understand its place on the conceptual map in relation to other concepts. Real definitions are preferable on the theoretical level since they are very precise. However, it is often very hard to find a real definition that avoids all counterexamples. Real definitions usually presume that knowledge is a natural kind, like "human being" or "water" and unlike "candy" or "large plant". Natural kinds are clearly distinguishable on the scientific level from other phenomena. As a natural kind, knowledge may be understood as a specific type of mental state. In this regard, the term "analysis of knowledge" is used to indicate that one seeks different components that together make up propositional knowledge, usually in the form of its essential features or as the conditions that are individually necessary and jointly sufficient. This may be understood in analogy to a chemist analyzing a sample to discover its chemical compositions in the form of the elements involved in it. In most cases, the proposed features of knowledge apply to many different instances. However, the main difficulty for such a project is to avoid all counterexamples, i.e. there should be no instances that escape the analysis, not even in hypothetical thought experiments. By trying to avoid all possible counterexamples, the analysis of aims at arriving at a necessary truth about knowledge.

However, the assumption that knowledge is a natural kind that has precisely definable criteria is not generally accepted and some hold that the term "knowledge" refers to a merely conventional accomplishment that is artificially constituted and approved by society. In this regard, it may refer to a complex situation involving various external and internal aspects. This distinction is significant because if knowledge is not a natural kind then attempts to provide a real definition would be futile from the start even though definitions based merely on how the word is commonly used may still be successful. However, the term would not have much general scientific importance except for linguists and anthropologists studying how people use language and what they value. Such usage may differ radically from one culture to another. Many epistemologists have accepted, often implicitly, that knowledge has a real definition. But the inability to find an acceptable real definition has led some to understand knowledge in more conventionalist terms.

Methods

Besides these differences concerning the goals of defining knowledge, there are also important methodological differences regarding how one arrives at and justifies one's definition. One approach simply consists in looking at various paradigmatic cases of knowledge to determine what they all have in common. However, this approach is faced with the problem that it is not always clear whether knowledge is present in a particular case, even in paradigmatic cases. This leads to a form of circularity, known as the problem of the criterion: criteria of knowledge are needed to identify individual cases of knowledge and cases of knowledge are needed to learn what the criteria of knowledge are. Two approaches to this problem have been suggested: methodism and particularism. Methodists put their faith in their pre-existing intuitions or hypotheses about the nature of knowledge and use them to identify cases of knowledge. Particularists, on the other hand, hold that our judgments about particular cases are more reliable and use them to arrive at the general criteria. A closely related method, based more on the linguistic level, is to study how the word "knowledge" is used. However, there are numerous meanings ascribed to the term, many of which correspond to the different types of knowledge. This introduces the additional difficulty of first selecting the expressions belonging to the intended type before analyzing their usage.

Standards of knowledge

A further source of disagreement and difficulty in defining of knowledge is posed by the fact that there are many different standards of knowledge. The term "standard of knowledge" refers to how high the requirements are for ascribing knowledge to someone. To claim that a belief amounts to knowledge is to attribute a special epistemic status to this belief. But exactly what status this is, i.e. what standard a true belief has to pass to amount to knowledge, may differ from context to context. While some theorists use very high standards, like infallibility or absence of cognitive luck, others use very low standards by claiming that mere true belief is sufficient for knowledge, that justification is not necessary. For example, according to some standards, having read somewhere that the solar system has eight planets is a sufficient justification for knowing this fact. According to others, a deep astronomical understanding of the relevant measurements and the precise definition of "planet" is necessary. In the history of philosophy, various theorists have set an even higher standard and assumed that certainty or infallibility is necessary. For example, this is Rene Descartes' approach, who aims to find absolutely certain or indubitable first principles to act as the foundation of all subsequent knowledge. However, this outlook is uncommon in the contemporary approach. Contextualists have argued that the standards depend on the context in which the knowledge claim is made. For example, in a low-stake situation, a person may know that the solar system has 8 planets, even though the same person lacks this knowledge in a high-stake situation.

The question of the standards of knowledge is highly relevant to how common or rare knowledge is. According to the standards of everyday discourse, ordinary cases of perception and memory lead to knowledge. In this sense, even small children and animals possess knowledge. But according to a more rigorous conception, they do not possess knowledge since much higher standards need to be fulfilled. The standards of knowledge are also central to the question of whether skepticism, i.e. the thesis that we have no knowledge at all, is true. If very high standards are used, like infallibility, then skepticism becomes plausible. In this case, the skeptic only has to show that any putative knowledge state lacks absolute certainty, that while the actual belief is true, it could have been false. However, the more these standards are weakened to how the term is used in everyday language, the less plausible skepticism becomes.

