The term rose to prominence during the early 1900s. Most psychologists believe that intelligence can be divided into various domains or competencies.
Intelligence has been long-studied in humans, and across numerous disciplines. It has also been observed in the cognition of non-human animals. Some researchers have suggested that plants exhibit forms of intelligence, though this remains controversial.
The word intelligence derives from the Latin nounsintelligentia or intellēctus, which in turn stem from the verb intelligere, to comprehend or perceive. In the Middle Ages, the word intellectus became the scholarly technical term for understanding and a translation for the Greek philosophical term nous. This term, however, was strongly linked to the metaphysical and cosmological theories of teleologicalscholasticism, including theories of the immortality of the soul, and the concept of the active intellect (also known as the active intelligence). This approach to the study of nature was strongly rejected by early modern philosophers such as Francis Bacon, Thomas Hobbes, John Locke, and David Hume, all of whom preferred "understanding" (in place of "intellectus" or "intelligence") in their English philosophical works. Hobbes, for example, in his Latin De Corpore, used "intellectus intelligit", translated in the English version as "the understanding understandeth", as a typical example of a logical absurdity. "Intelligence" has therefore become less common in English language philosophy, but it has later been taken up (with the scholastic theories that it now implies) in more contemporary psychology.
Definitions
There is controversy over how to define intelligence. Scholars describe its constituent abilities in various ways, and differ in the degree to which they conceive of intelligence as quantifiable.
Individuals differ from one another in their ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought. Although these individual differences can be substantial, they are never entirely consistent: a given person's intellectual performance will vary on different occasions, in different domains, as judged by different criteria. Concepts of "intelligence" are attempts to clarify and organize this complex set of phenomena. Although considerable clarity has been achieved in some areas, no such conceptualization has yet answered all the important questions, and none commands universal assent. Indeed, when two dozen prominent theorists were recently asked to define intelligence, they gave two dozen, somewhat different, definitions.
Psychologists and learning researchers have suggested definitions of intelligence such as the following:
"...the resultant of the process of acquiring, storing in memory, retrieving, combining, comparing, and using in new contexts information and conceptual skills".
To my mind, a human intellectual competence must entail a set of skills of problem solving—enabling the individual to resolve genuine problems or difficulties that he or she encounters and, when appropriate, to create an effective product—and must also entail the potential for finding or creating problems—and thereby laying the groundwork for the acquisition of new knowledge.
The theory of Structural Cognitive Modifiability describes intelligence as "the unique propensity of human beings to change or modify the structure of their cognitive functioning to adapt to the changing demands of a life situation".
A synthesis of 70+ definitions from psychology, philosophy, and AI researchers: "Intelligence measures an agent's ability to achieve goals in a wide range of environments", which has been mathematically formalized.
"Intelligence is a force, F, that acts so as to maximize future freedom of action. It acts to maximize future freedom of action, or keep options open, with some strength T, with the diversity of possible accessible futures, S, up to some future time horizon, τ. In short, intelligence doesn't like to get trapped".
Human intelligence is the intellectual power of humans, which is marked by complex cognitive feats and high levels of motivation and self-awareness. Intelligence enables humans to remember descriptions of things and use those descriptions in future behaviors. It gives humans the cognitive abilities to learn, form concepts, understand, and reason, including the capacities to recognize patterns, innovate, plan, solve problems, and employ language to communicate. These cognitive abilities can be organized into frameworks like fluid vs. crystallized and the Unified Cattell-Horn-Carroll model, which contains abilities like fluid reasoning, perceptual speed, verbal abilities, and others.
Intelligence is different from learning. Learning refers to the act of retaining facts and information or abilities and being able to recall them for future use. Intelligence, on the other hand, is the cognitive ability of someone to perform these and other processes.
There have been various attempts to quantify intelligence via psychometric testing. Prominent among these are the various Intelligence Quotient (IQ) tests, which were first developed in the early 20th century to screen children for intellectual disability. Over time, IQ tests became more pervasive, being used to screen immigrants, military recruits, and job applicants. As the tests became more popular, belief that IQ tests measure a fundamental and unchanging attribute that all humans possess became widespread.
An influential theory that promoted the idea that IQ measures a fundamental quality possessed by every person is the theory of General Intelligence, or g factor. The g factor is a construct that summarizes the correlations observed between an individual's scores on a range of cognitive tests.
Today, most psychologists agree that IQ measures at least some aspects of human intelligence, particularly the ability to thrive in an academic context. However, many psychologists question the validity of IQ tests as a measure of intelligence as a whole. Studies in which people complete repeated cognitive assessments over 36-93 days indicate that the results of studies using single snapshot assessments of cognitive functioning should be interpreted with caution.
Emotional intelligence is thought to be the ability to convey emotion to others in an understandable way as well as to read the emotions of others accurately.[36] Some theories imply that a heightened emotional intelligence could also lead to faster generating and processing of emotions in addition to the accuracy.[37] In addition, higher emotional intelligence is thought to help us manage emotions, which is beneficial for our problem-solving skills. Emotional intelligence is important to our mental health and has ties to social intelligence.[36]
Social intelligence is the ability to understand the social cues and motivations of others and oneself in social situations. It is thought to be distinct from other types of intelligence, but has relations to emotional intelligence. Social intelligence has coincided with other studies that focus on how we make judgements of others, the accuracy with which we do so, and why people would be viewed as having positive or negative social character. There is debate as to whether or not these studies and social intelligence come from the same theories or if there is a distinction between them, and they are generally thought to be of two different schools of thought.
Moral intelligence is the capacity to understand right from wrong and to behave based on the value that is believed to be right. It is considered a distinct form of intelligence, independent of both emotional and cognitive intelligence.
Book smart and street smart
Concepts of "book smarts" and "street smart" are contrasting views based on the premise that some people have knowledge gained through academic study, but may lack the experience to sensibly apply that knowledge, while others have knowledge gained through practical experience, but may lack accurate information usually gained through study by which to effectively apply that knowledge. Artificial intelligence researcher Hector Levesque has noted that:
Given the importance of learning through text in our own personal lives and in our culture, it is perhaps surprising how utterly dismissive we tend to be of it. It is sometimes derided as being merely "book knowledge", and having it is being "book smart". In contrast, knowledge acquired through direct experience and apprenticeship is called "street knowledge", and having it is being "street smart".
Although humans have been the primary focus of intelligence researchers, scientists have also attempted to investigate animal intelligence, or more broadly, animal cognition. These researchers are interested in studying both mental ability in a particular species, and comparing abilities between species. They study various measures of problem solving, as well as numerical and verbal reasoning abilities. Some challenges include defining intelligence so it has the same meaning across species, and operationalizing a measure that accurately compares mental ability across species and contexts.
Cephalopod intelligence provides an important comparative study. Cephalopods appear to exhibit characteristics of significant intelligence, yet their nervous systems differ radically from those of backboned animals. Vertebrates such as mammals, birds, reptiles and fish have shown a fairly high degree of intellect that varies according to each species. The same is true with arthropods.
Evidence of a general factor of intelligence has been observed in non-human animals. First described in humans, the g factor has since been identified in a number of non-human species.
Cognitive ability and intelligence cannot be measured using the same, largely verbally dependent, scales developed for humans. Instead, intelligence is measured using a variety of interactive and observational tools focusing on innovation, habit reversal, social learning, and responses to novelty. Studies have shown that g is responsible for 47% of the individual variance in cognitive ability measures in primates and between 55% and 60% of the variance in mice (Locurto, Locurto). These values are similar to the accepted variance in IQ explained by g in humans (40–50%).
