Generative AI

From Wikipedia, the free encyclopedia

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

Théâtre D'opéra Spatial (Space Opera Theater, 2022), an image made with Midjourney that won an award at the Colorado State Fair's fine art competition

Generative artificial intelligence, commonly known as generative AI or GenAI, is a subfield of artificial intelligence that uses generative models to generate text, images, videos, audio, software code (vibe coding) or other forms of data. These models learn the underlying patterns and structures of their training data, and use them to generate new data in response to input, which often takes the form of natural language prompts.

The prevalence of generative AI tools has increased significantly since the AI boom in the 2020s. This boom was made possible by improvements in deep neural networks, particularly large language models (LLMs), which are based on the transformer architecture. Generative AI applications include chatbots such as ChatGPT, Claude, Copilot, DeepSeek, Google Gemini and Grok; text-to-image models such as DALL-E, Firefly, Stable Diffusion, and Midjourney; and text-to-video models such as Veo, LTX and Sora.

Companies in a variety of sectors have used generative AI, including those in software development, healthcare, finance, entertainment, customer service, sales and marketing, art, writing, and product design.

Generative AI has been used for cybercrime, and to deceive and manipulate people through fake news and deepfakes. Generative AI models have been trained on copyrighted works without the rightholders' permission. Many generative AI systems use large-scale data centers, whose environmental impacts include electronic waste, consumption of fresh water for cooling, and high energy consumption that is estimated to be growing steadily.

History

Main article: History of artificial intelligence

Early history

The origins of algorithmically generated media can be traced to the development of the Markov chain, which has been used to model natural language since the early 20th century. Russian mathematician Andrey Markov introduced the concept in 1906, including an analysis of vowel and consonant patterns in Eugeny Onegin. Once trained on a text corpus, a Markov chain can generate probabilistic text.

By the early 1970s, artists began using computers to extend generative techniques beyond Markov models. Harold Cohen developed and exhibited works produced by AARON, a pioneering computer program designed to autonomously create paintings. The terms generative AI planning or generative planning were used in the 1980s and 1990s to refer to AI planning systems, especially computer-aided process planning, used to generate sequences of actions to reach a specified goal. Generative AI planning systems used symbolic AI methods such as state space search and constraint satisfaction and were a "relatively mature" technology by the early 1990s. They were used to generate crisis action plans for military use, process plans for manufacturing and decision plans such as in prototype autonomous spacecraft.

Generative neural networks (since the late 2000s)

See also: Machine learning and deep learning

Above: An image classifier, an example of a neural network trained with a discriminative objective. Below: A text-to-image model, an example of a network trained with a generative objective.

Machine learning uses both discriminative models and generative models to predict data. Beginning in the late 2000s and early 2010s, advances in deep learning led to major improvements in image classification, speech recognition, and natural language processing. Neural networks in this period were typically trained as discriminative models due to the relative difficulty of training generative models.

In 2014, the introduction of models such as the variational autoencoder (VAE) and generative adversarial network (GAN) enabled effective deep generative modeling of complex data such as images.

In 2017, the Transformer architecture enabled further advances in generative modeling compared to earlier long short-term memory (LSTM) networks. This led to the development of generative pre-trained transformer (GPT) models, beginning with GPT-1 in 2018.

Generative AI adoption

Main article: AI boom

AI generated images have become much more advanced.

In March 2020, the release of 15.ai, a free web application created by an anonymous MIT researcher that could generate convincing character voices using minimal training data, was one of the earliest publicly available uses for generative AI. The platform is credited as the first mainstream service for audio deepfakes.

In 2021, DALL-E, a closed-source transformer-based generative model developed by OpenAI, drew widespread attention to text-to-image generation.

Other projects, including open-source approaches such as VQGAN+CLIP and DALL·E Mini (later renamed Craiyon), made similar systems more accessible to the public.

Dream by Wombo was released at the end of 2021, followed by the releases of Midjourney and Stable Diffusion in 2022.

In November 2022, the public release of ChatGPT popularized generative AI for general-purpose text-based tasks.

Private investment in AI (pink) and generative AI (green)

In a 2024 survey by marketing research firm Ipsos, Asia–Pacific countries were significantly more optimistic than Western societies about generative AI and show higher adoption rates. Despite expressing concerns about privacy and the pace of change, 68% of Asia-Pacific respondents believed that AI was having a positive impact on the world, compared to 57% globally. According to a survey by SAS and Coleman Parkes Research, as of 2023, 83% of Chinese respondents were using the technology, exceeding both the global average of 54% and the U.S. rate of 65%. A UN report indicated that Chinese entities filed over 38,000 generative AI patents from 2014 to 2023, more than any other country. A 2024 survey by the Just So Soul social media app reported that 18% of respondents born after 2000 used generative AI "almost every day", and that over 60% of respondents like or love AI-generated content (AIGC), while less than 3% dislike or hate it.

By mid 2025 companies were increasingly abandoning generative AI pilot projects as they had difficulties with integration, data quality and unmet returns, leading analysts at Gartner and The Economist to characterize the period as entering the Gartner hype cycle's "trough of disillusionment" phase.

Applications

Main article: Applications of artificial intelligence

Generative artificial intelligence has been applied across multiple industries for content creation and automation. In healthcare, generative models are used for drug discovery and the generation of synthetic medical data to train diagnostic systems. In finance, they are used for report drafting, data generation, and customer service automation. Media and entertainment industries use generative systems for tasks such as music composition, script development, and image or video generation. Researchers and policymakers have raised concerns regarding accuracy, misuse, and impacts on academic and professional work.

Text and software code

Main article: Large language model

See also: Code completion, Autocomplete, List of large language models, List of text corpora, and List of chatbots

Large language models (LLMs) are trained on tokenized text from large corpora and are capable of natural language processing, machine translation, and natural language generation.

LLMs can be used as foundation models for a variety of downstream tasks. They can also be trained on source code to generate programs from prompts.

Audio

See also: Generative audio and Music and artificial intelligence

In 2016, DeepMind's WaveNet demonstrated that deep neural networks can generate raw audio waveforms. This enabled more realistic speech synthesis compared to earlier approaches. Subsequent systems such as Tacotron 2 demonstrated end-to-end neural text-to-speech generation.

Images

See also: Text-to-image model and Artificial intelligence art

Generative AI can be used to create visual art. Such systems are trained on image–text pairs. Examples include Stable Diffusion, DALL-E, and Midjourney.

Video

See also: Text-to-video model

Generative AI can be used to produce photorealistic videos. Systems such as Runway have demonstrated text-to-video generation capabilities.

Robotics

Generative models can be used for motion planning and robot control by learning from prior data.

3D modeling

Generative models can assist in automating 3D modeling tasks, including generating 3D assets from text or images.

World models

World models are neural networks designed to learn representations of physical environments, including spatial and dynamic properties. Recent multimodal systems have expanded these capabilities by integrating vision, language, and action into unified models.

Software and hardware

Architecture of a generative AI agent

Generative AI models are used to power chatbot products such as ChatGPT, programming tools such as GitHub Copilot, text-to-image products such as Midjourney, and text-to-video products such as Runway Gen-2. Generative AI features have been integrated into a variety of existing commercially available products such as Microsoft Office (Microsoft Copilot), Google Photos, and the Adobe Suite (Adobe Firefly). Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA language model.