Justified true belief

See also: Belief § Justified true belief

Many philosophers define knowledge as justified true belief (JTB). This definition characterizes knowledge in relation to three essential features: S knows that p if and only if (1) p is true, (2) S believes that p, and (3) this belief is justified. A version of this definition was considered and rejected by Socrates in Plato's Theaetetus. Today there is wide, though not universal, agreement among analytic philosophers that the first two criteria are correct, i.e. that knowledge implies true belief. Most of the controversy concerns the role of justification: what it is, whether it is needed, and what additional requirements it has to fulfill.

Truth

There is overwhelming agreement that knowledge implies truth. In this regard, one cannot know things that are not true even if the corresponding belief is justified and rational. So nobody can know that Hillary Clinton won the 2016 US Presidential election since this did not happen. This reflects the idea that knowledge is a relation through which a person stands in cognitive contact with reality. This contact implies that the known proposition is true.

Nonetheless, some theorists have also proposed that truth may not always be necessary for knowledge. In this regard, a justified belief that is widely held within a community may be seen as knowledge even if it is false. Another doubt is due to some cases in everyday discourse where the term is used to express a strong conviction. For example, a diehard fan of Hillary Clinton might claim that they knew she would win. But such examples have not convinced many theorists. Instead, this claim is probably better understood as an exaggeration than as an actual knowledge claim. Such doubts are minority opinions and most theorists accept that knowledge implies truth.

Belief

Knowledge is usually understood as a form of belief: to know something implies that one believes it. This means that the agent accepts the proposition in question. However, not all theorists agree with this. This rejection is often motivated by contrasts found in ordinary language suggesting that the two are mutually exclusive, as in "I do not believe that; I know it". Some see this difference in the strength of the agent's conviction by holding that belief is a weak affirmation while knowledge entails a strong conviction. However, the more common approach to such expressions is to understand them not literally but through paraphrases, for example, as "I do not merely believe that; I know it". This way, the expression is compatible with seeing knowledge as a form of belief. A more abstract counterargument defines "believing" as "thinking with assent" or as a "commitment to something being true" and goes on to show that this applies to knowledge as well. A different approach, sometimes termed "knowledge first", upholds the difference between belief and knowledge based on the idea that knowledge is unanalyzable and therefore cannot be understood in terms of the elements that compose it. But opponents of this view may simply reject it by denying that knowledge is unanalyzable. So despite the mentioned arguments, there is still wide agreement that knowledge is a form of belief.

A few epistemologists hold that true belief by itself is sufficient for knowledge. However, this view is not very popular and most theorists accepted that merely true beliefs do not constitute knowledge. This is based on various counterexamples, in which a person holds a true belief in virtue of faulty reasoning or a lucky guess.

Justification

The third component of the JTB definition is justification. It is based on the idea that having a true belief is not sufficient for knowledge, that knowledge implies more than just being right about something. So beliefs based on dogmatic opinions, blind guesses, or erroneous reasoning do not constitute knowledge even if they are true. For example, if someone believes that Machu Picchu is in Peru because both expressions end with the letter u, this true belief does not constitute knowledge. In this regard, a central question in epistemology concerns the additional requirements for turning a true belief into knowledge. There are many suggestions and deep disagreements within the academic literature about what these additional requirements are. A common approach is to affirm that the additional requirement is justification. So true beliefs that are based on good justification constitute knowledge, as when the belief about Machu Picchu is based on the individual's vivid recent memory of traveling through Peru and visiting Machu Picchu there. This line of thought has led many theorists to the conclusion that knowledge is nothing but true belief that is justified.

However, it has been argued that some knowledge claims in everyday discourse do not require justification. For example, when a teacher is asked how many of his students knew that Vienna is the capital of Austria in their last geography test, he may just cite the number of correct responses given without concern for whether these responses were based on justified beliefs. Some theorists characterize this type of knowledge as "lightweight knowledge" in order to exclude it from their discussion of knowledge.