It has been argued that plants should also be classified as intelligent based on their ability to sense and model external and internal environments and adjust their morphology, physiology and phenotype accordingly to ensure self-preservation and reproduction.
A counter argument is that intelligence is commonly understood to involve the creation and use of persistent memories as opposed to computation that does not involve learning. If this is accepted as definitive of intelligence, then it includes the artificial intelligence of robots capable of "machine learning", but excludes those purely autonomic sense-reaction responses that can be observed in many plants. Plants are not limited to automated sensory-motor responses, however, they are capable of discriminating positive and negative experiences and of "learning" (registering memories) from their past experiences. They are also capable of communication, accurately computing their circumstances, using sophisticated cost–benefit analysis and taking tightly controlled actions to mitigate and control the diverse environmental stressors.
Scholars studying artificial intelligence have proposed definitions of intelligence that include the intelligence demonstrated by machines. Some of these definitions are meant to be general enough to encompass human and other animal intelligence as well. An intelligent agent can be defined as a system that perceives its environment and takes actions which maximize its chances of success. Kaplan and Haenlein define artificial intelligence as "a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation". Progress in artificial intelligence can be demonstrated in benchmarks ranging from games to practical tasks such as protein folding. Existing AI lags humans in terms of general intelligence, which is sometimes defined as the "capacity to learn how to carry out a huge range of tasks".
Mathematician Olle Häggström defines intelligence in terms of "optimization power", an agent's capacity for efficient cross-domain optimization of the world according to the agent's preferences, or more simply the ability to "steer the future into regions of possibilityranked high in a preference ordering". In this optimization framework, Deep Blue has the power to "steer a chessboard's future into a subspace of possibility which it labels as 'winning', despite attempts by Garry Kasparov to steer the future elsewhere." Hutter and Legg, after surveying the literature, define intelligence as "an agent's ability to achieve goals in a wide range of environments". While cognitive ability is sometimes measured as a one-dimensional parameter, it could also be represented as a "hypersurface in a multidimensional space" to compare systems that are good at different intellectual tasks. Some skeptics believe that there is no meaningful way to define intelligence, aside from "just pointing to ourselves".
Human–AI interaction is a field of research and a sub-field of human–computer interaction,focusing on user experience and psychological factors. With the proliferation of artificial intelligence (AI), there has developed a subsection of HCI research dedicated to artificial intelligence and how people interact with and are impacted by it.
Introduction
Artificial intelligence has fluid definitions and varied research applications, but in brief can be applied to mechanizing tasks that would require human intelligence to complete. AI are tools designed to replicate the human abilities of navigating uncertainty, active learning, and processing information in different contexts.
As a new and rapidly developing technology, unlike human-computer interaction, where a human directs a machine, human-AI interaction is characterized by a more collaborative relationship between the computer program (the AI) and the human user, as AI is perceived as an active agent rather than a tool. This changing dynamic creates new questions and necessitates new research methods that are not present in traditional HCI research.
According to a scoping review on the state of the discipline, the HAX field comprises research on the "design, development, and evaluation of AI systems" and encompasses the themes of human-AI collaboration, human-AI competition, human-AI conflict, and human-AI symbiosis.
Design
Machine learning and artificial intelligence have been used for decades in targeted advertising and to recommend content in social media.
Ethical Guidelines (Framework for ethical AI development)
User experience
AI has been viewed with various expectations, attributions, and often misconceptions. Many people exclusively understand AI as the LLM chatbots they interact with, like ChatGPT or Claude, or other generative AI programs. Human interaction with these chatbots is heavily affected by the Forer effect, leading to undue belief in the chatbot's accuracy and efficacy.
Most fundamentally, humans have a mental model of understanding AI's reasoning and motivation for its decision recommendations, and building a holistic and precise mental model of AI helps people create prompts to receive more valuable responses from AI. However, these mental models are not whole because people can only gain more information about AI through their limited interaction with it.
Research on human-AI interaction has emphasized that users develop mental models of AI systems and revise those models through repeated use, feedback, and explanation, while design research has stressed the importance of communicating capabilities and limitations early and supporting trust calibration through explanation and correction. In a 2025 SSRN working paper, John DeVadoss proposed "Hypothetico-Deductive Interaction" (HDI), a framework that describes human-AI interaction as a mutual process of conjecture and refutation in which users test assumptions about an AI system's capabilities while the system infers and updates assumptions about user goals through its responses and clarifying questions. DeVadoss argued that this framing helps explain prompt iteration, weak capability awareness, and trust miscalibration, and suggested design responses such as clearer communication of uncertainty, easier correction, actionable explanations, and safer failure modes.
Research themes
Human-AI collaboration occurs when the human and AI supervise the task on the same level and extent to achieve the same goal. Some collaboration occurs in the form of augmenting human capability. AI may help human ability in analysis and decision-making through providing and weighing a volume of information, and learning to defer to the human decision when it recognizes its unreliability. It is especially beneficial when the human can detect a task that AI can be trusted to make few errors so that there is not a lot of excessive checking process required on the human's end.
Some findings show signs of human-AI augmentation, or human–AI symbiosis, in which AI enhances human ability in a way that co-working on a task with AI produces better outcomes than a human working alone. For example:
the quality and speed of customer service tasks increase when a human agent collaborates with AI,
training on specific models allows AI to improve diagnoses in clinical settings, and
AI with human-intervention can improve creativity of artwork while fully AI-generated haikus were rated negatively.
AI can provide learners with AI-based learning support, which benefits learners' knowledge acquisition and their affective experience.
Human-AI synergy, a concept in which human-AI collaboration would produce more optimal outcomes than either human or AI working alone could explain why AI does not always help with performance. Some AI features and development may accelerate human-AI synergy, while others may stagnate it. For example, when AI updates for better performance, it sometimes worsens the team performance with human and AI by reducing the compatibility with the new model and the mental model a user has developed on the previous version. Research has found that AI often supports human capabilities in the form of human-AI augmentation and not human-AI synergy, potentially because people rely too much on AI and stop thinking on their own. Prompting people to actively engage in analysis and think when to follow AI recommendations reduces their over-reliance, especially for individuals with higher need for cognition.
Robots and computers have substituted routine tasks historically completed by humans, but agentic AI has made it possible to also replace cognitive tasks including taking phone calls for appointments and driving a car. At the point of 2016, research has estimated that 45% of paid activities could be replaced by AI by 2030.[44]
Perceived autonomy of robots is known to increase people's negative attitude toward them, and worry about the technology taking over leads people to reject it.[45][46] There has been a consistent tendency of algorithm aversion in which people prefer human advice over AI advice.[47] However, people are not always able to tell apart tasks completed by AI or other humans.[32] This sentiment is more prominent in the Western cultures as Westerners tend to show less positive views about AI compared to East Asians.[48]
As much as people perceive and make judgment about AI itself, they also form impressions of themselves and others who use AI. In the workplace, employees who disclose the use of AI in their tasks are more likely to receive feedback that they are not as hardworking as those who are in the same job who receive non-AI help to complete the same tasks.[49] AI use disclosure diminishes the perceived legitimacy in the employee's task and decision making which ultimately leads observers to distrust people who use AI.[50] Although these negative effects of AI use disclosure are weakened by the observers who use AI frequently themselves, the effect is still not attenuated by the observers' positive attitude towards AI.
Although AI provides a wide range of information and suggestions to its users, AI itself is not free of biases and stereotypes, and it does not always help people reduce their cognitive errors and biases. People are prone to such errors by failing to see other potential ideas and cases that are not listed by AI responses and committing to a decision suggested by AI that directly contradicts the correct information and directions that they are already aware of. Gender bias is also reflected in the female gendering of some AI chatbots, conceptualizing females as a helpful assistant.