Smaller generative AI models with up to a few billion parameters can run on smartphones, embedded devices, and personal computers. For example, LLaMA-7B (a version with 7 billion parameters) can run on a Raspberry Pi 4 and one version of Stable Diffusion can run on an iPhone 11.

Larger models with tens of billions of parameters can run on laptop or desktop computers. To achieve an acceptable speed, models of this size may require accelerators such as the GPU chips produced by NVIDIA and AMD or the Neural Engine included in Apple silicon products. For example, the 65 billion parameter version of LLaMA can be configured to run on a desktop PC.

The advantages of running generative AI locally include protection of privacy and intellectual property, and avoidance of rate limiting and censorship. The subreddit r/LocalLLaMA in particular focuses on using consumer-grade gaming graphics cards through such techniques as compression.

Language models with hundreds of billions of parameters, such as GPT-4 or PaLM, typically run on datacenter computers equipped with arrays of GPUs (such as NVIDIA's H100) or AI accelerator chips (such as Google's TPU). These very large models are typically accessed as cloud services over the Internet.

In 2022, the United States New Export Controls on Advanced Computing and Semiconductors to China imposed restrictions on exports to China of GPU and AI accelerator chips used for generative AI. Chips such as the NVIDIA A800 and the Biren Technology BR104 were developed to meet the requirements of the sanctions.

There is free software on the market capable of recognizing text generated by generative artificial intelligence (such as GPTZero), as well as images, audio or video coming from it. Potential mitigation strategies for detecting generative AI content include digital watermarking, content authentication, information retrieval, and machine learning classifier models. Despite claims of accuracy, both free and paid AI text detectors have frequently produced false positives, mistakenly accusing students of submitting AI-generated work.

Generative models and training techniques

Generative adversarial networks

Workflow for the training of a generative adversarial network

Generative adversarial networks (GANs) are a generative modeling technique which consist of two neural networks—the generator and the discriminator—trained simultaneously in a competitive setting. The generator creates synthetic data by transforming random noise into samples that resemble the training dataset. The discriminator is trained to distinguish the authentic data from synthetic data produced by the generator. The two models engage in a minimax game: the generator aims to create increasingly realistic data to "fool" the discriminator, while the discriminator improves its ability to distinguish real from fake data. This continuous training setup enables the generator to produce high-quality and realistic outputs.

Variational autoencoders

Comparison between images generated by a VAE (left) and a GAN (right). VAEs tend to produce smoother but blurrier images due to their probabilistic decoding.

Variational autoencoders (VAEs) are deep learning models that probabilistically encode data. They are typically used for tasks such as noise reduction from images, data compression, identifying unusual patterns, and facial recognition. Unlike standard autoencoders, which compress input data into a fixed latent representation, VAEs model the latent space as a probability distribution, allowing for smooth sampling and interpolation between data points. The encoder ("recognition model") maps input data to a latent space, producing means and variances that define a probability distribution. The decoder ("generative model") samples from this latent distribution and attempts to reconstruct the original input. VAEs optimize a loss function that includes both the reconstruction error and a Kullback–Leibler divergence term, which ensures the latent space follows a known prior distribution. VAEs are particularly suitable for tasks that require structured but smooth latent spaces, although they may create blurrier images than GANs. They are used for applications like image generation, data interpolation and anomaly detection.

The full architecture of a GPT model

Transformers

Transformers became the foundation for the generative pre-trained transformer (GPT) series developed by OpenAI, replacing traditional recurrent and convolutional models.

The self-attention mechanism enables the model to determine the relative importance of each token in a sequence when predicting the next token, thereby improving contextual understanding. Unlike recurrent neural networks, transformers process tokens in parallel, which improves training efficiency and scalability.

Transformers are typically pre-trained on large corpora using self-supervised learning and then fine-tuned for specific tasks.

Law and regulation

Main article: Regulation of artificial intelligence

In the United States, a group of companies including OpenAI, Alphabet, and Meta signed a voluntary agreement with the Biden administration in July 2023 to watermark AI-generated content. In October 2023, Executive Order 14110 applied the Defense Production Act to require all US companies to report information to the federal government when training certain high-impact AI models.

In the European Union (EU), the proposed Artificial Intelligence Act includes requirements to disclose copyrighted material used to train generative AI systems, and to label any AI-generated output as such.

In China, the Interim Measures for the Management of Generative AI Services introduced by the Cyberspace Administration of China regulates any public-facing generative AI. It includes requirements to watermark generated images or videos, regulations on training data and label quality, restrictions on personal data collection, and a guideline that generative AI services must "adhere to socialist core values".

Copyright

Main article: Artificial intelligence and copyright

Training with copyrighted content

Generative AI systems such as ChatGPT and Midjourney are trained on large, publicly available datasets that include copyrighted works. AI developers have argued that such training is protected under fair use, while copyright holders have argued that it infringes their rights.

Proponents of fair use training have argued that it is a transformative use and does not involve making copies of copyrighted works available to the public. Critics have argued that image generators such as Midjourney can create nearly-identical copies of some copyrighted images, and that generative AI programs compete with the content they are trained on.

As of 2024, several lawsuits related to the use of copyrighted material in training are ongoing. Getty Images has sued Stability AI over the use of its images to train Stable Diffusion. Both the Authors Guild and The New York Times have sued Microsoft and OpenAI over the use of their works to train ChatGPT.

Copyright of AI-generated content

A separate question is whether AI-generated works can qualify for copyright protection. The United States Copyright Office has ruled that works created by artificial intelligence without any human input cannot be copyrighted, because they lack human authorship. Some legal professionals have suggested that Naruto v. Slater (2018), in which the U.S. 9th Circuit Court of Appeals held that non-humans cannot be copyright holders of artistic works, could be a potential precedent in copyright litigation over works created by generative AI. However, the office has also begun taking public input to determine if these rules need to be refined for generative AI.

In January 2025, the United States Copyright Office (USCO) released extensive guidance regarding the use of AI tools in the creative process, and established that "...generative AI systems also offer tools that similarly allow users to exert control. [These] can enable the user to control the selection and placement of individual creative elements. Whether such modifications rise to the minimum standard of originality required under Feist will depend on a case-by-case determination. In those cases where they do, the output should be copyrightable" Subsequently, the USCO registered the first visual artwork to be composed of entirely AI-generated materials, titled "A Single Piece of American Cheese".

Concerns

See also: Ethics of artificial intelligence and Artificial intelligence controversies

The development of generative AI has raised concerns from governments, businesses, and individuals, resulting in protests, legal actions, calls to pause AI experiments, and actions by multiple governments. In a July 2023 briefing of the United Nations Security Council, Secretary-General António Guterres stated "Generative AI has enormous potential for good and evil at scale", that AI may "turbocharge global development" and contribute between $10 and $15 trillion to the global economy by 2030, but that its malicious use "could cause horrific levels of death and destruction, widespread trauma, and deep psychological damage on an unimaginable scale". In addition, generative AI has a significant carbon footprint.

