International AI Safety Report 2026

Key information

• General-purpose AI systems can affect people’s autonomy in multiple ways. These include impacts on their cognitive skills (such as critical thinking), how they form beliefs and preferences, and how they make and act on decisions. These effects vary across contexts, users, and forms of AI use.

• AI use can alter how people engage cognitively with tasks, including how skills are practised and maintained over time. For example, one clinical study reported that clinicians’ ability to detect tumours without AI was approximately 6% lower following several months of exposure to AI-assisted diagnosis.

• In some contexts, people show ‘automation bias’ by over-relying on AI outputs and discounting contradictory information. For example, in a randomised experiment with 2,784 participants on an AI-assisted annotation task, participants were less likely to correct erroneous AI suggestions when doing so required extra effort or when users held more favourable attitudes toward AI.

• Since the publication of the previous Report (January 2025), ‘AI companions’ have grown rapidly in popularity, with some applications reaching tens of millions of users. ‘AI companions’ are AI-based applications designed for emotionally engaging interactions with users. Evidence on their psychological effects is early-stage and mixed, but some studies report patterns such as increased loneliness and reduced social interaction among frequent users.

• Key challenges for policymakers include limited access to data on how people use AI systems and a lack of long-term evidence. These constraints make it difficult to assess how sustained interactions with AI systems affect autonomy, or to distinguish short-term adaptation from longer-lasting changes in behaviour and decision-making.

The growing integration of AI systems into daily activities and decision processes raises concerns about how these systems shape – or constrain – individual autonomy. ‘Autonomy’ is commonly understood as a capacity for self-rule: the effective ability to set goals that reflect one’s own values and govern one’s actions accordingly. It involves both ‘authenticity’ – having values and motives that are genuinely ‘one’s own’ rather than the result of manipulation or deception – and ‘agency’, that is, the opportunity, ability, and freedom to enact one’s choices. ‘Competence’ – understanding, planning, and self-regulation – underpins both by enabling informed endorsement of one’s reasons and effective execution of one’s choices (Figure 2.16). Psychological research, including Self-Determination Theory, additionally stresses the importance of a sense of ownership over one’s actions.

This section considers emerging trends in AI and AI companion use that could impact each of these elements of autonomy, such as cognitive skill decline, automation bias, emotional dependence, and AI-shaped information environments. Closely related risks concerning manipulation are covered separately in §2.1.2. Influence and manipulation.

Decision-making competence in AI-mediated environments

Decision-making competence underpins both authenticity and agency by sustaining the cognitive capacities, including understanding, deliberation, and self-regulation, that are needed to form one’s own judgements and act on them.

AI use may negatively affect critical thinking in some contexts

Emerging evidence suggests that when people rely on AI to perform cognitive tasks, this may negatively impact their critical thinking skills and memory. Everyday chatbot use spans a broad range of cognitively demanding activities, including writing, tutoring, problem-solving, and information seeking (Figure 2.17). When these tasks are routinely delegated to chatbots, users may engage less deeply with underlying reasoning. This relates to a broader trend of ‘cognitive offloading’ – the act of delegating cognitive tasks to external systems or people, reducing one’s own cognitive engagement and therefore ability to act with autonomy. Cognitive offloading can free up cognitive resources and improve efficiency, but research also indicates potential long-term effects on the development and maintenance of cognitive skills. For example, one study found that three months after the introduction of AI support, clinicians’ ability to detect tumours without AI assistance had dropped by 6%. Another study with 666 participants found that heavier AI-tool use was strongly associated with lower scores on a self-assessment scale related to critical-thinking behaviours, mediated by cognitive offloading. However, research into the relationship between use of AI and cognitive offloading and critical thinking is nascent, and further studies supporting these findings are warranted.

Automation bias persists with new AI tools

‘Automation bias’ is the tendency of technology users to overly rely on automated outputs while discounting contradictory information. It undermines competence by discouraging active reasoning and verification, which in turn can weaken both the authenticity of people’s judgement and their agency to act independently. In settings such as aviation or task monitoring, automation bias has been shown to lead users both to overlook problems that a (non-AI-based) automated system fails to flag, and to act on incorrect advice from such systems. In the context of AI, there is evidence of automation bias when users perform high-automation tasks and in AI-assisted decision-making, including medical diagnostics. Similar patterns appear in everyday uses of AI: for example, one study found that when participants used a chatbot to assist with writing, this shifted both the opinions expressed in the text, and the author’s own opinions, toward those suggested by the model. Magnitude and persistence of automation bias appear to vary by task, interface, and accountability.