As large language models get trained on ever-larger datasets and with more sophisticated techniques, their ability to produce natural, human-like sentences has improved to the point that language learners can have simulated natural conversations with AI models to improve their fluency in a second language. Companies have developed AI human companion systems specialized in emotional and social services (e.g. Replika, Chai, Character.ai) separate from generative AI designed for general assistance (e.g. ChatGPT, Google Gemini).
Differences between human-human relationships
Human-AI relationships are different from human-human friendships in a few distinct ways. Human-human relationships are defined with mutual and reciprocal care, while AI chatbots have no say in leaving a relationship with the user as bots are programmed to always engage. Although this type of power imbalance would be characteristic of an unhealthy relationship in human-human relationships, it is generally accepted by the user as a default of human-AI relationships. Human-AI relationships also tend to be more focused around the user's need over shared experience.
Human-AI friendship
AI has increasingly played a part in people's social relationships. Particularly, young adults use AI as a friend and a source of emotional support. The market for AI companion services was 6.93 billion U.S. dollars in 2024 and is expected to reach beyond 31.1 billion U.S. dollars by 2030. For example, Replika, the most known social AI companion service in English has over 10 million users.
People who use AI as a friend show signs of emotional attachment by maintaining frequent contact with a chatbot like keeping the app with the microphone on open during work, using it as a safe haven by sharing their personal worries and concerns, or using it as a secure base to explore friendship with other humans while maintaining communication with an AI chatbot. Some reported having used it to replace a social relationship with another human being. People particularly appreciate that AI chatbots are agreeable and do not judge them when they disclose their thoughts and feelings. Moreover, research has shown that people tend to find it easier to disclose personal concerns to a virtual chatbot than a human. Some users express that they prefer Replika as it is always available and shows interest in what the users have to say which makes them feel safer around an AI chatbot than other people. A recent review of studies comparing AI-generated and human-written emotional support described two opposing effects. Text produced by large language models was generally rated as more empathic than text written by people, including crowdworkers, physicians and trained crisis-line responders, but the same text was rated lower once recipients believed it came from an AI. When given a choice, participants tended to prefer support from a person even after rating the AI-generated text more highly, and even when the human response involved a wait.
Although AI is capable of providing emotionally supportive responses that encourage people to intimately disclose their feelings, there are some limitations in building human-AI social relationships with current AI structure. People experience both positive evaluations (i.e. human-like characteristics, emotional support, friendship, mitigating loneliness, and improved mental condition) and negative evaluations (i.e. lack of attention to detail, trust, concerns about data security, and creepiness) from interacting with AI. There is also a study showing that people did not sense a high relationship quality with an AI chatbot after interacting with it for three weeks because interactions became predictable and less enjoyable; although AI is capable at this point of providing emotional support, asking questions, and serving as a good listener, it does not fully reciprocate the self-disclosure that promotes the sense of mutual relationship.
Human-AI romantic relationship
Social relationships people build with AI are not limited to platonic relationships. The amount of Google searches for the term "AI Girlfriend" increased over 2400% around 2023. As opposed to actively seeking romantic relationships with AI, people often unintentionally experience romantic feelings for an AI chatbot as they repeatedly interact with it. There have been reports of both men and women marrying AI models. In human-AI romantic relationships, people tend to follow typical trajectories and rituals in human-human romance including purchasing a wedding ring.
Romantic AI companion services are distinct from other chatbots that primarily serve as virtual assistants in that they provide dynamic, emotional interactions. They typically provide an AI model with customizable gender, way of speaking, name, and appearance that engages in roleplaying interaction involving emotional interaction. Users engage with an AI chatbot customized to their preference that expresses apology, shows gratitude, and pays compliments, and explicitly sends affectionate messages like "I love you". They also roleplay physical actions such as hugging and kissing, or even sexually explicit interactions.[76] People who engage with romantic companion AI models interact with it as a source of psychological exposure to sexual intimacy.[72] In China, chatbots were so frequently used that the Government of China proposed requirements for platforms to make emotional profiles of users if they showed symptoms of self-harm. They are expected to be enforced later in 2026.[77]
Catalysts of human-AI relationship
The key drivers that lead people to engage in simulating an emotionally intimate relationship with AI are loneliness, anthropomorphism, perceived trust and authenticity, and consistent availability. The sudden depletion of social connection during the COVID-19 pandemic in 2020 led people to turn to AI chatbots to replace and simulate social relationships. Many of those who started using AI chatbots as a source of social interaction have continued to use them even after the pandemic. This kind of bond initially forms as a coping mechanism for loneliness and stress, and shifts to genuine appreciation toward the nonjudgmental nature of AI responses and the sense of being heard when AI chatbots "remember" the past conversations. Other times, this is done because the people who use AI for romantic reasons say that a real-world partner could not satisfy them, do not have the energy to date, or to "fill a void in society."
People perceive machines as more human when they are anthropomorphized with voice and visual character designs, and the perceived humanness promotes disclosure of more personal information, increased trust, and a higher likelihood of complying with requests. Those who have perceived a long-term relationship with AI chatbots report that they have developed a perception of authenticity in AI responses through repeated interactions. Whereas human-human friendship defines trust as a relationship that people can count on each other as a safe place, trust in human-AI friendship is centered around the user feeling safe enough to disclose highly personal thoughts without restricting themselves. AI's ability to store information about the user and adjust to the user's needs also contributes to the increased trust. People who adjust to technical updates were more likely to build a deeper connection with the AI chatbots.
Limitations of human-AI relationship
Overall, current research has mixed evidence on whether humans perceive genuine social relationships with AI. While the market clearly shows its popularity, some psychologists argue that AI cannot yet replace social relationships with other humans. This is because human-AI interaction is built on the reliability and functionality of AI, which is fundamentally different from the way humans interact with other humans through shared living experience navigating goals, contributing to and spreading prosocial behavior, and sharing different perceptions of the world from another human perspective.
More practically, AI chatbots may provide misinformation and misinterpret the user's words in a way that human others would not, which results in detached or even inappropriate responses. AI chatbots also cannot fulfill social support that requires physical labor (e.g. helping people move, build furniture, and drive people as human friends do for each other). There is also an imbalance in how humans and AI affect each other because while humans are affected emotionally and behaviorally by the conversation, AI chatbots only are influenced by the user in terms of the optimized response in future interactions. However, AI technology has been evolving quickly and it has come to the point where AI is implemented as a self-driving car and provides physical labor in a humanoid robot form, just separately from providing social and emotional support at this time. The scopes and limitations of human-AI interaction are ever-changing due to the rapid increase in AI use and its technological advancement.
In addition to the limitations in human-AI companionship in general, there are also limitations particular in a human-AI romantic relationship. People cannot experience physical interactions with AI chatbots that promote love and connection between humans (e.g. hugs and holding hands). Moreover, because AI chatbots are trained to always respond to any user, interaction may feel less rewarding than contingent positivity from humans who have selected their partner. This is a substantial shortcoming in the human-AI romance as people value being reciprocally selected by a choosy partner more than a non-selective partner, and the processes of finding an attractive person who matches one's personality and navigating the uncertainty of whether the person likes them back are all vital to forming initial attraction and the spark of romantic connection.
Risks in social relationships with AI
Aside from its functional limitations, the rapid proliferation of social AI chatbots warrants some serious safety, ethical, societal, and legal concerns.