Societal impacts

Academic honesty

Generative AI can be used to generate and modify academic prose, paraphrase sources, and translate languages. The use of generative AI in a classroom setting has challenged traditional definitions of academic plagiarism, leading to a "cat-and-mouse" dynamic between students using AI and institutions attempting to detect it. In the immediate wake of ChatGPT's release, many school districts and universities issued temporary bans on the technology, though many institutions have since moved toward policies of managed integration. However, the implementation of these policies often lacks clarity. Research suggests that the burden of interpreting "acceptable use" frequently falls on individual students and teachers, creating an environment where academic honesty becomes difficult to define and enforce.

A commonly proposed use for teachers is grading and giving feedback. Companies like Pearson and ETS use AI to score grammar, mechanics, usage, and style, but not for main ideas or overall structure. The National Council of Teachers of English stated that machine scoring makes students feel their writing is not worth reading. AI scoring has also given unfair results for students from different ethnic backgrounds.

Fears over job losses

Main articles: Workplace impact of artificial intelligence and Technological unemployment

A picketer at the 2023 Writers Guild of America strike. While not a top priority, one of the WGA's 2023 requests was "regulations around the use of (generative) AI".

From the early days of the development of AI, there have been arguments put forward by ELIZA creator Joseph Weizenbaum and others about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculations and qualitative, value-based judgements. In April 2023, it was reported that image generation AI has resulted in 70% of the jobs for video game illustrators in China being lost. In July 2023, developments in generative AI contributed to the 2023 Hollywood labor disputes. Fran Drescher, president of the Screen Actors Guild, declared that "artificial intelligence poses an existential threat to creative professions" during the 2023 SAG-AFTRA strike. Voice generation AI has been seen as a potential challenge to the voice acting sector.

However, a 2025 study concluded that the US labor market had so far not experienced a discernible disruption from generative AI. Another study reported that Danish workers who used chatbots saved 2.8% of their time on average, and found no significant change in earnings or hours worked.

Use in journalism

See also: Automated journalism and List of fake news websites § Generative AI

In January 2023, Futurism broke the story that CNET had been using an undisclosed internal AI tool to write at least 77 of its stories; after the news broke, CNET posted corrections to 41 of the stories. In April 2023, Die Aktuelle published an AI-generated fake interview of Michael Schumacher. In May 2024, Futurism noted that a content management system video by AdVon Commerce, which had used generative AI to produce articles for many of the aforementioned outlets, appeared to show that they "had produced tens of thousands of articles for more than 150 publishers". In 2025, a report from the American Sunlight Project stated that Pravda network was publishing as many as 10,000 articles a day, and concluded that much of this content aimed to push Russian narratives into large language models through their training data.

In June 2024, Reuters Institute published its Digital News Report for 2024. In a survey of people in America and Europe, Reuters Institute reports that 52% and 47% respectively are uncomfortable with news produced by "mostly AI with some human oversight", and 23% and 15% respectively report being comfortable. 42% of Americans and 33% of Europeans reported that they were comfortable with news produced by "mainly human with some help from AI". The results of global surveys reported that people were more uncomfortable with news topics including politics (46%), crime (43%), and local news (37%) produced by AI than other news topics.

A 2025 Pew Research Survey found roughly half of all U.S. adults say that AI will have a very (24%) or somewhat (26%) negative impact on the news people get in the U.S. over the next 20 years. Because AI cannot do journalism, which requires interviewing people and a high degree of accuracy, AI poses a greater threat to journalism from the information it takes from publishers.

Bias

Racial and gender bias

Generative AI models can reflect and amplify cultural bias present in their training data. For example, a language model may associate certain professions with specific genders if such patterns are prevalent in the data. Similarly, image generation systems prompted with terms such as "a photo of a CEO" have been observed to disproportionately generate images of white male individuals when trained on biased datasets.

Various methods have been proposed to mitigate bias in generative AI systems, including modifying input prompts and reweighting training data.

Misinformation and disinformation

Deepfakes

Main article: Deepfake

Deepfakes (a portmanteau of "deep learning" and "fake") are AI-generated media that take a person in an existing image or video and replace them with someone else's likeness using artificial neural networks. Deepfakes have garnered widespread attention and concerns for their uses in deepfake celebrity pornographic videos, revenge porn, fake news, hoaxes, health disinformation, financial fraud, and covert foreign election interference.

In July 2023, the fact-checking company Logically found that the popular generative AI models Midjourney, DALL-E 2 and Stable Diffusion would produce plausible disinformation images when prompted to do so, such as images of electoral fraud in the United States and Muslim women supporting India's Bharatiya Janata Party.

Audio deepfakes

Main article: Audio deepfake

Instances of users abusing software to generate controversial statements in the vocal style of celebrities, public officials, and other famous individuals have raised ethical concerns over voice generation AI. In response, companies such as ElevenLabs have stated that they would work on mitigating potential abuse through safeguards and identity verification.

Concerns and fandoms have spawned from AI-generated music. The same software used to clone voices has been used on famous musicians' voices to create songs that mimic their voices, gaining both tremendous popularity and criticism. Similar techniques have also been used to create improved quality or full-length versions of songs that have been leaked or have yet to be released.

Information laundering

Generative AI has been noted for its use by state-sponsored propaganda campaigns in information laundering. According to a 2025 report by Graphika, generative AI is used to launder articles from Chinese state media such as China Global Television Network through various social media sites in an attempt to disguise the articles' origin.

Content quality

See also: AI slop and Dead Internet theory

The New York Times defines slop as analogous to spam: "shoddy or unwanted A.I. content in social media, art, books, and ... in search results." Journalists have expressed concerns about the scale of low-quality generated content with respect to social media content moderation, the monetary incentives from social media companies to spread such content, false political messaging, spamming of scientific research paper submissions, increased time and effort to find higher quality or desired content on the Internet, the indexing of generated content by search engines, and on journalism itself. Studies have found that AI can create inaccurate claims, citations or summaries that sound confidently correct, a phenomenon called hallucination.

A paper published by researchers at Amazon Web Services AI Labs found that over 57% of sentences from a sample of over 6 billion sentences from Common Crawl, a snapshot of web pages, were machine translated. Many of these automated translations were seen as lower quality, especially for sentences that were translated into at least three languages. Many lower-resource languages (ex. Wolof, Xhosa) were translated across more languages than higher-resource languages (ex. English, French).

In September 2024, Robyn Speer, the author of wordfreq, an open source database that calculated word frequencies based on text from the Internet, announced that she had stopped updating the data for several reasons: high costs for obtaining data from Reddit and Twitter, excessive focus on generative AI compared to other methods in the natural language processing community, and that "generative AI has polluted the data".

The adoption of generative AI tools led to an explosion of AI-generated content across multiple domains. A study from University College London estimated that in 2023, more than 60,000 scholarly articles—over 1% of all publications—were likely written with LLM assistance. According to Stanford University's Institute for Human-Centered AI, approximately 17.5% of newly published computer science papers and 16.9% of peer review text now incorporate content generated by LLMs.

If AI-generated content is included in new data crawls from the Internet for additional training of AI models, defects in the resulting models may occur. Training an AI model exclusively on the output of another AI model produces a lower-quality model. Repeating this process, where each new model is trained on the previous model's output, leads to progressive degradation and eventually results in a "model collapse" after multiple iterations.

On the other side, synthetic data can be deployed to train machine learning models while preserving user privacy. The approach is not limited to text generation; image generation has been employed to train computer vision models.