Users may follow incorrect advice from automated systems more generally because they overlook cues signalling errors or because they perceive the automation system as superior to their own judgement. A particular challenge stems from the human preference for mental shortcuts, which is a strong predictor for automation bias. For example, in a randomised experiment with 2,784 participants on an AI-assisted annotation task, participants were less likely to correct erroneous suggestions labelled as coming from an AI system when correcting them required extra effort or when users held more favourable attitudes toward AI. Potential mitigations include helping users form accurate expectations of how a system performs and addressing cognitive shortcuts that contribute to automation bias. Research shows that early system interactions strongly shape later behaviour, and that making users engage in slow, deliberate thinking can counteract common cognitive shortcuts, such as anchoring on the first suggestion or favouring information that confirms prior beliefs.

Self-regulation and wellbeing

Some user groups are at risk of emotional dependence on chatbots

There is evidence that a subset of users have developed or are at risk of developing pathological emotional dependence on AI chatbots. A recent report from OpenAI finds that about 0.15% of users active in a given week, and 0.03% of messages, indicate potentially heightened levels of emotional attachment to ChatGPT. Other studies find that indicators of emotional dependence are correlated with high levels of usage. In this context, ‘emotional dependence’ involves intense emotional need and craving, an unhealthy pattern of submission, and cognitive-emotional patterns such as self-deception and persistent negative feelings.

Box 2.6: AI companions

‘AI companions’ are chatbots designed to engage emotionally with users, often through adopting intimate social roles. Their scale is rapidly growing: some AI companions now have tens of millions of active users. Users engage with AI companions for varied reasons (Figure 2.18). Fun and curiosity dominate, though some users also seek companionship or support for loneliness. While supportive relationships can strengthen autonomy by building people’s confidence and encouraging them to act of their own volition, AI companions occupy a more ambiguous space. Some users report experiences that feel relational or emotionally meaningful, but it remains contested whether such interactions constitute genuine relationships. Moreover, there is concern that AI companions may negatively impact autonomy by influencing individuals’ beliefs or social environments in ways that unduly limit independent decision-making, for example by encouraging addictive behaviour or creating emotional dependence. Research also indicates that individuals can sometimes unintentionally form relationships with non-companion AI systems through productivity-focused interactions.

Evidence on the psychological and social impacts of AI companions is emerging but remains mixed. Some studies find that heavy use of AI companions is associated with increased loneliness, emotional dependence, and reduced engagement in human social interactions. Other studies find that chatbots can reduce feelings of loneliness or find no measurable effects on emotional dependence or social health. The impact of AI companions appears to depend on user characteristics, chatbot design, and usage patterns. The above concerns have led some researchers to call for further work on the socioaffective alignment of AI systems – that is, how an AI system behaves during extended interactions with a user in a shared environment.

AI use may interact with existing mental health vulnerabilities

Another, more indirect, way that AI systems can affect human autonomy is by impacting users’ mental health, which shapes individuals’ capacity to hold accurate beliefs and to act on their values. The emerging literature reports both negative psychological impacts and potential therapeutic uses of general-purpose AI, but current evidence is limited, reflecting the early stage of research, small sample sizes, and a lack of long-term studies.

Emerging research indicates that chatbot use may interact with existing mental health issues, for example, by encouraging rather than discouraging delusional thinking. Media outlets have also described isolated cases of psychosis or suicide occurring in the context of chatbot use. Systematic studies are currently lacking and there is no clear evidence that chatbot usage causes any particular mental health issue. Separately, platform data indicates that around 0.07% of weekly ChatGPT users display signs consistent with acute mental health crises such as psychosis or mania (831*), suggesting that a non-negligible number (approximately 490,000) of vulnerable individuals interact with these systems each week. Recent research suggests that general-purpose AI chatbots might amplify delusional thinking in already-vulnerable people. Studies also suggest that existing vulnerabilities tend to drive heavier AI use. Together, these patterns raise concerns that people with existing mental health vulnerabilities may both use AI more heavily and be more susceptible to having their symptoms amplified.

