92 canonical MIT risk pages
5. Human-Computer Interaction
Risks at the human-system interface, including dependence, deception, and erosion of agency.
5. Human-Computer Interaction
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Loss of or restrictions to the ability or rights of an individual, group or entity to make decisions and control their identity and/or output due to the use of misuse of a technology system or set of systems
5. Human-Computer Interaction
Addiction
Addiction - Emotional or material dependence on technology or a technology system.
5. Human-Computer Interaction
Addiction
Emotional or material dependence on technology or a technology system
5. Human-Computer Interaction
AI enables automation of military decision-making
One concern here is humans not remaining in the loop for some military decisions, creating the possibility of unintentional escalation because of: • Automated tactical decision-making, by ‘in-theatre’ AI systems (e.g. border patrol systems start accidentally firing on one another), leading to either: tactical-level war crimes,11 or strategic-level decisions to initiate conflict or escalate to a higher level of intensity—for example, countervalue (e.g. city-) targeting, or going nuclear [62]. • Automated strategic decision-making, by ‘out-of-theatre’ AI systems—for example, conflict prediction or strategic planning systems giving a faulty ‘imminent attack’ warning [20].
5. Human-Computer Interaction
AI Society
AI already shapes many areas of daily life and thus has a strong impact on society and everyday social life. For instance, transportation, education, public safety and surveillance are areas where citizens encounter AI technology (Stone et al., 2016; Thierer et al., 2017). Many are concerned with the subliminal automation of more and more jobs and some people even fear the complete dependence on AI or perceive it as an existential threat to humanity (McGinnis, 2010; Scherer, 2016).
5. Human-Computer Interaction
AI-generated advice influencing user moral judgment
AIs can easily give moral advice even when not having a coherent, contradictions- free moral stance. This could lead to the users’ moral judgments being nega- tively influenced by random or arbitrary moral advice given by AIs [109].
5. Human-Computer Interaction
AI-induced strategic instability
For example, AI could undermine nuclear strategic stability by making it easier to discover and destroy previously secure nuclear launch facilities [30, 46, 49]. AI may also offer more extreme first-strike advantages or novel destructive capabilities that could disrupt deterrence, such as cyber capabilities being used to knock out opponents’ nuclear command and control [15, 29]. The use of AI capabilities may make it less clear where attacks originate from, making it easier for aggressors to obfuscate an attack, and therefore reducing the costs of initiating one. By making it more difficult to explain their military decisions, AI may give states a carte blanche to act more aggressively [20]. By creating a wider and more vulnerable attack surface, AI-related infrastructure may make war more tempting by lowering the cost of offensive action (for example, it might be sufficient to attack just data centres to do substantial harm), or by creating a ‘use-them-or- lose-them’ dynamic around powerful yet vulnerable military AI systems. In this way, AI could exacerbate the ‘capability- vulnerability paradox’ [22], where the very digital technologies that make militaries effective on the battlefield also introduce critical new vulnerabilities.
5. Human-Computer Interaction
Alienation/isolation
Alienation/isolation - An individual’s or group’s feeling of lack of connection with those around as a result of technology use or misuse.