Malicious use

Illegal imagery

Main article: Child pornography § Artificially generated or simulated imagery

Many websites that allow explicit AI generated images or videos have been created, and this has been used to create illegal content, such as rape, child sexual abuse material, necrophilia, and zoophilia.

Cybercrime

Generative AI's ability to create realistic fake content has been exploited in numerous types of cybercrime, including phishing scams. Deepfake video and audio have been used to create disinformation and fraud. In 2020, former Google click fraud czar Shuman Ghosemajumder argued that once deepfake videos become perfectly realistic, they would stop appearing remarkable to viewers, potentially leading to uncritical acceptance of false information. Additionally, large language models and other forms of text-generation AI have been used to create fake reviews of e-commerce websites to boost ratings. Cybercriminals have created large language models focused on fraud, including WormGPT and FraudGPT.

A 2023 study showed that generative AI can be vulnerable to jailbreaks, reverse psychology and prompt injection attacks, enabling attackers to obtain help with harmful requests, such as for crafting social engineering and phishing attacks. Additionally, other researchers have demonstrated that open-source models can be fine-tuned to remove their safety restrictions at low cost.

RAG poisoning

In 2025, Israel signed a $6 million contract with the US-based firm Clock Tower X that aimed to influence ChatGPT, Gemini and Grok by spreading pro-Israel information onto social media and websites. This was in an attempt to take advantage of the retrieval-augmented generation (RAG) technique which is used by LLMs to provide more up-to-date information.

Privacy and data governance

Extraterritorial data access

The CLOUD Act allows United States authorities to request data from covered service providers, including some AI service providers, regardless of where the data is physically stored. Courts can require parent companies to provide data held by their subsidiaries, and such orders may be accompanied by nondisclosure requirements preventing the provider from notifying affected users. This framework has been described in legal commentary as creating legal tension with Article 48 of the General Data Protection Regulation (GDPR), which restricts the transfer of personal data in response to foreign court or administrative orders unless based on an international agreement. As a result, service providers operating in both jurisdictions may face competing legal obligations under U.S. and EU law.

Environmental and industry impacts

Energy and environment

Main article: Environmental impacts of artificial intelligence

According to research institute Epoch AI, energy consumption per typical ChatGPT query (0.3 watt-hours) is small compared to the average U.S. household consumption per minute (almost 20 watt-hours). Queries containing long entries can consume significantly more energy (2.5 watt-hours for a query of around 7,500 words).

AI has a significant carbon footprint due to growing energy consumption from both training and usage. Scientists and journalists have expressed concerns about the environmental impact that the development and deployment of generative models are having: high CO₂ emissions, large amounts of freshwater used for data centers, high amounts of electricity usage, electronic waste, and pollution due to backup diesel generator exhaust. There is also concern that these impacts may increase as these models are incorporated into widely used search engines such as Google Search and Bing, as chatbots and other applications become more popular and as models need to be retrained.

The carbon footprint of generative AI globally is estimated to be growing steadily, with potential annual emissions ranging from 18.21 to 245.94 million tons of CO₂ by 2035, with the highest estimates for 2035 nearing the impact of the United States beef industry on emissions (currently estimated to emit 257.5 million tons annually as of 2024).

Proposed mitigation strategies include factoring potential environmental costs prior to model development or data collection, increasing efficiency of data centers to reduce electricity/energy usage, building more efficient machine learning models, minimizing the number of times that models need to be retrained, developing a government-directed framework for auditing the environmental impact of these models, regulating for transparency of these models, regulating their energy and water usage, encouraging researchers to publish data on their models' carbon footprint, and increasing the number of subject matter experts who understand both machine learning and climate science.

Reliance on industry giants

Training frontier AI models requires an enormous amount of computing power. Usually only Big Tech companies have the financial resources to make such investments. Smaller start-ups such as Cohere and OpenAI end up buying access to data centers from Google and Microsoft respectively.

Detection and awareness

See also: Artificial intelligence content detection

Tools such as GPTZero can detect content generated by AI. However, they can also make false accusations (false positives). Digital watermarking is a technique that improves detection accuracy. It works by altering the generated content at the source, in subtle ways which can be detected by corresponding software.

In 2023, OpenAI developed a watermarking tool for ChatGPT. They didn't release it, because they worried that users would switch to competitors. They also argued that it would be easy to circumvent, for example by asking another AI to rephrase.

In March 2025, the Cyberspace Administration of China issued rules, requiring online service providers to label AI content.

In May 2025, Google deployed its watermarking tool, SynthID. It marks output from Gemini (text), Imagen (images), and Veo (video). To detect output from these products, one uses Google's "SynthID detector" portal.

In June 2025, users mistakenly accused gaming companies of using generative AI for the video games Little Droid and Catly.

God of the gaps

From Wikipedia, the free encyclopedia

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

"God of the gaps" is a theological concept that emerged in the 19th century, and revolves around the idea that gaps in scientific understanding are regarded as indications of the existence of God. This perspective has its origins in the observation that some individuals, often with religious inclinations, point to areas where science falls short in explaining natural phenomena as opportunities to insert the presence of a divine creator. The term itself was coined in response to this tendency. This theological view suggests that God fills in the gaps left by scientific knowledge, and that these gaps represent moments of divine intervention or influence.

This concept has been met with criticism and debate from various quarters. Detractors argue that this perspective is problematic as it seems to rely on gaps in human understanding and ignorance to make its case for the existence of God. As scientific knowledge continues to advance, these gaps tend to shrink, potentially weakening the argument for God's existence. Critics contend that such an approach can undermine religious beliefs by suggesting that God only operates in the unexplained areas of our understanding, leaving little room for divine involvement in a comprehensive and coherent worldview.

The "God of the gaps" perspective has been criticized for its association with logical fallacies. The "God of the gaps" perspective is also a form of confirmation bias, since it involves interpreting ambiguous evidence (or rather no evidence) as supporting one's existing attitudes. This type of reasoning is seen as inherently flawed and does not provide a robust foundation for religious faith. In this context, some theologians and scientists have proposed that a more satisfactory approach is to view evidence of God's actions within the natural processes themselves, rather than relying on the gaps in scientific understanding to validate religious beliefs.

Origins of the term

From the 1880s, Friedrich Nietzsche's Thus Spoke Zarathustra, Part Two, "On Priests", said that "into every gap they put their delusion, their stopgap, which they called God". The concept, although not the exact wording, goes back to Henry Drummond, a 19th-century evangelist lecturer, from his 1893 Lowell Lectures on The Ascent of Man. He chastises those Christians who point to the things that Science has not explained as presence of God – "gaps which they will fill up with God" – and urges them to embrace all nature as God's, as the work of "an immanent God, which is the God of Evolution, is infinitely grander than the occasional wonder-worker, who is the God of an old theology."

In 1933, Ernest Barnes, the Bishop of Birmingham, used the phrase in a discussion of general relativity's implication of a Big Bang:

Must we then postulate Divine intervention? Are we to bring in God to create the first current of Laplace's nebula or to let off the cosmic firework of Lemaître's imagination? I confess an unwillingness to bring God in this way upon the scene. The circumstances which thus seem to demand his presence are too remote and too obscure to afford me any true satisfaction. Men have thought to find God at the special creation of their own species, or active when mind or life first appeared on earth. They have made him God of the gaps in human knowledge. To me the God of the trigger is as little satisfying as the God of the gaps. It is because throughout the physical Universe I find thought and plan and power that behind it I see God as the creator.