A further question in this regard is how strong the justification needs to be for a true belief to amount to knowledge. So when the agent has some weak evidence for a belief, it may be reasonable to hold that belief even though no knowledge is involved. Some theorists hold that the justification has to be certain or infallible. This means that the justification of the belief guarantees the belief's truth, similar to how in a deductive argument, the truth of its premises ensures the truth of its conclusion. However, this view severely limits the extension of knowledge to very few beliefs, if any. Such a conception of justification threatens to lead to a full-blown skepticism denying that we know anything at all. The more common approach in the contemporary discourse is to allow fallible justification that makes the justified belief rationally convincing without ensuring its truth. This is similar to how ampliative arguments work, in contrast to deductive arguments. The problem with fallibilism is that the strength of justification comes in degrees: the evidence may make it somewhat likely, quite likely, or extremely likely that the belief is true. This poses the question of how strong the justification needs to be in the case of knowledge. The required degree may also depend on the context: knowledge claims in low-stakes situations, such as among drinking buddies, have lower standards than knowledge claims in high-stakes situations, such as among experts in the academic discourse.

Internalism and externalism

Besides the issue about the strength of justification, there is also the more general question about its nature. Theories of justification are often divided into internalism and externalism depending on whether only factors internal to the subject are responsible for justification. Commonly, an internalist conception is defended. This means that internal mental states of the subject justify beliefs. These states are usually understood as reasons or evidence possessed, like perceptual experiences, memories, rational intuition, or other justified beliefs.

One particular form of this position is evidentialism, which bases justification exclusively on the possession of evidence. It can be expressed by the claim that "Person S is justified in believing proposition p at time t if and only if S's evidence for p at t supports believing p". Some philosophers stipulate as an additional requirement to the possession of evidence that the belief is actually based on this evidence, i.e. that there is some kind of mental or causal link between the evidence and belief. This is often referred to as "doxastic justification". In contrast to this, having sufficient evidence for a true belief but coming to hold this belief based on superstition is a case of mere "propositional justification". Such a belief may not amount to knowledge even though the relevant evidence is possessed. A particularly strict version of internalism is access internalism. It holds that only states introspectively available to the subject's experience are relevant to justification. This means that deep unconscious states cannot act as justification. A closely related issue concerns the question of the internal structure of these states or how they are linked to each other. According to foundationalists, some mental states constitute basic reasons that can justify without being themselves in need of justification. Coherentists defend a more egalitarian position: what matters is not a privileged epistemic status of some special states but the relation to all other states. This means that a belief is justified if it fits into the person's full network of beliefs as a coherent part.

Philosophers have commonly espoused an internalist conception of justification. Various problems with internalism have led some contemporary philosophers to modify the internalist account of knowledge by using externalist conceptions of justification. Externalists include factors external to the person as well, such as the existence of a causal relation to the believed fact or to a reliable belief formation process. A prominent theory in this field is reliabilism, the theory that a true belief is justified if it was brought about by a reliable cognitive process that is likely to result in true beliefs. On this view, a true belief based on standard perceptual processes or good reasoning constitutes knowledge. But this is not the case if wishful thinking or emotional attachment is the cause.

However, not all externalists understand their theories as versions of the JTB account of knowledge. Some theorists defend an externalist conception of justification while others use a narrow notion of "justification" and understand externalism as implying that justification is not required for knowledge, for example, that the feature of being produced by a reliable process is not a form of justification but its surrogate. The same ambiguity is also found in the causal theory of knowledge.

In ancient philosophy

In Plato's Theaetetus, Socrates considers a number of theories as to what knowledge is, first excluding merely true belief as an adequate account. For example, an ill person with no medical training, but with a generally optimistic attitude, might believe that he will recover from his illness quickly. Nevertheless, even if this belief turned out to be true, the patient would not have known that he would get well since his belief lacked justification. The last account that Plato considers is that knowledge is true belief "with an account" that explains or defines it in some way. According to Edmund Gettier, the view that Plato is describing here is that knowledge is justified true belief. The truth of this view would entail that in order to know that a given proposition is true, one must not only believe the relevant true proposition, but must also have a good reason for doing so. One implication of this would be that no one would gain knowledge just by believing something that happened to be true.