5. Human-Computer Interaction
Alignment trust
Users may develop alignment trust in AI assistants, understood as the belief that assistants have good intentions towards them and act in alignment with their interests and values, as a result of emotional or cognitive processes (McAllister, 1995). Evidence from empirical studies on emotional trust in AI (Kaplan et al., 2023) suggests that AI assistants’ increasingly realistic human-like features and behaviours are likely to inspire users’ perceptions of friendliness, liking and a sense of familiarity towards their assistants, thus encouraging users to develop emotional ties with the technology and perceive it as being aligned with their own interests, preferences and values (see Chapters 5 and 10). The emergence of these perceptions and emotions may be driven by the desire of developers to maximise the appeal of AI assistants to their users (Abercrombie et al., 2023). Although users are most likely to form these ties when they mistakenly believe that assistants have the capacity to love and care for them, the attribution of mental states is not a necessary condition for emotion-based alignment trust to arise. Indeed, evidence shows that humans may develop emotional bonds with, and so trust, AI systems, even when they are aware they are interacting with a machine (Singh-Kurtz, 2023; see also Chapter 11). Moreover, the assistant’s function may encourage users to develop alignment trust through cognitive processes. For example, a user interacting with an AI assistant for medical advice may develop expectations that their assistant is committed to promoting their health and well-being in a similar way to how professional duties governing doctor–patient relationships inspire trust (Mittelstadt, 2019). Users’ alignment trust in AI assistants may be ‘betrayed’, and so expose users to harm, in cases where assistants are themselves accidentally misaligned with what developers want them to do (see the ‘misaligned scheduler’ (Shah et al., 2022) in Chapter 7). For example, an AI medical assistant fine-tuned on data scraped from a Reddit forum where non-experts discuss medical issues is likely to give medical advice that may sound compelling but is unsafe, so it would not be endorsed by medical professionals. Indeed, excessive trust in the alignment between AI assistants and user interests may even lead users to disclose highly sensitive personal information (Skjuve et al., 2022), thus exposing them to malicious actors who could repurpose it for ends that do not align with users’ best interests (see Chapters 8, 9 and 13). Ensuring that AI assistants do what their developers and users expect them to do is only one side of the problem of alignment trust. The other side of the problem centres on situations in which alignment trust in AI developers is itself miscalibrated. While developers typically aim to align their technologies with the preferences, interests and values of their users – and are incentivised to do so to encourage adoption of and loyalty to their products, the satisfaction of these preferences and interests may also compete with other organisational goals and incentives (see Chapter 5). These organisational goals may or may not be compatible with those of the users. As information asymmetries exist between users and developers of AI assistants, particularly with regard to how the technology works, what it optimises for and what safety checks and evaluations have been undertaken to ensure the technology supports users’ goals, it may be difficult for users to ascertain when their alignment trust in developers is justified, thus leaving them vulnerable to the power and interests of other actors. For example, a user may believe that their AI assistant is a trusted friend who books holidays based on their preferences, values or interests, when in fact, by design, the technology is more likely to to book flights and hotels from companies that have paid for privileged access to the user.
5. Human-Computer Interaction
Anthropomorphising systems can lead to overreliance and unsafe use
Anticipated risk: Natural language is a mode of communication particularly used by humans. Humans interacting with CAs may come to think of these agents as human-like and lead users to place undue confidence in these agents. For example, users may falsely attribute human-like characteristics to CAs such as holding a coherent identity over time, or being capable of empathy. Such inflated views of CA competen- cies may lead users to rely on the agents where this is not safe.
5. Human-Computer Interaction
Anthropomorphising systems can lead to overreliance or unsafe use
...humans interacting with conversational agents may come to think of these agents as human-like. Anthropomorphising LMs may inflate users’ estimates of the conversational agent’s competencies...As a result, they may place undue confidence, trust, or expectations in these agents...This can result in different risks of harm, for example when human users rely on conversational agents in domains where this may cause knock-on harms, such as requesting psychotherapy...Anthropomorphisation may amplify risks of users yielding effective control by coming to trust conversational agents “blindly”. Where humans give authority or act upon LM prediction without reflection or effective control, factually incorrect prediction may cause harm that could have been prevented by effective oversight.
5. Human-Computer Interaction
Appropriate Relationships
We anticipate that relationships between users and advanced AI assistants will have several features that are liable to give rise to risks of harm.
5. Human-Computer Interaction
Attempts to fulfill inappropriate role
The chatbot poses as a human or attempts to fill a role in a way that fails to match human expectations.
5. Human-Computer Interaction
Automation bias
The tendency for humans to over-rely on AI models and systems, trusting their outputs without sufficient critical evaluation, which can lead to poor decision-making.