During World War II, the German theologian and martyr Dietrich Bonhoeffer expressed the concept in similar terms in letters he wrote while in a Nazi prison. Bonhoeffer wrote, for example:

how wrong it is to use God as a stop-gap for the incompleteness of our knowledge. If in fact the frontiers of knowledge are being pushed further and further back (and that is bound to be the case), then God is being pushed back with them, and is therefore continually in retreat. We are to find God in what we know, not in what we don't know.

In his 1955 book Science and Christian Belief Charles Alfred Coulson (1910−1974) wrote:

There is no 'God of the gaps' to take over at those strategic places where science fails; and the reason is that gaps of this sort have the unpreventable habit of shrinking.

and

Either God is in the whole of Nature, with no gaps, or He's not there at all.

Coulson was a mathematics professor at Oxford University as well as a Methodist church leader, often appearing in the religious programs of British Broadcasting Corporation. His book got national attention, was reissued as a paperback, and was reprinted several times, most recently in 1971. It is claimed that the actual phrase 'God of the gaps' was invented by Coulson.

The term was then used in a 1971 book and a 1978 article, by Richard Bube. He articulated the concept in greater detail in Man come of Age: Bonhoeffer's Response to the God-of-the-Gaps (1978). Bube attributed modern crises in religious faith in part to the inexorable shrinking of the God-of-the-gaps as scientific knowledge progressed. As humans progressively increased their understanding of nature, the previous "realm" of God seemed to many persons and religions to be getting smaller and smaller by comparison. Bube maintained that Darwin's Origin of Species was the "death knell" of the God-of-the-gaps. Bube also maintained that the God-of-the-gaps was not the same as the God of the Bible (that is, he was not making an argument against God per se, but rather asserting there was a fundamental problem with the perception of God as existing in the gaps of present-day knowledge).

General usage

The term "God of the gaps" is sometimes used in describing the incremental retreat of religious explanations of physical phenomena in the face of increasingly comprehensive scientific explanations for those phenomena. Dorothy Dinnerstein includes psychological explanations for developmental distortions leading to a person believing in a deity, particularly a male deity.

R. Laird Harris writes of the physical science aspect of this:

The expression, "God of the Gaps," contains a real truth. It is erroneous if it is taken to mean that God is not immanent in natural law but is only to be observed in mysteries unexplained by law. No significant Christian group has believed this view. It is true, however, if it be taken to emphasize that God is not only immanent in natural law but also is active in the numerous phenomena associated with the supernatural and the spiritual. There are gaps in a physical-chemical explanation of this world, and there always will be. Because science has learned many marvelous secrets of nature, it cannot be concluded that it can explain all phenomena. Meaning, soul, spirits, and life are subjects incapable of physical-chemical explanation or formation.

Usage in referring to a type of argument

The term God-of-the-gaps fallacy can refer to a position that assumes an act of God as the explanation for an unknown phenomenon, which according to the users of the term, is a variant of an argument from ignorance fallacy. Such an argument is sometimes reduced to the following form:

• There is a gap in understanding of some aspect of the natural world.
• Therefore, the cause must be supernatural.

One example of such an argument, which uses God as an explanation of one of the current gaps in biological science, is as follows: "Because current science can't figure out exactly how life started, it must be God who caused life to start." Critics of intelligent design creationism, for example, have accused proponents of using this basic type of argument.

God-of-the-gaps arguments have been discouraged by some theologians who assert that such arguments tend to relegate God to the leftovers of science: as scientific knowledge increases, the dominion of God decreases.

Criticism

The term was invented as a criticism of people who perceive that God only acts in the gaps, and who restrict God's activity to such "gaps". It has also been argued that the God-of-the-gaps view is predicated on the assumption that any event which can be explained by science automatically excludes God; that if God did not do something via direct action, that he had no role in it at all. The "God of the gaps" argument, as traditionally advanced by scholarly Christians, was intended as a criticism against weak or tenuous faith, not as a statement against theism or belief in God.

According to John Habgood in The Westminster Dictionary of Christian Theology, the phrase is generally derogatory, and is inherently a direct criticism of a tendency to postulate acts of God to explain phenomena for which science has not (at least at present) given a satisfactory account. Habgood also states:

It is theologically more satisfactory to look for evidence of God's actions within natural processes rather than apart from them, in much the same way that the meaning of a book transcends, but is not independent of, the paper and ink of which it is comprised.

It has been criticized by both theologians and scientists, who say that it is a logical fallacy to base belief in God on gaps in scientific knowledge. In this vein, Richard Dawkins, an atheist, dedicates a chapter of his book The God Delusion to criticism of the God-of-the-gaps argument. He noted that:

Creationists eagerly seek a gap in present-day knowledge or understanding. If an apparent gap is found, it is assumed that God, by default, must fill it. What worries thoughtful theologians such as Bonhoeffer is that gaps shrink as science advances, and God is threatened with eventually having nothing to do and nowhere to hide.

Social anxiety disorder

From Wikipedia, the free encyclopedia

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

 

Social anxiety disorder
Other names	Social phobia
Specialty	Psychiatry, clinical psychology
Symptoms	Social isolation, hypervigilance, self-consciousness
Usual onset	Typically during childhood or adolescence
Risk factors	Genetic factors, preexisting mental disorder
Treatment	Psychotherapy, medication
Medication	SSRIs, venlafaxine, phenelzine, propranolol (for performance anxiety)
Frequency	7% (2003) to 36% (2020)

Social anxiety disorder is distinct from the personality traits of introversion and shyness.

Social anxiety disorder (SAD), previously known as social phobia, is an anxiety disorder characterized by high levels of anxiety and self-consciousness in social situations, resulting in significant distress and an impaired ability to function in daily life. The defining feature of social anxiety disorder is a persistent fear of negative or positive evaluation by others. These fears can be triggered by perceived or actual scrutiny from others. Recent data suggest the prevalence of social anxiety disorder is rising, particularly among young people.

Physical symptoms often include excessive blushing, excessive sweating, trembling, palpitations, muscle tension, shortness of breath, and nausea. Panic attacks can also occur under intense fear and discomfort. Some affected individuals may use alcohol or other drugs to reduce fears and inhibitions at social events. It is common for socially anxious individuals to self-medicate in this fashion, especially if they are undiagnosed or untreated. This results in a heightened risk of alcohol use disorder, eating disorders, or other substance use disorders among sufferers. According to ICD-11 guidelines, an individual meets the criteria for social anxiety disorder if they experience persistent symptoms for at least several months, resulting in significant distress and impairment in personal, family, social, educational, occupational, or other important areas of functioning.

The first line of treatment for social anxiety disorder is cognitive behavioral therapy (CBT) with or without medication. CBT is most effective when delivered individually, though it can be offered in a group format. The cognitive and behavioral components seek to change thought patterns and physical reactions to anxiety-inducing situations. Metacognitive therapy and acceptance and commitment therapy are alternative options with efficacy at least as high as CBT.