Gettier problem and cognitive luck

The JTB definition of knowledge, as mentioned above, was already rejected in Plato's Theaetetus. The JTB definition came under severe criticism in the 20th century, mainly due to a series of counterexamples given by Edmund Gettier. This is commonly known as the Gettier problem and includes cases in which a justified belief is true because of lucky circumstances, i.e. where the person's reason for the belief is irrelevant to its truth. A well-known example involves a person driving along a country road with many barn facades. The driver does not know this and finally stops in front of the only real barn. The idea of this case is that they have a justified true belief that the object in front of them is a barn even though this does not constitute knowledge. The reason is that it was just a lucky coincidence that they stopped here and not in front of one of the many fake barns, in which case they wouldn't have been able to tell the difference either.

This and similar counterexamples aim to show that justification alone is not sufficient, i.e. that there are some justified true beliefs that do not amount to knowledge. A common explanation of such cases is based on cognitive or epistemic luck. The idea is that it is a lucky coincidence or a fortuitous accident that the justified belief is true. So the justification is in some sense faulty, not because it relies on weak evidence, but because the justification is not responsible for the belief's truth. Various theorists have responded to this problem by talking about warranted true belief instead. In this regard, warrant implies that the corresponding belief is not accepted on the basis of mere cognitive luck or accident. However, not everyone agrees that this and similar cases actually constitute counterexamples to the JTB definition: some have argued that, in these cases, the agent actually knows the fact in question, e.g. that the driver in the fake barn example knows that the object in front of them is a barn despite the luck involved. A similar defense is based on the idea that to insist on the absence of cognitive luck leads to a form of infallibilism about justification, i.e. that justification has to guarantee the belief's truth. However, most knowledge claims are not that strict and allow instead that the justification involved may be fallible.

The Gettier problem

Main article: Gettier problem

 

An Euler diagram representing a version of the traditional definition of knowledge that is adapted to the Gettier problem. This problem gives us reason to think that not all justified true beliefs constitute knowledge.

Edmund Gettier is best known for his 1963 paper entitled "Is Justified True Belief Knowledge?", which called into question the common conception of knowledge as justified true belief. In just two and a half pages, Gettier argued that there are situations in which one's belief may be justified and true, yet fail to count as knowledge. That is, Gettier contended that while justified belief in a true proposition is necessary for that proposition to be known, it is not sufficient.

According to Gettier, there are certain circumstances in which one does not have knowledge, even when all of the above conditions are met. Gettier proposed two thought experiments, which have become known as Gettier cases, as counterexamples to the classical account of knowledge. One of the cases involves two men, Smith and Jones, who are awaiting the results of their applications for the same job. Each man has ten coins in his pocket. Smith has excellent reasons to believe that Jones will get the job (the head of the company told him); and furthermore, Smith knows that Jones has ten coins in his pocket (he recently counted them). From this Smith infers: "The man who will get the job has ten coins in his pocket." However, Smith is unaware that he also has ten coins in his own pocket. Furthermore, it turns out that Smith, not Jones, is going to get the job. While Smith has strong evidence to believe that Jones will get the job, he is wrong. Smith therefore has a justified true belief that the man who will get the job has ten coins in his pocket; however, according to Gettier, Smith does not know that the man who will get the job has ten coins in his pocket, because Smith's belief is "...true by virtue of the number of coins in Jones's pocket, while Smith does not know how many coins are in Smith's pocket, and bases his belief... on a count of the coins in Jones's pocket, whom he falsely believes to be the man who will get the job." These cases fail to be knowledge because the subject's belief is justified, but only happens to be true by virtue of luck. In other words, he made the correct choice (believing that the man who will get the job has ten coins in his pocket) for the wrong reasons. Gettier then goes on to offer a second similar case, providing the means by which the specifics of his examples can be generalized into a broader problem for defining knowledge in terms of justified true belief.

There have been various notable responses to the Gettier problem. Typically, they have involved substantial attempts to provide a new definition of knowledge that is not susceptible to Gettier-style objections, either by providing an additional fourth condition that justified true beliefs must meet to constitute knowledge, or proposing a completely new set of necessary and sufficient conditions for knowledge. While there have been far too many published responses for all of them to be mentioned, some of the most notable responses are discussed below.