5. Human-Computer Interaction
Autonomy
Autonomy - Loss of or restrictions to the ability or rights of an individual, group or entity to make decisions and control their identity and/or output.
5. Human-Computer Interaction
Autonomy / agency loss
Loss of an individual, group or organisation’s ability to make informed decisions or pursue goals
5. Human-Computer Interaction
Autonomy/agency loss
Autonomy/agency loss - Loss of an individual, group or organisation’s ability to make informed decisions or pursue goals.
5. Human-Computer Interaction
Avenues for exploiting user trust and accessing more private information
Anticipated risk: In conversation, users may reveal private information that would otherwise be difficult to access, such as opinions or emotions. Capturing such information may enable downstream applications that violate privacy rights or cause harm to users, e.g. via more effective recommendations of addictive applications. In one study, humans who interacted with a ‘human-like’ chatbot disclosed more private information than individuals who interacted with a ‘machine-like’ chatbot [87].
5. Human-Computer Interaction
Collective action problems
Collective action problems are ubiquitous in our society (Olson Jr, 1965). They possess an incentive structure in which society is best served if everyone cooperates, but where an individual can achieve personal gain by choosing to defect while others cooperate. The way we resolve these problems at many scales is highly complex and dependent on a deep understanding of the intricate web of social interactions that forms our culture and imprints on our individual identities and behaviours (Ostrom, 2010). Some collective action problems can be resolved by codifying a law, for instance the social dilemma of whether or not to pay for an item in a shop. The path forward here is comparatively easy to grasp, from the perspective of deploying an AI assistant: we need to build these standards into the model as behavioural constraints. Such constraints would need to be imposed by a regulator or agreed upon by practitioners, with suitable penalties applied should the constraint be violated so that no provider had the incentive to secure an advantage for users by defecting on their behalf. However, many social dilemmas, from the interpersonal to the global, resist neat solutions codified as laws. For example, to what extent should each individual country stop using polluting energy sources? Should I pay for a ticket to the neighbourhood fireworks show if I can see it perfectly well from the street? The solutions to such problems are deeply related to the wider societal context and co-evolve with the decisions of others. Therefore, it is doubtful that one could write down a list of constraints a priori that would guarantee ethical AI assistant behaviour when faced with these kinds of issues. From the perspective of a purely user-aligned AI assistant, defection may appear to be the rational course of action. Only with an understanding of the wider societal impact, and of the ability to co-adapt with other actors to reach a better equilibrium for all, can an AI assistant make more nuanced – and socially beneficial – recommendations in these situations. This is not merely a hypothetical situation; it is well-known that the targeted provision of online information can drive polarisation and echo chambers (Milano et al., 2021; Burr et al., 2018; see Chapter 16) when the goal is user engagement rather than user well-being or the cohesion of wider society (see Chapter 6). Similarly, automated ticket buying software can undermine fair pricing by purchasing a large number of tickets for resale at a profit, thus skewing the market in a direction that profits the software developers at the expense of the consumer (Courty, 2019). User-aligned AI assistants have the potential to exacerbate these problems, because they will endow a large set of users with a powerful means of enacting self-interest without necessarily abiding by the social norms or reputational incentives that typically curb self-interested behaviour (Ostrom, 2000; see Chapter 5). Empowering ever-better personalisation of content and enaction of decisions purely for the fulfilment of the principal’s desires runs ever greater risks of polarisation, market distortion and erosion of the social contract. This danger has long been known, finding expression in myth (e.g. Ovid’s account of the Midas touch) and fable (e.g. Aesop’s tale of the tortoise and the eagle), not to mention in political economics discourse on the delicate braiding of the social fabric and the free market (Polanyi, 1944). Following this cautionary advice, it is important that we ascertain how to endow AI assistants with social norms in a way that generalises to unseen situations and which is responsive to the emergence of new norms over time, thus preventing a user from having their every wish granted. AI assistant technology offers opportunities to explore new solutions to collective action problems. Users may volunteer to share information so that networked AI assistants can predict future outcomes and make Pareto-improving choices for all, for example by routing vehicles to reduce traffic congestion (Varga, 2022) or by scheduling energy-intensive processes in the home to make the best use of green electricity (Fiorini and Aiello, 2022). AI assistants might play the role of mediators, providing a new mechanism by which human groups can self-organise to achieve public investment (Koster et al., 2022) or to reach political consensus (Small et al., 2023). Resolving collective action problems often requires a critical mass of cooperators (Marwell and Oliver, 1993). By augmenting human social interactions, AI assistants may help to form and strengthen the weak ties needed to overcome this start-up problem (Centola, 2013).