The attention given to social anxiety disorder has significantly increased since 1999, with the approval and marketing of drugs for its treatment. Approved medications include the selective serotonin reuptake inhibitors (SSRIs) paroxetine, sertraline, and fluvoxamine, the serotonin–norepinephrine reuptake inhibitor (SNRI) venlafaxine, and the monoamine oxidase inhibitor (MAOI) phenelzine. Propranolol, a beta blocker, is sometimes used off-label for performance anxiety.

Signs and symptoms

The 11th revision of the International Classification of Diseases (ICD-11) classifies social anxiety as an anxiety or fear-related disorder.

Cognitive aspects

In cognitive models of social anxiety disorder, those with social anxiety disorder experience dread over how they will present to others. They may feel overly self-conscious, pay excessive attention to themselves, or have high performance standards for themselves. According to the social psychology theory of self-presentation, an affected person attempts to create a well-mannered impression towards others but believes they are unable to do so. Many times, before the potentially anxiety-provoking social situation, they may deliberately review what could go wrong and how to deal with each unexpected case. After the event, they may have the perception that they performed unsatisfactorily. Consequently, they will perceive anything that may have possibly been abnormal as embarrassing. These thoughts may extend for weeks or longer. Cognitive distortions are a hallmark and are learned about in CBT (cognitive-behavioral therapy). Thoughts are often self-defeating and inaccurate. Those with social phobia tend to interpret neutral or ambiguous conversations with a negative outlook and many studies suggest that socially anxious individuals remember more negative memories than those less distressed.

Behavioral aspects

Social anxiety disorder is a persistent fear of one or more situations in which the person is exposed to possible scrutiny by others and fears that they may do something or act in a way that will be humiliating or embarrassing. It exceeds normal "shyness" as it leads to excessive social avoidance and substantial social or occupational impairment.

Those who have social anxiety disorder fear being judged by others in society. In particular, individuals with social anxiety are nervous in the presence of people with authority and feel uncomfortable during physical examinations. People who have this disorder may behave a certain way or say something and then feel embarrassed or humiliated after. As a result, they often choose to isolate themselves from society to avoid such situations. They may also feel uncomfortable meeting people they do not know and act distant when they are with large groups of people. In some cases, they may show evidence of this disorder by avoiding eye contact, or blushing when someone is talking to them.

Physiological aspects

Physiological effects may include excessive sweating, nausea, difficulty breathing, shaking, palpitations, and increased heart rate.

Social aspects

People with SAD avoid situations that most people consider normal. People with SAD avoid all or most social situations and hide from others, which can affect their personal relationships. Social phobia can completely remove people from social situations due to the irrational fear of these situations. People with SAD may be addicted to social media networks, have sleep deprivation, and feel good when they avoid human interactions. SAD can also lead to low self-esteem, negative thoughts, major depressive disorder, sensitivity to criticism, and poor social skills that do not improve. People with SAD experience anxiety in a variety of social situations, from important, meaningful encounters to common situations. These people may feel more nervous in job interviews, dates, interactions with authority, or at work and school.

Comorbidity

SAD shows a high degree of co-occurrence with psychiatric disorders. In fact, a population-based study found that 66% of those with SAD had one or more additional mental health disorders. SAD often occurs alongside low self-esteem and most commonly clinical depression. Clinical depression is 1.49 to 3.5 times more likely to occur in those with SAD. Research also indicates that the presence of certain social fears (e.g., avoidance of participating in small groups, avoidance of going to a party) are more likely to trigger comorbid depressive symptoms than other social fears.

Anxiety disorders other than SAD are also common in people with SAD, in particular generalized anxiety disorder. Avoidant personality disorder is likewise highly correlated with SAD, with comorbidity rates ranging from 25% to 89%.

To try to reduce their anxiety and alleviate depression, people with social phobia may use alcohol or other drugs, which can lead to substance use disorders. It is estimated that one-fifth of people with social anxiety disorder also have alcohol use disorder. However, some research suggests SAD is unrelated to, or even protective against alcohol-related problems. Those who have both alcohol use disorder and social anxiety disorder are more likely to avoid group-based treatments and to relapse compared to people who do not have this combination.

Causes

Research into the causes of social anxiety and social phobia is wide-ranging, encompassing multiple perspectives from neuroscience to sociology. Scientists have yet to pinpoint the exact causes. Studies suggest that genetics can play a part in combination with environmental factors. Social phobia is not caused by other mental disorders or substance use. Generally, social anxiety begins at a specific point in an individual's life. This will develop over time as the person struggles to recover. Eventually, mild social awkwardness can develop into symptoms of social anxiety or phobia. Passive social media usage may cause social anxiety in some people.

Genetics

It has been shown that there is a two to a threefold greater risk of having social phobia if a first-degree relative also has the disorder. This could be due to genetics and/or due to children acquiring social fears and avoidance through processes of observational learning or parental psychosocial education. Studies of identical twins brought up (via adoption) in different families have indicated that, if one twin developed social anxiety disorder, then the other was between 30 percent and 50 percent more likely than average to also develop the disorder. To some extent, this "heritability" may not be specific – for example, studies have found that if a parent has any kind of anxiety disorder or clinical depression, then a child is somewhat more likely to develop an anxiety disorder or social phobia. Studies suggest that parents of those with social anxiety disorder tend to be more socially isolated themselves, and shyness in adoptive parents is significantly correlated with shyness in adopted children.

Growing up with overprotective and hypercritical parents has also been associated with social anxiety disorder. Adolescents who were rated as having an insecure (anxious-ambivalent) attachment with their mother as infants were twice as likely to develop anxiety disorders by late adolescence, including social phobia.

A related line of research has investigated 'behavioural inhibition' in infants – early signs of an inhibited and introspective or fearful nature. Studies have shown that around 10–15 percent of individuals show this early temperament, which appears to be partly due to genetics. Some continue to show this trait into adolescence and adulthood and appear to be more likely to develop a social anxiety disorder.

Social experiences

A previous negative social experience can be a trigger to social phobia, perhaps particularly for individuals high in "interpersonal sensitivity". For around half of those diagnosed with social anxiety disorder, a specific traumatic or humiliating social event appears to be associated with the onset or worsening of the disorder; this kind of event appears to be particularly related to specific social phobia, for example, regarding public speaking. As well as direct experiences, observing or hearing about the socially negative experiences of others (e.g. a faux pas committed by someone), or verbal warnings of social problems and dangers, may also make the development of a social anxiety disorder more likely. Social anxiety disorder may be caused by the longer-term effects of not fitting in, or being bullied, rejected, or ignored. Shy adolescents or avoidant adults have emphasized unpleasant experiences with peers or childhood bullying or harassment. In one study, popularity was found to be negatively correlated with social anxiety, and children who were neglected by their peers reported higher social anxiety and fear of negative evaluation than other categories of children. Socially phobic children appear less likely to receive positive reactions from peers, and anxious or inhibited children may isolate themselves.

Parental influences

Different parenting styles can also contribute to the development of social anxiety disorder. The common negative parenting styles, such as overcontrol and criticism can be detrimental for a child to be able to overcome difficult situations. More aggressive and harsh parenting styles that include both verbal abuse and physical punishment are linked with an insecure attachment and risk for social anxiety disorder. On the contrary, positive parenting that fosters a more supportive and warm environment for the child is correlated to a decreased risk of developing this disorder. On the biological level as well, there is strong evidence that states how children from parents with social anxiety disorder have significantly increased risk to the disorder.