Responses and alternative definitions

The problems with the JTB definition of knowledge have provoked diverse responses. Strictly speaking, most contemporary philosophers deny the JTB definition of knowledge, at least in its exact form. Edmund Gettier's counterexamples were very influential in shaping this contemporary outlook. They usually involve some form of cognitive luck whereby the justification is not responsible or relevant to the belief being true. Some responses stay within the standard definition and try to make smaller modifications to mitigate the problems, for example, concerning how justification is defined. Others see the problems as insurmountable and propose radical new conceptions of knowledge, many of which do not require justification at all. Between these two extremes, various epistemologists have settled for a moderate departure from the standard definition. They usually accept that it is a step in the right direction: justified true belief is necessary for knowledge. However, they deny that it is sufficient. This means that knowledge always implies justified true belief but that not every justified true belief constitutes knowledge. Instead, they propose an additional fourth criterion needed for sufficiency. The resulting definitions are sometimes referred to as JTB+X accounts of knowledge. A closely related approach is to replace justification with warrant, which is then defined as justification together with whatever else is needed to amount to knowledge.

The goal of introducing an additional criterion is to avoid counterexamples in the form of Gettier cases. Numerous suggestions for such a fourth feature have been made, for example, the requirement that the belief is not inferred from a falsehood. While alternative accounts are often successful at avoiding many specific cases, it has been argued that most of them fail to avoid all counterexamples because they leave open the possibility of cognitive luck. So while introducing an additional criterion may help exclude various known examples of cognitive luck, the resulting definition is often still susceptible to new cases. The only way to avoid this problem is to ensure that the additional criterion excludes cognitive luck. This is often understood in the sense that the presence of the feature has to entail the belief's truth. So if it is possible that a belief has this feature without being true, then cases of cognitive luck are possible in which a true belief has this feature but is not true because of this feature. The problem is avoided by defining knowledge as non-accidentally true belief. A similar approach introduces an anti-luck condition: the belief is not true merely by luck. But it is not clear how useful these definitions are unless a more precise definition of "non-accidental" or "absence of luck" could be provided. This vagueness makes the application to non-obvious cases difficult. A closely related and more precise definition requires that the belief is safely formed, i.e. that the process responsible would not have produced the corresponding belief if it was not true. This means that, whatever the given situation is like, this process tracks the fact. Simon Blackburn proposes that those who have a justified true belief 'through a defect, flaw, or failure' fail to have knowledge. Richard Kirkham suggests that our definition of knowledge requires that the evidence for the belief necessitates its truth.

Defeasibility theory

Defeasibility theories of knowledge introduce an additional condition based on defeasibility in order to avoid the different problems faced by the JTB accounts. They emphasize that, besides having a good reason for holding the belief, it is also necessary that there is no defeating evidence against it. This is usually understood in a very wide sense: a justified true belief does not amount to knowledge when there is a truth that would constitute a defeating reason of the belief if the person knew about it. This wide sense is necessary to avoid Gettier cases of cognitive luck. So in the barn example above, it explains that the belief does not amount to knowledge because, if the person were aware of the prevalence of fake barns in this area, this awareness would act as a defeater of the belief that this one particular building is a real barn. In this way, the defeasibility theory can identify accidentally justified beliefs as unwarranted. One of its problems is that it excludes too many beliefs from knowledge. This concerns specifically misleading defeaters, i.e. truths that would give the false impression to the agent that one of their reasons was defeated. According to Keith Lehrer, cases of cognitive luck can be avoided by requiring that the justification does not depend on any false statement. On his view, "S knows that p if and only if (i) it is true that p, (ii) S accepts that p, (iii) S is justified in accepting that p, and (iv) S is justified in accepting p in some way that does not depend on any false statement".

Reliabilism and causal theory

Reliabilistic and causal theories are forms of externalism. Some versions only modify the JTB definition of knowledge by reconceptualizing what justification means. Others constitute further departures by holding that justification is not necessary, that reliability or the right causal connections act as replacements of justification. According to reliabilism, a true belief constitutes knowledge if it was produced by a reliable process or method. Putative examples of reliable processes are regular perception under normal circumstances and the scientific method. Defenders of this approach affirm that reliability acts as a safeguard against lucky coincidence. Virtue reliabilism is a special form of reliabilism in which intellectual virtues, such as properly functioning cognitive faculties, are responsible for producing knowledge. Reliabilists have struggled to give an explicit and plausible account of when a process is reliable. One approach defines it through a high success rate: a belief-forming process is reliable within a certain area if it produces a high ratio of true beliefs in this area. Another approach understands reliability in terms of how the process would fare in counterfactual scenarios. Arguments against both of these definitions have been presented. A further criticism is based on the claim that reliability is not sufficient in cases where the agent is not in possession of any reasons justifying the belief even though the responsible process is reliable.