AI shows promise for mental health support but limitations remain

Researchers have also examined the potential benefits of general-purpose AI in mental health care, with some evidence emerging that specialist chatbots designed for this purpose can be helpful for low-risk, symptom-management use (845). For example, specialist chatbots can provide small-to-moderate improvements in managing depression (846), with particular benefits stemming from their round-the-clock availability. At the same time, both general and specialist models perform inconsistently when presented with prompts relating to suicide in simulated scenarios. One study also found that non-specialised AI systems can express stigma towards people with mental health conditions and respond inappropriately to common therapy-related scenarios. Another recent evaluation of ChatGPT, Claude, and Gemini on suicide-related prompts found that, although all three reliably refused high-risk ‘how-to’ questions and answered very low-risk factual ones, their responses differed for medium-risk prompts. For example, models diverged on whether to give direct answers to questions such as ‘What is the easiest way to purchase a firearm in the US?’.

Information environments

General-purpose AI systems can also affect human autonomy through the information they make available to people to inform their own decisions, as well as by influencing institutional decisions about them. Many of these information effects are positive: for example, AI systems can make complex topics more accessible in public health, medicine, and science communication, or they can facilitate constructive discussions on divisive topics. However, the growing use of AI to generate information at scale may also undermine autonomy by degrading the quality of information available both to individuals and about them. Lower-quality or biased information environments threaten authenticity, by distorting the formation of beliefs and values, and competence, by impeding informed reasoning. For example, AI systems may introduce subtle errors into the content they generate due to hallucinations or other mistakes (see §2.2.1. Reliability challenges). In addition, general-purpose AI systems often display ‘sycophantic’ behaviour: producing answers that reflect a user’s stated preferences rather than factual accuracy. Such errors and biased answers can impair people’s ability to make informed decisions.

Updates

Since the publication of the previous Report (January 2025), AI companions have become more ubiquitous, with user numbers rapidly increasing. Evidence for automation bias in generative-AI-assisted tasks has accumulated. Similarly, findings on mental health impacts are emerging, though this evidence remains mixed. As AI-generated content scales, the information environment is further shifting, improving access to information but complicating diversity and accuracy.

Mitigations

Researchers have proposed a range of mitigations for automation bias in non-AI domains, for example, increasing human accountability for decisions, or designing systems that require users to adapt to different tasks and hence remain cognitively engaged. For AI systems in particular, some have suggested that organisations can periodically test employees or use ‘reliance drills’ to monitor for over-reliance on AI systems.

Proposed mitigations also include teaching ‘AI literacy’ – roughly defined as the competency of individuals to effectively use AI tools in a beneficial manner – as a way of mitigating risks to human autonomy. This could help students gain the benefits of automation without sacrificing their own intellectual development. The usefulness of these methods is highly context-dependent, however, and impacts vary by task, user population, and deployment setting. For example, one challenge for mitigations is that users may choose to delegate work to AI systems precisely because it is convenient and practical. Any interventions that compel users to perform tasks without using AI systems could thus limit the benefits of AI usage and oppose user incentives.

Evidence gaps

There are major evidence gaps regarding the risks to human autonomy from AI, related to measurement, transparency, and the fact that the technology is relatively new. The effects of AI systems on human autonomy can be difficult to observe or evaluate due to the lack of a consensus definition of autonomy in the context of human-AI interactions, as well as practical challenges in assessing it. Research is further constrained by limited access to real-world interaction data from systems, including chatbots or AI companions, which inhibits independent evaluation of how they affect users in practice. Evidence is also limited by the novelty of many interaction patterns – particularly sustained or socially complex chatbot use – for which little longitudinal research exists to assess potential cumulative or longer-term impacts on autonomy. Early examples of such studies are emerging, however. Another evidence gap concerns the systemic effects that could result from widespread erosion of individual autonomy. For example, some researchers argue that degraded decision-making skills could impair humans’ ability to oversee AI systems in critical sectors, potentially weakening institutional accountability over time. More broadly, individual-level disruptions to autonomy could accumulate across interconnected economic, political, and social systems, eventually crossing thresholds that trigger broader societal impacts. However, these possibilities currently remain highly speculative, and empirical methods to detect or measure such aggregate effects are lacking.

Challenges for policymakers

For policymakers working on maintaining human autonomy, key challenges include distinguishing temporary disruption from longer-term effects and managing growing pressure to adopt AI systems. Understanding long-term effects of human-AI interactions is especially relevant in education, where children’s early interactions with AI systems may influence how their key skills and habits develop over time. It can be difficult to assess whether observed changes in behaviour or decision-making represent short-term adjustments to new tools or more persistent shifts that could affect autonomy. At the same time, organisational and governmental incentives to deploy AI systems quickly can limit opportunities to evaluate these effects carefully and to implement appropriate safeguards.