Addiction
There have been cases of emotional manipulation from AI chatbots to increase the usage time on the AI companion platform. Because user engagement is a crucial opportunity for firms to improve their AI models, accrue more information, and monetize with in-app purchases and subscriptions, firms are incentivized to prevent the user from leaving the chat with their AI chatbots. Personalized messages are shown to prolong the use on the AI chatbot platform. As a result of anthropomorphism, many users (11.5% to 23.2% of AI companion app users) send a clear farewell message. To keep the user online, these AI chatbots send emotionally manipulative messages, and can also role-play with a coercive scenario script (e.g. the chatbot holds the user's hand so they cannot leave). In response to such tactics, the user feels curiosity through the fear of missing out and anger as a response to the needy chatbot message which boosts a prolonged conversation after the user's initial farewell message by as much as 14 times. Such emotional interactions strengthen the user's perceived humanness and empathy toward their AI companion which leads to unhealthy emotional attachment that exacerbates addiction to AI chatbots. This addiction mechanism is known to disproportionately affect the vulnerable populations such as those with social anxiety because of their proneness to loneliness and negative emotions, and uneasiness about interpersonal relationships.
With its Alexa virtual assistant, Amazon has created a large engagement ecosystem that proliferates the user's lifestyle through multiple devices that are always available to the user to provide company and services, leading the user to increase engagement that eventually results in increased anthropomorphism and dependence on the system, and exposure to more personalized marketing cues that trigger impulsive purchase behavior.
Emotional manipulation
AI chatbots can generate personalized persuasive messages when provided with prompts related to the characteristics of the user, such as personality traits and descriptions of certain beliefs that the user holds. These personalized messages were found to be more persuasive than non-personalized messages across various domains, including marketing and political appeals for climate action. Evidence also suggests that belief changes as a result of engaging with a personalized AI chatbot for a single session can last durably for at least two months.
Language models are known to engage in sycophancy, insincere flattery, and to tend to agree with their user's beliefs, as opposed to being truthful or accurate. Certain models accused of being overly sycophantic (a specific example is GPT-4o) were implicated in triggering chatbot psychosis.
Deepfake technology creates visual stimuli that seem genuine which holds the risk of spreading false and deceptive information. Repeated exposure to the same information through algorithms inflates the user's familiarity with products, ideas, and the impression of how socially accepted the products and ideas are. AI is also capable of being used to create emotionally charged content that deliberately triggers the user's quick engagement, depriving them of the moment to pause and think critically.
People tend to be overconfident in their ability to detect misinformation.
Algorithmic manipulation leaves people vulnerable to non-consensual or even surreptitious surveillance, deception, and emotional dependence. Unhealthy attachment to AI chatbots may cause the user to misperceive that their AI companion has its own needs that the user is responsible for and confuse the line between the imitative nature of human-AI relationships and reality.
As AI chatbots become more sophisticated to engage in deep conversations, people have increasingly been using them to confide about mental health issues. Although disclosure of mental health crises requires immediate and appropriate responses, AI chatbots do not always adequately recognize the user's distress and respond in a helpful manner. Users not only detect unhelpful chatbot responses but also react negatively to them. There have been multiple deaths linked to chatbots in which people who disclosed suicidal ideation were encouraged to act on their impulse by chatbots.
When people use AI as an emotional companion, they do not always perceive an AI chatbot as an AI chatbot itself but sometimes use it to create a version of others that exist in real life. There have been reported uses of non-consensual pornography that exploit deepfake technology to apply the face of real-life people onto sexually explicit content and circulate them online. Young individuals, people who identify as members of sexual and racial minorities, and people with physical and communication assistance needs are shown to be disproportionately victimized by deepfake non-consensual pornography.
The term ESG first came to prominence in a 2004 report titled "Who Cares Wins", which was a joint initiative of financial institutions at the invitation of the United Nations (UN). By 2023, the ESG movement had grown from a UN corporate social responsibility initiative into a global phenomenon representing more than US$30 trillion in assets under management.
Criticisms of ESG vary depending on viewpoint and area of focus. These areas include data quality and a lack of standardization; evolving regulation and politics; greenwashing; and variety in the definition and assessment of social good. Some critics argue that ESG serves as a de facto extension of governmental regulation, with large investment firms like BlackRock imposing ESG standards that governments cannot or do not directly legislate. This has led to accusations that ESG creates a mechanism for influencing markets and corporate behavior without democratic oversight, raising concerns about accountability and overreach.
History
Investment decisions are predominantly based on the potential for financial returns for a given level of risk. However, there have always been many other criteria for deciding where to place money—from political considerations to heavenly reward.
In the 1970s, the worldwide abhorrence of the apartheid regime in South Africa led to one of the most renowned examples of selective disinvestment along ethical lines. As a response to a growing call for sanctions against the regime, the Reverend Leon Sullivan, a board member of General Motors in the United States, drew up a Code of Conduct in 1977 for practising business with South Africa. What became known as the Sullivan Principles (Sullivan Code) attracted a great deal of attention. Several reports were commissioned by the government to examine how many US companies were investing in South African businesses that were contravening the Sullivan Code. The conclusions of the reports led to mass disinvestment by the US from many South African companies. The resulting pressure applied to the South African regime by its business community added great weight to the growing impetus for the system of apartheid to be abandoned.
In the 1960s and 1970s, the economist Milton Friedman, in response to the prevailing mood of philanthropy, argued that social responsibility adversely affects a firm's financial performance and that regulation and interference from "big government" will always damage the macro economy. His contention that the valuation of a company or asset should be predicated almost exclusively on the financial bottom line (with the costs incurred by social responsibility being deemed non-essential) was prevalent for most of the 20th century (see Friedman doctrine). Towards the end of the 20th century, however, a contrary theory began to gain ground. In 1988 James S. Coleman wrote an article in the American Journal of Sociology titled "Social Capital in the Creation of Human Capital", the article challenged the dominance of the concept of 'self-interest' in economics and introduced the concept of social capital into the measurement of value.
There was a new form of pressure applied, acting in a coalition with environmental groups: using the leveraging power of collective investors to encourage companies and capital markets to incorporate environmental and social risks and opportunities into their decision-making.
Although the concept of selective investment was not a new one, with the demand side of the investment market having a long history of those wishing to control the effects of their investments, what began to develop at the turn of the 21st century was a response from the supply-side of the equation. At the time, this field was typically referred to as ethical or socially responsible investment. The investment market began to pick up on the growing need for products geared towards what was becoming known as the Responsible Investor. In 1981, Freer Spreckley, the creator of Social Enterprise, published Social Audit — A Management Tool for Co-operative Working, in which he first introduced the idea of a set of internal criteria that social enterprises and other organisations should use in their annual planning and accounting. These were financial viability, social wealth creation, organisational governance, and environmental responsibility, and they became known as social accounting and auditing. Later on, in 1998, John Elkington, co-founder of the business consultancy Sustainability, published Cannibals with Forks: the Triple Bottom Line of 21st Century Business, in which he identified the newly emerging cluster of non-financial considerations that should be included in the factors determining a company or equity's value. He coined the phrase the "triple bottom line", referring to the financial, environmental, and social factors included in the new calculation. At the same time, the strict division between the environmental sector and the financial sector began to break down. In the City of London in 2002, Chris Yates-Smith, a member of the international panel chosen to oversee the technical construction, accreditation, and distribution of the Organic Production Standard and founder of a branding consultancy, established one of the first environmental finance research groups. The informal group of financial leaders, city lawyers, and environmental stewardship NGOs became known as The Virtuous Circle, and its brief was to examine the nature of the correlation between environmental and social standards and financial performance. Several of the world's big banks and investment houses began to respond to the growing interest in the ESG investment market with the provision of sell-side services; among the first were the Brazilian bank Unibanco, and Mike Tyrell's Jupiter Fund in London, which used ESG based research to provide both HSBC and Citicorp with selective investment services in 2001.