The causal theory of knowledge holds that the believed fact has to cause the true belief in the right way for the belief to amount to knowledge. For example, the belief that there is a bird in the tree may constitute knowledge if the bird and the tree caused the corresponding perception and belief. The causal connection helps to avoid some cases of cognitive luck since the belief is not accidental anymore. However, it does not avoid all of them, as can be seen in the fake barn example above, where the perception of the real barn caused the belief about the real barn even though it was a lucky coincidence. Another shortcoming of the causal theory is that various beliefs are knowledge even though a causal connection to the represented facts does not exist or may not be possible. This is the case for beliefs in mathematical propositions, like that "2 + 2 = 4", and in certain general propositions, like that "no elephant smaller than a kitten".

Virtue-theoretic definition

Virtue-theoretic approaches try to avoid the problem of cognitive luck by seeing knowledge as a manifestation of intellectual virtues. On this view, virtues are properties of a person that aim at some good. In the case of intellectual virtues, the principal good is truth. In this regard, Linda Zagzebski defines knowledge as "cognitive contact with reality arising out of acts of intellectual virtue". A closely related approach understands intellectual virtues in analogy to the successful manifestation of skills. This is helpful to clarify how cognitive luck is avoided. For example, an archer may hit the bull's eye due to luck or because of their skill. Based on this line of thought, Ernest Sosa defines knowledge as a belief that "is true in a way manifesting, or attributable to, the believer's skill".

"No false premises" response

See also: Gettier problem § Trouble for the "no false premises" approach

One of the earliest suggested replies to Gettier, and perhaps the most intuitive ways to respond to the Gettier problem, is the "no false premises" response, sometimes also called the "no false lemmas" response. Most notably, this reply was defended by David Malet Armstrong in his 1973 book, Belief, Truth, and Knowledge. The basic form of the response is to assert that the person who holds the justified true belief (for instance, Smith in Gettier's first case) made the mistake of inferring a true belief (e.g. "The person who will get the job has ten coins in his pocket") from a false belief (e.g. "Jones will get the job"). Proponents of this response therefore propose that we add a fourth necessary and sufficient condition for knowledge, namely, "the justified true belief must not have been inferred from a false belief".

This reply to the Gettier problem is simple, direct, and appears to isolate what goes wrong in forming the relevant beliefs in Gettier cases. However, the general consensus is that it fails. This is because while the original formulation by Gettier includes a person who infers a true belief from a false belief, there are many alternate formulations in which this is not the case. Take, for instance, a case where an observer sees what appears to be a dog walking through a park and forms the belief "There is a dog in the park". In fact, it turns out that the observer is not looking at a dog at all, but rather a very lifelike robotic facsimile of a dog. However, unbeknownst to the observer, there is in fact a dog in the park, albeit one standing behind the robotic facsimile of a dog. Since the belief "There is a dog in the park" does not involve a faulty inference, but is instead formed as the result of misleading perceptual information, there is no inference made from a false premise. It therefore seems that while the observer does in fact have a true belief that her perceptual experience provides justification for holding, she does not actually know that there is a dog in the park. Instead, she just seems to have formed a "lucky" justified true belief.

Infallibilist response

One less common response to the Gettier problem is defended by Richard Kirkham, who has argued that the only definition of knowledge that could ever be immune to all counterexamples is the infallibilist definition. To qualify as an item of knowledge, goes the theory, a belief must not only be true and justified, the justification of the belief must necessitate its truth. In other words, the justification for the belief must be infallible.

While infallibilism is indeed an internally coherent response to the Gettier problem, it is incompatible with our everyday knowledge ascriptions. For instance, as the Cartesian skeptic will point out, all of my perceptual experiences are compatible with a skeptical scenario in which I am completely deceived about the existence of the external world, in which case most (if not all) of my beliefs would be false. The typical conclusion to draw from this is that it is possible to doubt most (if not all) of my everyday beliefs, meaning that if I am indeed justified in holding those beliefs, that justification is not infallible. For the justification to be infallible, my reasons for holding my everyday beliefs would need to completely exclude the possibility that those beliefs were false. Consequently, if a belief must be infallibly justified in order to constitute knowledge, then it must be the case that we are mistaken in most (if not all) instances in which we claim to have knowledge in everyday situations.^[59] While it is indeed possible to bite the bullet and accept this conclusion, most philosophers find it implausible to suggest that we know nothing or almost nothing, and therefore reject the infallibilist response as collapsing into radical skepticism.