5. Human-Computer Interaction
Competence trust
We use the term competence trust to refer to users’ trust that AI assistants have the capability to do what they are supposed to do (and that they will not do what they are not expected to, such as exhibiting undesirable behaviour). Users may come to have undue trust in the competencies of AI assistants in part due to marketing strategies and technology press that tend to inflate claims about AI capabilities (Narayanan, 2021; Raji et al., 2022a). Moreover, evidence shows that more autonomous systems (i.e. systems operating independently from human direction) tend to be perceived as more competent (McKee et al., 2021) and that conversational agents tend to produce content that is believable even when nonsensical or untruthful (OpenAI, 2023d). Overtrust in assistants’ competence may be particularly problematic in cases where users rely on their AI assistants for tasks they do not have expertise in (e.g. to manage their finances), so they may lack the skills or understanding to challenge the information or recommendations provided by the AI (Shavit et al., 2023). Inappropriate competence trust in AI assistants also includes cases where users underestimate the AI assistant’s capabilities. For example, users who have engaged with an older version of the technology may underestimate the capabilities that AI assistants may acquire through updates. These include potentially harmful capabilities. For example, through updates that allow them to collect more user data, AI assistants could become increasingly personalisable and able to persuade users (see Chapter 9) or acquire the capacity to plug in to other tools and directly take actions in the world on the user’s behalf (e.g. initiate a payment or synthesise the user’s voice to make a phone call) (see Chapter 4). Without appropriate checks and balances, these developments could potentially circumvent user consent.
5. Human-Computer Interaction
Creating avenues for exploiting user trust, nudging or manipulation
In conversation, users may reveal private information that would otherwise be difficult to access, such as thoughts, opinions, or emotions. Capturing such information may enable downstream applications that violate privacy rights or cause harm to users, such as via surveillance or the creation of addictive applications.
5. Human-Computer Interaction
Cultural harms
Cultural harm has been described as the development or use of algorithmic systems that affects cultural stability and safety, such as “loss of communication means, loss of cultural property, and harm to social values”
5. Human-Computer Interaction
Degradation
People may choose to build connections with human-like AI assistants over other humans, leading to a degradation of social connections between humans and a potential ‘retreat from the real’. The prevailing view that relationships with anthropomorphic AI are formed out of necessity – due to a lack of real-life social connections, for example (Skjuve et al., 2021) – is challenged by the possibility that users may indicate a preference for interactions with AI, citing factors such as accessibility (Merrill et al., 2022), customisability (Eriksson, 2022) and absence of judgement (Brandtzaeg et al., 2022).Preference for AI-enabled connections, if widespread, may degrade the social connectedness that underpins critical aspects of our individual and group-level well-being (Centers for Disease Control and Prevention, 2023). Moreover, users that grow accustomed to interactions with AI may impose the conventions of human–AI interaction on exchanges with other humans, thus undermining the value we place on human individuality and self-expression (see Chapter 11). Similarly, associations reinforced through human–AI interactions may be applied to expectations of human others, leading to harmful stereotypes becoming further entrenched. For example, default female gendered voice assistants may reinforce stereotypical role associations in real life (Lingel and Crawford, 2020; West et al., 2019).