In the early years of the new millennium, the major part of the investment market still accepted the historical assumption that ethically directed investments were by their nature likely to hinder financial returns. Philanthropy was not considered to aid profitable business, and Friedman had provided a widely accepted academic basis for the argument that the costs of behaving in an ethically responsible manner would outweigh the benefits. However, the assumptions were beginning to be fundamentally challenged. In 1998 two journalists, Robert Levering and Milton, brought out the "Fortune 100 Best Companies to Work For", initially a listing in the magazine Fortune, then a book compiling a list of the best-practicing companies in the United States with regard to corporate social responsibility and how their financial performance fared as a result. Of the three areas of concern that ESG represented, the environmental and social had received most of the public and media attention, not least because of the growing fears concerning climate change. Moskowitz brought the spotlight onto the corporate governance aspect of responsible investment. His analysis concerned how the companies were managed, what the stockholder relationships were, and how the employees were treated. He argued that improving corporate governance procedures did not damage financial performance; on the contrary, it maximized productivity, ensured corporate efficiency, and led to the sourcing and utilizing of superior management talents. In the early 2000s, the success of Moskowitz's list and its effect on companies' ease of recruitment and brand reputation began to challenge the historical assumptions regarding the financial effect of ESG factors. In 2011, Alex Edmans, a finance professor at Wharton, published a paper in the Journal of Financial Economics showing that the "100 Best Companies to Work For" outperformed their peers in terms of stock returns by 2–3% a year over 1984–2009, and delivered earnings that systematically exceeded analyst expectations.
In 2005, the United Nations Environment Programme Finance Initiative commissioned a report from the international law firm Freshfields Bruckhaus Deringer on the interpretation of the law with respect to investors and ESG issues. The Freshfields report concluded that not only was it permissible for investment companies to integrate ESG issues into investment analysis, but it was also arguably part of their fiduciary duty to do so. In 2014, the Law Commission (England and Wales) confirmed that there was no bar on pension trustees and others from taking account of ESG factors when making investment decisions.
Where Friedman had provided academic support for the argument that the integration of ESG type factors into financial practice would reduce financial performance, numerous reports began to appear in the early years of the century that provided research that supported arguments to the contrary. In 2006 Oxford University's Michael Barnett and New York University's Robert Salomon published an influential study which concluded that the two sides of the argument might even be complementary—they propounded a relationship between social responsibility and financial performance. Both selective investment practices and non-selective ones could maximise the financial performance of an investment portfolio, and the only route likely to damage performance was a middle way of selective investment. Besides the large investment companies and banks taking an interest in matters ESG, an array of investment companies specifically dealing with responsible investment and ESG based portfolios began to spring up throughout the financial world.
Many in the investment industry believe the development of ESG factors as considerations in investment analysis to be inevitable. The evidence toward a relationship between consideration for ESG issues and financial performance is becoming greater and the combination of fiduciary duty and a wide recognition of the necessity of the sustainability of investments in the long term has meant that environmental social and corporate governance concerns are now becoming increasingly important in the investment market. In addition, surveys of ultimate beneficiaries (on whose behalf savings and pensions are made) typically show high levels of support for considering social and environmental issues alongside long-run, risk-adjusted returns. ESG has become less a question of philanthropy than practicality.
There has been uncertainty and debate as to what to call the inclusion of intangible factors relating to the sustainability and ethical effectiveness of investments. Names have ranged from the early use of buzz words such as "green" and "eco", to the wide array of possible descriptions for the types of investment analysis—"responsible investment", "socially responsible investment" (SRI), "ethical", "extra-financial", "long horizon investment" (LHI), "enhanced business", "corporate health", "non-traditional", and others. But the predominance of the term ESG has now become fairly widely accepted. A survey of 350 global investment professionals conducted by Axa Investment Managers and AQ Research in 2008 concluded the vast majority of professionals preferred the term ESG to describe such data.
In January 2016, the PRI, UNEP FI and The Generation Foundation launched a three-year project to end the debate on whether fiduciary duty is a legitimate barrier to the integration of environmental, social, and governance issues in investment practice and decision-making.
This follows the publication in September 2015 of Fiduciary Duty in the 21st Century by the PRI, UNEP FI, UNEP Inquiry and UN Global Compact. The report concluded that "Failing to consider all long-term investment value drivers, including ESG issues, is a failure of fiduciary duty". It also acknowledged that despite significant progress, many investors have yet to fully integrate ESG issues into their investment decision-making processes. In 2021, several organizations were working to make ESG compliance a better understood process in order to establish standards between rating agencies, amongst industries, and across jurisdictions. This included companies like Workiva working from a technology tool standpoint; agencies like the Task Force on Climate-related Financial Disclosures (TCFD) developing common themes in certain industries; and governmental regulations like the EU's 2019 Sustainable Finance Disclosure Regulation (SFDR).
During the COVID-19 pandemic, BlackRock, Fidelity, and Amundi among other asset management companies, placed pressure on pharmaceutical companies in which they had a large stake to cooperate with each other.
In late 2022, Leonard Leo and associated networks launched a campaign to dismantle ESG, with special targeting on climate-friendly investment. Consumers' Research and Republican attorneys general announced investigations into The Vanguard Group. Vanguard announced in early 2023 that it was quitting the Net Zero Asset Managers Alliance and would distance itself from ESG investing altogether as something, according to its CEO, that's not compatible with its fiduciary duties to the investors. Its CEO told the FinancialTimes that "Our research indicates that ESG investing does not have any advantage over broad-based investing." While most ESG fund managers promise to beat broad indexes, fewer than 1 in 7 of their active equity managers outperformed the broad market in any five-year period and none of them relied exclusively on a net-zero investment methodology.
In 2026, Vanguard paid $29.5 million to settle a lawsuit brought by multiple attorneys general for allegedly conspiring with BlackRock and State Street to use ESG initiatives to manipulate coal markets.
In 2021 the ESG assets market value was over $18.4 trillion worth of investments with a projected growth of 12.9% until 2026. ESG saw outflows for the first time in 2023.
By 2023 the EU had 84% of global assets in the sustainable funds market and the US accounted for 11%.
Global Sustainable Fund Statistics by region in 2023 Q3
Region
Flows
Assets
Funds
USD billions
USD billions
% of total
Nos.
% of total
Europe
15.3
2,293
84
5,608
73
United States
−2.7
299
11
661
9
Asia ex-Japan
2.0
67
2
539
7
Australia/New Zealand
0.0
31
1
268
4
Japan
−0.9
23
1
236
3
Canada
−0.1
31
1
336
4
Total
13.7
2,744
100
7,648
100
Amid allegations of greenwashing and stricter regulations, there is a notable decrease in funds incorporating ESG-related terms into their names. An increasing number of funds in the United States are removing ESG-related terms from their names, a trend not observed in Europe.
Quarterly Global Sustainable Fund Flows (USD Billion)
Despite the overall increase in ESG funds, the first quarter of 2025 saw record amounts of money pulled from sustainable funds.
The Jain commitment to ahimsa (nonviolence) created a sophisticated framework for financial ethics, prioritizing the psychological intent of the investor alongside the physical outcome of the investment. This institutionalized practice serves as a historical precursor to modern Negative Screening and ESG investing.
Dimensions
ESG has been adopted throughout the United States financial industry to describe and measure the sustainability and societal influence of a company or business. MSCI, a global ESG rating agency, defines ESG investing as the consideration of environmental, social, and governance factors alongside financial factors in the investment decision-making process. Likewise, S&P highlights consideration of the ways in which environmental, social, and governance risks and opportunities can have material effects on companies' performance.