Cultural influences

Cultural factors that have been related to social anxiety disorder include a society's attitude towards shyness and avoidance, affecting the ability to form relationships or access employment or education, and shame. One study found that the effects of parenting are different depending on the culture: American children appear more likely to develop social anxiety disorder if their parents emphasize the importance of others' opinions and use shame as a disciplinary strategy, but this association was not found for Chinese/Chinese-American children. In China, research has indicated that shy-inhibited children are more accepted than their peers and more likely to be considered for leadership and considered competent, in contrast to the findings in Western countries. Purely demographic variables may also play a role.

Problems in developing social skills, or 'social fluency', may be a cause of some social anxiety disorder, through either inability or lack of confidence to interact socially and gain positive reactions and acceptance from others. The studies have been mixed, however, with some studies not finding significant problems in social skills while others have. What does seem clear is that the socially anxious perceive their own social skills to be low. It may be that the increasing need for sophisticated social skills in forming relationships or careers, and an emphasis on assertiveness and competitiveness, is making social anxiety problems more common, at least among the 'middle classes'. An interpersonal or media emphasis on 'normal' or 'attractive' personal characteristics has also been argued to fuel perfectionism and feelings of inferiority or insecurity regarding negative evaluation from others. The need for social acceptance or social standing has been elaborated in other lines of research relating to social anxiety.

Substance-induced

While alcohol initially relieves social phobia, excessive alcohol misuse can worsen social phobia symptoms and cause panic disorder to develop or worsen during alcohol intoxication and especially during alcohol withdrawal syndrome. This effect is not unique to alcohol but can also occur with long-term use of drugs that have a similar mechanism of action to alcohol such as the benzodiazepines which are sometimes prescribed as tranquillisers. Benzodiazepines possess anti-anxiety properties and can be useful for the short-term treatment of severe anxiety. Like the anticonvulsants, they tend to be mild and well-tolerated, although there is a risk of habit-forming. Benzodiazepines are usually administered orally for the treatment of anxiety; however, occasionally lorazepam or diazepam may be given intravenously for the treatment of panic attacks.

The World Council of Anxiety does not recommend benzodiazepines for the long-term treatment of anxiety due to a range of problems associated with long-term use including tolerance, psychomotor impairment, cognitive and memory impairments, physical dependence and a benzodiazepine withdrawal syndrome upon discontinuation of benzodiazepines. Despite increasing focus on the use of antidepressants and other agents for the treatment of anxiety, benzodiazepines have remained a mainstay of anxiolytic pharmacotherapy due to their robust efficacy, rapid onset of therapeutic effect, and generally favorable side effect profile. Treatment patterns for psychotropic drugs appear to have remained stable over the past decade, with benzodiazepines being the most commonly used medication for panic disorder.

Many people who are addicted to alcohol or prescribed benzodiazepines when it is explained to them they have a choice between ongoing ill mental health or quitting and recovering from their symptoms decide on quitting alcohol or their benzodiazepines. Symptoms may temporarily worsen however, during alcohol withdrawal or benzodiazepine withdrawal.

Psychological factors

Research has indicated the role of 'core' or 'unconditional' negative beliefs (e.g. "I am inept") and 'conditional' beliefs nearer to the surface (e.g. "If I show myself, I will be rejected"). They are thought to develop based on personality and adverse experiences and to be activated when the person feels under threat. Recent research has also highlighted that conditional beliefs may also be at play (e.g., "If people see I'm anxious, they'll think that I'm weak").

A secondary factor is self-concealment which involves concealing the expression of one's anxiety or its underlying beliefs. One line of work has focused more specifically on the key role of self-presentational concerns. The resulting anxiety states are seen as interfering with social performance and the ability to concentrate on interaction, which in turn creates more social problems, which strengthens the negative schema. Also highlighted has been a high focus on and worry about anxiety symptoms themselves and how they might appear to others. A similar model emphasizes the development of a distorted mental representation of the self and overestimates of the likelihood and consequences of negative evaluation, and of the performance standards that others have. Such cognitive-behavioral models consider the role of negatively biased memories of the past and the processes of rumination after an event (also known as post-event processing), and fearful anticipation before it.

Studies have also highlighted the role of subtle avoidance and defensive factors, and shown how attempts to avoid feared negative evaluations or use of "safety behaviors" can make social interaction more difficult and the anxiety worse in the long run. This work has been influential in the development of cognitive behavioral therapy for social anxiety disorder, which has been shown to have efficacy.

Diagnosis

ICD-10 defines social phobia as fear of scrutiny by other people leading to avoidance of social situations. The anxiety symptoms may present as a complaint of blushing, hand tremor, nausea, or urgency of urination. Symptoms may progress to panic attacks.

Standardized rating scales such as the Social Phobia Inventory, the SPAI-B, Liebowitz Social Anxiety Scale, and the Social Interaction Anxiety Scale can be used to screen for social anxiety disorder and measure the severity of anxiety.

SAD is categorized into two main types:

Generalized Social Anxiety Disorder: Affects nearly all aspects of a person's social life, making everyday interactions extremely stressful.

Specific (Performance-Based) Social Anxiety Disorder: Individuals feel extreme nervousness in specific situations, like giving a speech or performing on stage.

DSM-5 diagnostic criteria

Although the DSM defines social anxiety disorder as an intense fear or anxiety of social situations, it makes clear a distinction to separate social anxiety disorder from simply social anxiety or social fear.

Social situations

• The anxiety must occur in a social setting under circumstances that are conducive to the possible scrutiny of others.
  • For children, the DSM-5 notes that the anxiety must be in a setting with other children and not with adults.
• Social situations induce and are avoided due to the intense feelings of anxiety or fear.
• Social situations must be the cause of anxiety or fear.

The DSM-5 notes that for social anxiety disorder, the fear must be attributed or correlated to social situations and not another condition.

Anxiety

• The fear or anxiety is out of reasonable proportion to the context of the situation.
• The fear or anxiety affects an individual for an abnormally long time – 6 months or more.
• There is a significant negative impact on an individual's life due to fear or anxiety in a social, professional, or other life event.

To determine a reasonable proportion, an individual's sociocultural situation is assessed. Different cultures have individual criteria for determining a reasonable fear to a learned behavior for a particular social situation. Criteria for anxiety assess whether a fear has a significant impact on social, professional, or other life function.

Other causes

• Condition is not a psychological effect induced by a substance (e.g., drugs, alcohol or other medication).
• Condition is not a psychological effect induced by another medical condition.
• Condition is not a psychological effect induced by another mental disorder.

Performance

• Fear is limited to only public speaking or public performing

The DSM-5 notes that performance only type of social anxiety disorder (a subset specific version of this disorder) often affects individual's professional lives of those involved with public speaking or public performing. These fears can arise in settings other than just an individual's professional life but are limited to only public social performance situations.

Differential diagnosis

The DSM-IV criteria stated that an individual cannot receive a diagnosis of social anxiety disorder if existing symptoms are better diagnosed by one of the autism spectrum disorders, such as autism or Asperger syndrome.

Social anxiety disorder is often linked to bipolar disorder and attention deficit hyperactivity disorder (ADHD), leading to an assumption of a shared cyclothymic-anxious-sensitive disposition. The co-occurrence of ADHD and social phobia is common, especially when cognitive disengagement syndrome is present.