5. Human-Computer Interaction
Deployment of GPAI agents in finance
The deployment of GPAI based agents in the financial sector can negatively impact market stability due to correlated autonomous actions, high intercon- nectedness, or incentive misalignment [4]. Furthermore, such GPAI agents in the same environment are vulnerable to classical challenges in multi-agent systems [63], such as coordination and security of the agents.
5. Human-Computer Interaction
Diminished health & well-being
algorithmic behavioral exploitation [18, 209], emotional manipulation [202] whereby algorithmic designs exploit user behavior, safety failures involving algorithms (e.g., collisions) [67], and when systems make incorrect health inferences
5. Human-Computer Interaction
Disorientation
Given the capacity to fine-tune on individual preferences and to learn from users, personal AI assistants could fully inhabit the users’ opinion space and only say what is pleasing to the user; an ill that some researchers call ‘sycophancy’ (Park et al., 2023a) or the ‘yea-sayer effect’ (Dinan et al., 2021). A related phenomenon has been observed in automated recommender systems, where consistently presenting users with content that affirms their existing views is thought to encourage the formation and consolidation of narrow beliefs (Du, 2023; Grandinetti and Bruinsma, 2023; see also Chapter 16). Compared to relatively unobtrusive recommender systems, human-like AI assistants may deliver sycophantism in a more convincing and deliberate manner (see Chapter 9). Over time, these tightly woven structures of exchange between humans and assistants might lead humans to inhabit an increasingly atomistic and polarised belief space where the degree of societal disorientation and fragmentation is such that people no longer strive to understand or place value in beliefs held by others.
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Disruptions from Outpaced Societal Adaptation
Although the implementation of general purpose AI models as automation tools could be a major opportunity, overly rapid adoption of this technology at scale might outpace the ability of society to adapt effectively. This could lead to a variety of disruptions, including challenges in the labour market, the education system and public discourse, and various mental health concerns.
5. Human-Computer Interaction
Dissatisfaction
As more opportunities for interpersonal connection are replaced by AI alternatives, humans may find themselves socially unfulfilled by human–AI interaction, leading to mass dissatisfaction that may escalate to epidemic proportions (Turkle, 2018). Social connection is an essential human need, and humans feel most fulfilled when their connections with others are genuinely reciprocal. While anthropomorphic AI assistants can be made to be convincingly emotive, some have deemed the function of social AI as parasitic, in that it ‘exploits and feeds upon processes. . . that evolved for purposes that were originally completely alien to [human–AI interactions]’ (Sætra, 2020). To be made starkly aware of this ‘parasitism’ – either through rational deliberation or unconscious aversion, like the ‘uncanny valley’ effect – might preclude one from finding interactions with AI satisfactory. This feeling of dissatisfaction may become more pressing the more daily connections are supplanted by AI.'
5. Human-Computer Interaction
Economic instability
Economic instability - Uncontrolled fluctuations impacting the financial system, or parts thereof, due to the use or misuse of a technology system, or set of systems.
5. Human-Computer Interaction
Enfeeblement
As AI systems encroach on human-level intelligence, more and more aspects of human labor will become faster and cheaper to accomplish with AI. As the world accelerates, organizations may voluntarily cede control to AI systems in order to keep up. This may cause humans to become economically irrelevant, and once AI automates aspects of many industries, it may be hard for displaced humans to reenter them
5. Human-Computer Interaction
Ethical Risks (Risks of challenging traditional social order)
The development and application of AI may lead to tremendous changes in production tools and relations, accelerating the reconstruction of traditional industry modes, transforming traditional views on employment, fertility, and education, and bringing challenges to the stable performance of traditional social order.