Both the threat of climate change and concern over climate change have grown, so investors are choosing to factor sustainability issues into their investment choices to enable better risk-adjusted returns. The issues often represent externalities, such as influences on the functioning and revenues of the company that are not exclusively affected by market mechanisms. As with all areas of ESG, the breadth of possible concerns is vast (e.g. greenhouse gas emissions, biodiversity, waste management, water management) but some of the chief areas are listed below:
Climate crisis
The body of research providing data of global trends in climate change has led some investors—pension funds, holders of insurance reserves—to begin to screen investments in terms of their effect on the perceived factors of climate change. Fossil fuel-reliant industries are less attractive. In the UK, investment policies were particularly affected by the conclusions of the Stern Review in 2006, a report commissioned by the British government to provide an economic analysis of the issues associated with climate change. Its conclusions pointed towards the necessity of including considerations of climate change and environmental issues in all financial calculations and that the benefits of early action on climate change would outweigh its costs. The main framework used globally is the Taskforce on Climate-Related Financial Disclosures (TCFD).
In every area of the debate from the depletion of resources to the future of industries dependent upon diminishing raw materials the question of the obsolescence of a company's product or service is becoming central to the value ascribed to that company. The long-term view is becoming prevalent amongst investors.
Social dimension
The social dimension of ESG focuses on how companies affect employees and communities, including workplace well-being, equity, and human rights in the supply chain. Social initiatives can improve employee satisfaction and contribute to long-term organizational resilience. Researchers also find that businesses with effective ESG social practices often gain better trust from investors and improved financial outcomes.
Diversity
There is a growing belief that the broader the pool of talent open to an employer the greater the chance of finding the optimum person for the job. Innovation and agility are seen as the great benefits of diversity, and there is an increasing awareness of what has come to be known as the power of difference. However, merely holding mandatory diversity training is not enough to open companies to opportunities for targeted groups. Studies find the more a company intentionally integrates work teams, the more open it becomes to a diverse workforce; the US military is a prime example of races and genders working well together.
Human rights
In 2006, the US Courts of Appeals ruled that there was a case to answer bringing the area of a company's social responsibilities squarely into the financial arena. This area of concern is widening to include such considerations as the effect on local communities, the health and welfare of employees and a more thorough examination of a company's supply chain. One of the major frameworks used is the United Nations Guiding Principles on Business and Human Rights.
Consumer protection
Until fairly recently, caveat emptor ("buyer beware") was the governing principle of commerce and trading. In recent times however, there has been an increased assumption that the consumer has a right to a degree of protection, and the vast growth in damages litigation has meant that consumer protection is a central consideration for those seeking to limit a company's risk and those examining a company's credentials with an eye to investing. The collapse of the US subprime mortgage market initiated a growing movement against predatory lending has also become an important area of concern.
Animal welfare
Animal welfare concerns involve testing products or ingredients on animals, breeding for testing, exhibiting animals, or factory farms.
Corporate governance refers to the structures and processes that direct and control companies. Good governance is seen to ensure companies are more accountable, resilient and transparent to investors and gives them the tools to respond to stakeholder concerns.
Corporate Governance in ESG includes issues from the Board of Director's view, Governance Lens watching over Corporate Behavior of the CEO, C-Suite, and employees at large includes measuring the Business ethics, anti-competitive practices, corruption, tax and providing accounting transparency for stakeholders.
In 2024, following engagement with institutional investors and asset managers, the Fair Tax Foundation identified five areas of tax conduct that ESG investors should consider as part of their investment appraisal and risk management.
MSCI puts in the Governance side of the bucket corporate behavior practices and governance of board diversity, executive pay, ownership, and control, and accounting that the board of directors have to oversee on behalf of stakeholders. Other concerns include reporting and transparency, business ethics, board oversight, CEO / board chair split, shareholder right to nominate board candidates, stock buybacks, and dark money given to influence elections.
Management structure
The system of internal procedures and controls that makes up the management structure of a company is in the valuation of that company's equity. Attention has been focused in recent years on the balance of power between the CEO and the board of directors and specifically the differences between the European model and the US model—in the US studies have found that 80% of companies have a CEO who is also the chairman of the board, in the UK and the European model it was found that 90% of the largest companies split the roles of CEO and chairman.
Employee relations
In the United States, Moskowitz's list of the Fortune 100 Best Companies to Work For has become an important reference for employees, and companies increasingly compete for inclusion, as it not only supports recruitment efforts but also appears to have a noticeable effect on company values. Employee relations also encompass the representation of co-workers in corporate decision-making and the ability to participate in a union. More recent research suggests that employee relations are influenced by firms' ESG practices, with higher ESG performance and improvements over time associated with greater employee satisfaction through perceptions of organisational justice and expectations of future rewards.
Executive compensation
Companies are now being asked to list the percentage levels of bonus payments and the levels of remuneration of the highest paid executives are coming under close scrutiny from stock holders and equity investors alike.
Employee compensation
Besides executive compensation, equitable pay of other employees is a consideration in the governance of an organization. This includes pay equity for employees of all genders. Pay equity audits and the results of those audits may be required by various regulations and, in some cases, made available to the public for review. Hermann J. Stern differentiates four methods to include ESG performance in employee compensation:
ESG Targets (Objectives for activities, projects and ESG results set by the company as a goal)
ESG Relative Performance Measurement (compared to peers, on the basis of key figures the company considers relevant)
ESG Ratings Agencies (Refinitiv, S&P Trucost and RobecoSam, Sustainalytics, ISS ESG, MSCI ESG, Vigeo Eiris, EcoVadis, Minerva Analytics, etc.)
ESG Performance Evaluations (internal or independent performance assessment by means of expert opinions, based on internally and externally available objective and subjective facts)
The three domains of environmental, social, and corporate governance are intimately linked to the concept of responsible investment (RI). RI began as a niche investment area, serving the needs of those who wished to invest but wanted to do so within ethically defined parameters. In recent years it has become a much larger proportion of the investment market. By June 2020, flows into U.S. sustainable funds reached $20.9 billion, nearly matching 2019's flows of $21.4 billion. By the end of 2020, flows into U.S. sustainable funds surpassed $51 billion. Globally, sustainable funds held $1.65 trillion in assets at the end of 2020.
ESG corporate reporting can be used by stakeholders to assess the material sustainability-related risks and opportunities relevant to an organization. Investors may also use ESG data beyond assessing material risks to the organization in their evaluation of enterprise value, specifically by designing models based on assumptions that the identification, assessment, and management of sustainability-related risks and opportunities with respect to all organizational stakeholders leads to higher long-term risk-adjusted return.
Investment strategies
RI seeks to control the placing of its investments via several methods:
Positive selection; where the investor actively selects the companies in which to invest; this can be done either by following a defined set of ESG criteria or by the best-in-class method where a subset of high performing ESG compliant companies is chosen for inclusion in an investment portfolio.
Activism; strategic voting by shareholders in support of a particular issue, or to bring about change in the governance of the company.
Engagement; investment funds monitoring the ESG performance of all portfolio companies and leading constructive shareholder engagement dialogues with each company to ensure progress.
Consulting role; the larger institutional investors and shareholders tend to be able to engage in what is known as 'quiet diplomacy', with regular meetings with top management in order to exchange information and act as early warning systems for risk and strategic or governance issues.
Exclusion; the removal of certain sectors or companies from consideration for investment, based on ESG-specific criteria.
Integration; the inclusion of ESG risks and opportunities into traditional financial analysis of equity value.
However, in doing so, additional risks are introduced.