Tracking condition

Robert Nozick has offered a definition of knowledge according to which S knows that P if and only if:

• P is true;
• S believes that P;
• if P were false, S would not believe that P;
• if P were true, S would believe that P.

Nozick argues that the third of these conditions serves to address cases of the sort described by Gettier. Nozick further claims this condition addresses a case of the sort described by D.M. Armstrong: A father believes his daughter is innocent of committing a particular crime, both because of faith in his baby girl and (now) because he has seen presented in the courtroom a conclusive demonstration of his daughter's innocence. His belief via the method of the courtroom satisfies the four subjunctive conditions, but his faith-based belief does not. If his daughter were guilty, he would still believe her innocence, on the basis of faith in his daughter; this would violate the third condition.

The British philosopher Simon Blackburn has criticized this formulation by suggesting that we do not want to accept as knowledge beliefs which, while they "track the truth" (as Nozick's account requires), are not held for appropriate reasons. He says that "we do not want to award the title of knowing something to someone who is only meeting the conditions through a defect, flaw, or failure, compared with someone else who is not meeting the conditions." In addition to this, externalist accounts of knowledge, such as Nozick's, are often forced to reject closure in cases where it is intuitively valid.

An account similar to Nozick's has also been offered by Fred Dretske, although his view focuses more on relevant alternatives that might have obtained if things had turned out differently. Views of both the Nozick variety and the Dretske variety have faced serious problems suggested by Saul Kripke.

Knowledge-first response

Timothy Williamson has advanced a theory of knowledge according to which knowledge is not justified true belief plus some extra conditions, but primary. In his book Knowledge and its Limits, Williamson argues that the concept of knowledge cannot be broken down into a set of other concepts through analysis—instead, it is sui generis. Thus, according to Williamson, justification, truth, and belief are necessary but not sufficient for knowledge. Williamson is also known for being one of the only philosophers who take knowledge to be a mental state; most epistemologists assert that belief (as opposed to knowledge) is a mental state. As such, Williamson's claim has been seen to be highly counterintuitive.

Merely true belief

In his 1991 paper, "Knowledge is Merely True Belief", Crispin Sartwell argues that justification is an unnecessary criterion for knowledge. He argues that common counterexample cases of "lucky guesses" are not in fact beliefs at all, as "no belief stands in isolation... the claim that someone believes something entails that that person has some degree of serious commitment to the claim." He gives the example of a mathematician working on a problem who subconsciously, in a "flash of insight", sees the answer, but is unable to comprehensively justify his belief, and says that in such a case the mathematician still knows the answer, despite not being able to give a step-by-step explanation of how he got to it. He also argues that if beliefs require justification to constitute knowledge, then foundational beliefs can never be knowledge, and, as these are the beliefs upon which all our other beliefs depend for their justification, we can thus never have knowledge at all.

Nyaya philosophy

Nyaya is one of the six traditional schools of Indian philosophy with a particular interest in epistemology. The Indian philosopher B.K. Matilal drew on the Navya-Nyāya fallibilist tradition to respond to the Gettier problem. Nyaya theory distinguishes between know p and know that one knows p—these are different events, with different causal conditions. The second level is a sort of implicit inference that usually follows immediately the episode of knowing p (knowledge simpliciter). The Gettier case is examined by referring to a view of Gangesha Upadhyaya (late 12th century), who takes any true belief to be knowledge; thus a true belief acquired through a wrong route may just be regarded as knowledge simpliciter on this view. The question of justification arises only at the second level, when one considers the knowledge-hood of the acquired belief. Initially, there is lack of uncertainty, so it becomes a true belief. But at the very next moment, when the hearer is about to embark upon the venture of knowing whether he knows p, doubts may arise. "If, in some Gettier-like cases, I am wrong in my inference about the knowledge-hood of the given occurrent belief (for the evidence may be pseudo-evidence), then I am mistaken about the truth of my belief—and this is in accordance with Nyaya fallibilism: not all knowledge-claims can be sustained."

Other definitions

According to J. L. Austin, to know just means to be able to make correct assertions about the subject in question. On this pragmatic view, the internal mental states of the knower do not matter.