Treatment

Psychotherapies

The first-line treatment for social anxiety disorder is cognitive behavioral therapy (CBT), with medications such as selective serotonin reuptake inhibitors (SSRIs) sometimes used in combination with CBT. The purpose of CBT is to help individuals address unhelpful thinking patterns and behaviors that contribute to emotional distress. Self-help based on principles of CBT is an alternative option for those unable to access in-person mental health services.

Another treatment with a growing evidence base for social anxiety disorder is metacognitive therapy (MCT), which targets the underlying processes that maintain the disorder. More specifically, the aim of MCT is to identify and modify dysfunctional metacognitive beliefs that contribute to and sustain a perseverative style of thinking known as the cognitive attentional syndrome (CAS), which comprises worry and rumination, threat monitoring, self-focused attention, and maladaptive coping behaviors. Some studies have suggested that MCT may be superior to CBT for social anxiety disorder.

There is emerging evidence for the use of acceptance and commitment therapy (ACT) in the treatment of social anxiety disorder. ACT is considered an offshoot of traditional CBT and emphasizes accepting unpleasant symptoms rather than fighting against them, as well as psychological flexibility – the ability to adapt to changing situational demands, to shift one's perspective, and to balance competing desires. ACT may be useful as an alternative treatment for the disorder in situations where CBT is ineffective or refused.

Some studies have suggested social skills training can help with social anxiety.  Examples of social skills that may be modified for social anxiety disorder include initiating conversations, establishing friendships, interacting with members of the preferred sex, constructing a speech, and assertiveness skills. However, it is not clear whether specific social skills techniques and training are required, rather than just support with general social functioning and exposure to social situations.

Social anxiety disorder may predict subsequent development of other psychiatric disorders, such as depression. Social anxiety disorder remains under-recognized in primary care practice, with people presenting for treatment only after the onset of complications, such as clinical depression or substance use disorders.

Medications

See also: List of investigational social anxiety disorder drugs

A comparison of the treatment effects on social anxiety disorder showed that using a medication is faster, while CBT is longer-lasting. Using antidepressants for treating social anxiety disorder is typically not as effective as using CBT.

SSRIs & SNRIs

Selective serotonin reuptake inhibitors (SSRIs), a class of antidepressants, are the first choice of medication for generalized social phobia but a second-line treatment. Compared to older forms of medication, there is less risk of tolerability and drug dependency associated with SSRIs. Paroxetine and paroxetine CR, sertraline, venlafaxine XR and fluvoxamine CR (Luvox CR) are all approved and effective for treating social anxiety disorder. The effectiveness of medications other than paroxetine is small.

General side effects are common during the first weeks while the body adjusts to SSRI drugs. Symptoms may include headaches, nausea, insomnia and changes in sexual behavior.^[112]

Other drugs

Other prescription drugs are also used, if other methods are not effective. Before the introduction of SSRIs, monoamine oxidase inhibitors (MAOIs) such as phenelzine were frequently used in the treatment of social anxiety. Evidence continues to indicate that MAOIs are effective in the treatment and management of social anxiety disorder and they are still used, but generally only as a last resort medication, owing to concerns about dietary restrictions, possible adverse drug interactions and a recommendation of multiple doses per day. RIMAs have been found to be less efficacious for social anxiety disorder than irreversible MAOIs like phenelzine. Serotonergic anxiolytic buspirone may also be used.

Propranolol, a beta blocker commonly used to control high blood pressure, is used for performance anxiety specifically.

Pregablin at high doses appears to have modest efficacy. Gabapetin has been investigated for social anxiety disorder in preliminary long-term studies.

Anticonvulsants, tricyclic antidepressants, antipsychotic drugs, and St. John's wort should not be used. Guidelines vary regarding whether benzodiazepines should be used.

Epidemiology

Country	Prevalence
United States	2–7%
England	0.4% (children)
Scotland	1.8% (children)
Wales	0.6% (children)
Australia	1–2.7%
Brazil	4.7–7.9%
India	12.8% (adolescents)
Iran	0.8%
Israel	4.5%
Nigeria	9.4% (university students)
Sweden	15.6% (university students)
Turkey	9.6% (university students)
Poland	7–9% (2002)
Taiwan	7% children (2002~2008)

Social anxiety disorder is known to appear at an early age in most cases. Fifty percent of those who develop this disorder have developed it by the age of 11, and 80% have developed it by age 20. This early age of onset may lead to people with social anxiety disorder being particularly vulnerable to depressive illnesses, substance use, and other psychological conflicts.

The National Comorbidity Survey of over 8,000 American correspondents in 1994 revealed 12-month and lifetime prevalence rates of 7.9 percent and 13.3 percent, respectively; this makes it the third most prevalent psychiatric disorder after depression and alcohol use disorder, and the most common of the anxiety disorders. According to US epidemiological data from the National Institute of Mental Health, social phobia affects 15 million adult Americans in any given year. Estimates vary within 2 percent and 7 percent of the US adult population.

The mean onset of social phobia is 10 to 13 years. Onset after age 25 is rare and is typically preceded by panic disorder or major depression. Social anxiety disorder occurs more often in females than males. The prevalence of social phobia appears to be increasing among white, married, and well-educated individuals. As a group, those with generalized social phobia are less likely to graduate from high school and are more likely to rely on government financial assistance or have poverty-level salaries. Surveys carried out in 2002 show the youth of England, Scotland, and Wales have a prevalence rate of 0.4 percent, 1.8 percent, and 0.6 percent, respectively. In Canada, the prevalence of self-reported social anxiety for Nova Scotians older than 14 years was 4.2 percent in June 2004 with women (4.6 percent) reporting more than men (3.8 percent). In Australia, social phobia is the 8th and 5th leading disease or illness for males and females between 15 and 24 years of age as of 2003. Because of the difficulty in separating social phobia from poor social skills or shyness, some studies have a large range of prevalence. The table also shows higher prevalence in Sweden.

History

Literary descriptions of shyness can be traced back to the days of Hippocrates around 400 BC. Hippocrates described someone who "through bashfulness, suspicion, and timorousness, will not be seen abroad; loves darkness as life and cannot endure the light or to sit in lightsome places; his hat still in his eyes, he will neither see, nor be seen by his good will. He dare not come in company for fear he should be misused, disgraced, overshoot himself in gesture or speeches, or be sick; he thinks every man observes him".

The first mention of the psychiatric term "social phobia" (phobie des situations sociales) was made in the early 1900s. Psychologists used the term "social neurosis" to describe extremely shy patients in the 1930s. After extensive work by Joseph Wolpe on systematic desensitization, research on phobias and their treatment grew. The idea that social phobia was a separate entity from other phobias came from the British psychiatrist Isaac Marks in the 1960s. This was accepted by the American Psychiatric Association and was first officially included in the third edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III).

Research

Although social anxiety disorder has been under study for decades, the underlying neurobiology is not well understood. Neurotransmitters under research include serotonin, dopamine, and glutamate. Neuroimaging technologies are in use to clarify brain regions involved. The amygdala is a primary brain structure involved in SAD, as explored in imaging studies.

Parenting that is intrusive or controlling and stressful life events may increase the risk for SAD development during childhood, extending into adult years. Genetic factors may have a role, although genetic biomarkers are not specifically identified.