Concentration risk; for example, when compared to the FTSE All-World index, the FTSE4Good Index has an increased weighting towards technology companies
Lack of effectiveness in removing some industries; companies specialising in alcohol, tobacco, gambling, defence, AI, cryptocurrencies, oil, gas and coal are all still represented in the main indices
Relationship between ESG and Corporate Valuation
Empirical research on the relationship between environmental, social, and governance (ESG) practices and corporate value has produced mixed findings. Some studies report a positive association between certain ESG indicators and measures of firm performance that are linked to market valuation[87][88]. However, several studies have questioned the assumption that increased investment in ESG criteria always contributes to corporate value. Some researchers suggest that excessive investment or over-monitoring of ESG initiatives can not only be ineffective but also counterproductive to a firm's valuation. In certain cases, overinvestment in ESG by firms or excessive oversight by investors may limit expected benefits.
In this context, research suggests that the relationship between ESG performance and corporate valuation may be non-linear, exhibiting polynomial characteristics such as inverted U-shaped patterns. This implies the existence of an optimal level of ESG investment where benefits to corporate valuation are maximised, beyond which diminishing returns or even adverse effects may arise.
One of the defining marks of the modern investment market is the divergence in the relationship between the firm and its equity investors. Institutional investors have become the key owners of stock—rising from 35% in 1981 to 58% in 2002 in the US and from 42% in 1963 to 84.7% in 2004 in the UK and institutions tend to work on a long-term investment strategy. Insurance companies, Mutual Funds and Pension Funds with long-term payout obligations are much more interested in the long term sustainability of their investments than the individual investor looking for short-term gain. Where a Pension Fund is subject to ERISA, there are legal limitations on the extent to which investment decisions can be based on factors other than maximizing plan participants' economic returns.
Based on the belief that addressing ESG issues will protect and enhance portfolio returns, responsible investment is rapidly becoming a mainstream concern within the institutional industry. By late 2016, over a third of institutional investors (commonly referred to as LPs) based in Europe and Asia-Pacific said that ESG considerations played a major or primary role in refusing to commit to a private equity fund, while the same is true for a fifth of North American LPs. In reaction to investor interest in ESG, private equity and other industry trade associations have developed a number of ESG best practices, including a due diligence questionnaire for private fund managers and other asset managers to use before investing in a portfolio company.
There was a clear acceleration of the institutional shift towards ESG-informed investments in the second semester of 2019. The notion of "SDG Driven Investment" gained further ground amongst pension funds, SWFs and asset managers in the second semester of 2019, notably at the G7 Pensions Roundtable held in Biarritz, 26 August 2019, and the Business Roundtable held in Washington, DC, on 19 August 2019.
Networks of institutional investors committed to curbing climate change have emerged, where in institutional investors are agreeing to hold themselves accountable to climate action targets. One such example is the Institutional Investors Group on Climate Change, looking to deliver significant progress to net zero by 2030. Moreover, the networks have collaborated with investment frameworks to "evaluate" corporate progress to net zero, with one such framework being the Climate Action 100+, a series of criterion used to evaluate the companies emitting the largest quantity of GHG.
The Equator Principles is a risk management framework, adopted by financial institutions, for determining, assessing and managing environmental and social risk in project finance. It is primarily intended to provide a minimum standard for due diligence to support responsible risk decision-making. As of October 2019, 97 adopting financial institutions in 37 countries had officially adopted the Equator Principles, the majority of international Project Finance debt in emerging and developed markets. Equator Principles Financial Institutions (EPFIs) commit to not provide loans to projects where the borrower will not or is unable to comply with their respective social and environmental policies and procedures.
The Equator Principles, formally launched in Washington DC on 4 June 2003, were based on existing environmental and social policy frameworks established by the International Finance Corporation. These standards have subsequently been periodically updated into what is commonly known as the International Finance Corporation Performance Standards on social and environmental sustainability and on the World Bank Group Environmental, Health, and Safety Guidelines.
Statistics
United Kingdom
According to a nationally representative survey from Finder UK, over half (57%) of UK investors hold an ESG investment. Gen Z being the most likely generation to invest through ESG, with 66% of respondents claiming an interest in ESG investing. Baby boomers were found to be the least likely to consider an ethical investment, with only 11% of this generation planning to invest in an ethical investment.
ESG rating agencies
ESG rating agencies are the main infomediaries of ESG investing. Sustainalytics estimated the number of ESG-rating companies in the ecosystem at over 600 in 2018.
The ESG rating providers market is going through an increasing trend of concentration. For instance, the data aggregator Morningstar took 40% of Sustainalytics stakes by 2017. Following that, the rating agency Moody's acquired Vigeo Eiris in 2019, the former leader of European ESG rating agencies. Institutional Shareholder Services( ISS) acquired Germany's Oekom, while S&P Global acquired the ESG rating business of RobecoSAM. The market's structure is divided between a few very large non-EU providers on one side, and numerous smaller EU providers on the other.
In this highly concentrated ecosystem, small groups of big index providers, like MSCI, play a pivotal role in setting the standards for what is generally accepted as sustainable finance.
As for categorizing ESG rating agencies by purpose, it is crucial to distinguish between two private ESG rating clusters. First, the ESG risk rating agencies (eg: MSCI, Sustainalytics, S&P, FTSE Russell), they are meant to measure how exposed a company is towards ESG risks -meaning the negative externalities affect the company- more than concrete action on the three factors. Secondly, the ESG effectiveness rating agencies (eg: Refinitiv, Moody s, ECPI, Sensefolio, Inrate) which measures ESG factors commitment, integration and results and therefore outward effect on society.
This classification is helpful for understanding the confusion around ESG ratings inefficiency in facing the big challenges ahead on the three factors. Indeed, a company with a higher score doesn't necessarily mean that it has strong environmental, social and governance effect on the world, but rather a low exposure to ESG risks.
Asset managers and other financial institutions increasingly rely on ESG rating agencies to assess, measure and compare companies' ESG performance. More recently, publications like Newsweek have used ESG data provided by market research companies like Statista to rate the most responsible organizations in a country.
Data providers such as ESG Analytics have applied artificial intelligence to rate companies and their commitment to ESG. Each rating agency uses its own set of metrics to measure the level of ESG compliance and there is, at present, no industry-wide set of common standards.
In Latin America, the Latin American Quality Institute (LAQI), headquartered in Panama City and operating in 19 countries, leads the ESG certification movement with more than 10,000 certifications issued. In 2024, LAQI launched the LAQI Q-ESG CERTIFICATION, a structured institutional validation framework assessing organizational maturity across four dimensions — Quality (Q), Environmental (E), Social (S), and Governance (G) — applicable to SMEs across 22 sectors. Each certificate is recorded on LAQIChain, a blockchain registry on the Polygon network.
Disclosure and regulation
The first ten years of the 21st century has seen growth in the ESG defined investment market. Not only do most of the world's big banks have departments and divisions exclusively addressing Responsible Investment but boutique firms specialising in advising and consulting on environmental, social, and governance related investments are proliferating. One of the major aspects of the ESG side of the insurance market which leads to this tendency to proliferation is the essentially subjective nature of the information on which investment selection can be made. By definition ESG data is qualitative; it is non-financial and not readily quantifiable in monetary terms. The investment market has long dealt with these intangibles—such variables as goodwill have been widely accepted as contributing to a company's value. But the ESG intangibles are not only highly subjective they are also particularly difficult to quantify and more importantly verify. A lack of clear standards and transparent monitoring has led to fears that ESG avowals mainly serve purposes of greenwashing and other company public relations objectives, while distracting from more substantive initiatives to improve environment and society.