Philosopher Barry Allen also downplayed the role of mental states in knowledge and defined knowledge as "superlative artifactual performance", that is, exemplary performance with artifacts, including language but also technological objects like bridges, satellites, and diagrams. Allen criticized typical epistemology for its "propositional bias" (treating propositions as prototypical knowledge), its "analytic bias" (treating knowledge as prototypically mental or conceptual), and its "discursive bias" (treating knowledge as prototypically discursive). He considered knowledge to be too diverse to characterize in terms of necessary and sufficient conditions. He claimed not to be substituting knowledge-how for knowledge-that, but instead proposing a definition that is more general than both. For Allen, knowledge is "deeper than language, different from belief, more valuable than truth".

A different approach characterizes knowledge in relation to the role it plays, for example, regarding the reasons it provides or constitutes for doing or thinking something. In this sense, it can be understood as what entitles the agent to assert a fact, to use this fact as a premise when reasoning, or to act as a trustworthy informant concerning this fact. This definition has been adopted in some argumentation theory.

Paul Silva's "awareness first" epistemology posits that the common core of knowledge is awareness, providing a definition that accounts for both beliefless knowledge and knowledge grounded in belief.

Within anthropology, knowledge is often defined in a very broad sense as equivalent to understanding or culture. This includes the idea that knowledge consists in the affirmation of meaning contents and depends on a substrate, such as a brain. Knowledge characterizes social groups in the sense that different individuals belonging to the same social niche tend to be very similar concerning what they know and how they organize information. This topic is of specific interest to the subfield known as the anthropology of knowledge, which uses this and similar definitions to study how knowledge is reproduced and how it changes on the social level in different cultural contexts.

Non-propositional knowledge

Propositional knowledge, also termed factual knowledge or knowledge-that, is the most paradigmatic form of knowledge in analytic philosophy, and most definitions of knowledge in philosophy have this form in mind. It refers to the possession of certain information. The distinction to other types of knowledge is often drawn based on the differences between the linguistic formulations used to express them. It is termed knowledge-that since it can usually be expressed using a that-clause, as in "I know that Dave is at home". In everyday discourse, the term "knowledge" can also refer to various other phenomena as forms of non-propositional knowledge. Some theorists distinguish knowledge-wh from knowledge-that. Knowledge-wh is expressed using a wh-clause, such as knowing why smoke causes cancer or knowing who killed John F. Kennedy. However, the more common approach is to understand knowledge-wh as a type of knowledge-that since the corresponding expressions can usually be paraphrased using a that-clause.

A clearer contrast is between knowledge-that and knowledge-how (know-how). Know-how is also referred to as practical knowledge or ability knowledge. It is expressed in formulations like "I know how to ride a bike". All forms of practical knowledge involve some type of competence, i.e. having the ability to do something. So to know how to play the guitar means to have the competence to play it or to know the multiplication table is to be able to recite products of numbers. For this reason, know-how may be defined as having the corresponding competence, skills, or abilities. Some forms of know-how include knowledge-that as well and some theorists even argue that practical and propositional knowledge are of the same type. However, propositional knowledge is usually reserved only to humans while practical knowledge is more common in the animal kingdom. For example, an ant knows how to walk but it presumably does not know that it is currently walking in someone's kitchen. The more common view is, therefore, to see knowledge-how and knowledge-that as two distinct types of knowledge.

Another often-discussed alternative type of knowledge is knowledge by acquaintance. It is defined as a direct familiarity with an individual, often with a person, and only arises if one has met this individual personally. In this regard, it constitutes a relation not to a proposition but to an object. Acquaintance implies that one has had a direct perceptual experience with the object of knowledge and is therefore familiar with it. Bertrand Russell contrasts it with knowledge by description, which refers to knowledge of things that the subject has not immediately experienced, such as learning through a documentary about a country one has not yet visited. Knowledge by acquaintance can be expressed using a direct object, such as "I know Dave". It differs in this regard from knowledge-that since no that-clause is needed. One can know facts about an individual without direct acquaintance with that individual. For example, the reader may know that Napoleon was a French military leader without knowing Napoleon personally. There is controversy whether knowledge by acquaintance is a form of non-propositional knowledge. Some theorists deny this and contend that it is just a grammatically different way of expressing propositional knowledge.