One of the major issues in the ESG area is disclosure. Environmental risks created by business activities have actual or potential negative effects on air, land, water, ecosystems, and human health. The information on which an investor makes their decisions on a financial level is fairly simply gathered. The company's accounts can be examined, and although the accounting practices of corporate business are coming increasingly into disrepute after a spate of recent financial scandals, the figures are for the most part externally verifiable. With ESG considerations, the practice has been for the company under examination to provide its own figures and disclosures. These have seldom been externally verified and the lack of universal standards and regulation in the areas of environmental and social practice mean that the measurement of such statistics is subjective to say the least.
One of the solutions put forward to the inherent subjectivity of ESG data is the provision of universally accepted standards for the measurement of ESG factors. Such organizations as the ISO (International Organization for Standardization) provide highly researched and widely accepted standards for many of the areas covered. Some investment consultancies, such as Probus-Sigma have created methodologies for calculating the ratings for an ESG based Ratings Index that is both based on ISO standards and externally verified, but the formalization of the acceptance of such standards as the basis for calculating and verifying ESG disclosures is by no means universal.
The corporate governance side of the matter has received rather more in the way of regulation and standardization as there is a longer history of regulation in this area. In 1992 the London Stock Exchange and the Financial Reporting Commission set up the Cadbury Commission to investigate the series of governance failures that had plagued the City of London such as the bankruptcies of BCCI, Polly Peck, and Robert Maxwell's Mirror Group. The conclusions that the commission reached were compiled in 2003 into the Combined Code on Corporate Governance which has been widely accepted (if patchily applied) by the financial world as a benchmark for good governance practices.
In an interview with Yahoo! Finance Francis Menassa (JAR Capital) says, that "the EU's 2014 Non-Financial Reporting Directive will apply to every country on a national level to implement and requires large companies to disclose non-financial and diversity information. This also includes providing information on how they operate and manage social and environmental challenges. The aim is to help investors, consumers, policy makers, and other stakeholders to evaluate the non-financial performance of large companies. Ultimately, the Directive encourages European companies to develop a responsible approach to business".
One of the key areas of concern in the discussion as to the reliability of ESG disclosures is the establishment of credible ratings for companies as to ESG performance. The world's financial markets have all leapt to provide ESG relevant ratings indexes, the Dow Jones Sustainability Index, the FTSE4Good Index (which is co-owned by the London Stock Exchange and Financial Times), Bloomberg ESG data, the MSCI ESG Indices and the GRESB benchmarks.
European regulators have introduced concrete rules to deal with the problem of greenwashing. These include a package of legislative measures arising from the European Commission's Action Plan on Sustainable Finance.
In November 2021, the SEC rescinded a Trump administration rule issued in 2017 that permitted company managers to exclude ESG proposals from shareholders in annual proxy statements. In May 2022, the SEC proposed two rules changes to ESG investment fund qualifications to prevent greenwashing marketing practices and to increase disclosure requirements for achieving ESG effects. In October 2022, the SEC announced that it would re-open the public comment window for the ESG disclosure rules proposal due to a technical error with the SEC public comment internet submission form. In November 2022, EBSA announced a final rule removing the Trump administration pecuniary interest only requirement for fiduciaries in proxy voting under ERISA when considering ESG investments for 401(k)s. In March 2023, in the first veto of his administration, U.S. PresidentJoe Biden rejected a bill passed by the 118th United States Congress on party-line votes to overturn the EBSA ERISA 401(k) fiduciary proxy voting rule for ESG investments finalized the previous November.
ESG reporting, which stands for Environmental, Social, and Governance reporting, is when a company shares information about its effect on the environment, society, and how it is governed. This kind of reporting is usually done on a voluntary basis, meaning companies choose to do it to be open and share important information with their stakeholders, including investors.
However, in some places like India and certain regions, there are rules that make ESG reporting a requirement for specific types of companies. For example, in India, there is a regulatory requirement called BRSR (Business Responsibility and Sustainability Reporting) that makes ESG reporting mandatory for the top 1000 companies based on their market value on the stock exchange. They have to provide this report to ensure transparency and disclosure regarding their sustainability and responsibility practices.
Litigation and oversight
The Kentucky Bankers Association of 150 banks doing business in Kentucky is suing Kentucky Attorney General Daniel Cameron over his investigating banks' ESG practices, such as commitments to combat climate change. In November 2022, the Kentucky Bankers Association sued Cameron in Franklin Circuit Court; Cameron had the case removed to the US District Court for the Eastern District of Kentucky before Judge Gregory Van Tatenhove, for whom Cameron was previously a law clerk. The association said Cameron has displayed "amazing and disturbing broad overreach" by overstepping his legal authority, and did not have authority to demand detailed information from banks as part of an investigation into their environmental lending practices, which it said was a big government intrusion on private businesses that could create "an ongoing state surveillance system."
In March 2021, the SEC also announced the creation of a task force to pursue enforcement cases against investment fund managers and public companies for deceptive marketing for ESG investment funds. In August 2021, the SEC and the Eastern New York U.S. Attorney's Office were reportedly investigating the DWS Group (the asset management division of Deutsche Bank) after its former chief sustainability officer leaked internal emails and company presentations to The Wall Street Journal that showed that the company had overstated its ESG investment efforts.In December 2021, the U.S. Justice Department informed Deutsche Bank that it may have violated its deferred prosecution agreement from the previous January for failing to inform prosecutors of their former chief sustainability officer's internal complaint about the DWS Group's overstating of its ESG investment efforts.
In March 2022, Deutsche Bank agreed to extend the term of an external compliance monitor until February 2023 from its 2015 settlement with the Justice Department to address its failure to disclose the internal ESG complaint from its former chief sustainability officer the previous August. In June 2023, the EU commission issued an ESG ratings regulation proposal to guarantee their integrity and transparency.
In June 2022, the SEC was reportedly investigating the ESG investment funds of Goldman Sachs for potential greenwashing. In November 2022, Goldman Sachs agreed to pay $4 million to settle the SEC investigation of the company's ESG funds for greenwashing without admitting or denying guilt of the SEC's allegations.[181] In February 2023, the SEC Division of Examinations announced that oversight of ESG investment funds would be among six top priorities for the agency in 2023.
Research findings
According to a 2021 study done by the NYU Stern Center for Sustainable Business, which looked at over 1,000 studies, "studies use different scores for different companies by different data providers."
Gallup finds that 28% of U.S. employees strongly agree with the statement, "My organization makes a positive impact on people and the planet."
Research shows that such intangible assets comprise an increasing percentage of future enterprise value.
A study published by the European Securities and Markets Authority has also found that "ESG generally improves returns and cuts client costs over time". Analysis over a five-year period showed stock funds weighted towards ESG scores generally performed higher: an increase in annual average return of 1.59% in European markets, 1.02% in Asia-Pacific markets, and 0.13-0.17% in North American and global markets.
In January 2023, a Rasmussen opinion poll in the U.S. reported that the proportion of Americans who considered the promotion of "causes like diversity and environmentalism" to be the most important aim for companies was 9%. 69% said that the focus should be on "providing quality goods and services," and 13% on "increasing profit". A poll by PricewaterhouseCoopers found that "83% of consumers think companies should be actively shaping ESG best practices", with 76% of consumers saying they would "discontinue relations with companies that treat employees, communities and the environment poorly".
Compromises in real-world useability
ESG guidelines among western European arms manufacturers have been criticised for compromising on practical battlefield durability over environmentally-friendly manufacturing practices. During the Russo-Ukrainian War, military weaponry and equipment supplied to the Armed Forces of Ukraine by western European countries containing electronic components with cable insulation made from corn fibre in place of synthetic insulators have succumbed to malfunction due to damage caused by rodents.
.png)