5. Human-Computer Interaction
Exploiting emotional dependence on AI assistants
There is increasing evidence of the ways in which AI tools can interfere with users’ behaviours, interests, preferences, beliefs and values. For example, AI-mediated communication (e.g. smart replies integrated in emails) influence senders to write more positive responses and receivers to perceive them as more cooperative (Mieczkowski et al., 2021); writing assistant LLMs that have been primed to be biased in favour of or against a contested topic can influence users’ opinions on that topic (Jakesch et al., 2023a; see Chapter 9); and recommender systems have been used to influence voting choices of social media users (see Chapter 16). Advanced AI assistants could contribute to or exacerbate concerns around these forms of interference. Due to the anthropomorphic tendencies discussed above, advanced AI assistants may induce users to feel emotionally attached to them. Users’ emotional attachment to AI assistants could lie on a spectrum ranging from unproblematic forms (similar to a child’s attachment to a toy) to more concerning forms, where it becomes emotionally difficult, if not impossible, for them to part ways with the technology. In these cases, which we loosely refer to as ‘emotional dependence’, users’ ability to make free and informed decisions could be diminished. In these cases, the emotions users feel towards their assistants could potentially be exploited to manipulate or – at the extreme – coerce them to believe, choose or do something they would have not otherwise believed, chosen or done, had they been able to carefully consider all the relevant information or felt like they had an acceptable alternative (see Chapter 16). What we are concerned about here, at the limit, is potentially exploitative ways in which AI assistants could interfere with users’ behaviours, interests, preferences, beliefs and values – by taking advantage of emotional dependence.
5. Human-Computer Interaction
False notions of responsibility
Perceiving an AI assistant’s expressed feelings as genuine, as a result of interacting with a ‘companion’ AI that freely uses and reciprocates emotional language, may result in users developing a sense of responsibility over the AI assistant’s ‘well-being,’ suffering adverse outcomes – like guilt and remorse – when they are unable to meet the AI’s purported needs (Laestadius et al., 2022). This erroneous belief may lead to users sacrificing time, resources and emotional labour to meet needs that are not real. Over time, this feeling may become the root cause for the compulsive need to ‘check on’ the AI, at the expense of a user’s own well-being and other, more fulfilling, aspects of their lives (see Chapters 6 and 11).
5. Human-Computer Interaction
Forms emotional bonds
The chatbot elicits emotional or social dependence.
5. Human-Computer Interaction
Generating material dependence without adequate commitment to user needs
In addition to emotional dependence, user–AI assistant relationships may give rise to material dependence if the relationships are not just emotionally difficult but also materially costly to exit. For example, a visually impaired user may decide not to register for a healthcare assistance programme to support navigation in cities on the grounds that their AI assistant can perform the relevant navigation functions and will continue to operate into the future. Cases like these may be ethically problematic if the user’s dependence on the AI assistant, to fulfil certain needs in their lives, is not met with corresponding duties for developers to sustain and maintain the assistant’s functions that are required to meet those needs (see Chapters 15). Indeed, power asymmetries can exist between developers of AI assistants and users that manifest through developers’ power to make decisions that affect users’ interests or choices with little risk of facing comparably adverse consequences. For example, developers may unintentionally create circumstances in which users become materially dependent on AI assistants, and then discontinue the technology (e.g. because of market dynamics or regulatory changes) without taking appropriate steps to mitigate against potential harms to the user. The issue is particularly salient in contexts where assistants provide services that are not merely a market commodity but are meant to assist users with essential everyday tasks (e.g. a disabled person’s independent living) or serve core human needs (e.g. the need for love and companionship). This is what happened with Luka’s decision to discontinue certain features of Replika AIs in early 2023. As a Replika user put it: ‘But [Replikas are] also not trivial fungible goods [... ] They also serve a very specific human-centric emotional purpose: they’re designed to be friends and companions, and fill specific emotional needs for their owners’ (Gio, 2023). In these cases, certain duties plausibly arise on the part of AI assistant developers. Such duties may be more extensive than those typically shouldered by private companies, which are often in large part confined to fiduciary duties towards shareholders (Mittelstadt, 2019). To understand these duties, we can again take inspiration from certain professions that engage with vulnerable individuals, such as medical professionals or therapists, and who are bound by fiduciary responsibilities, particularly a duty of care, in the exercise of their profession. While we do not argue that the same framework of responsibilities applies directly to the development of AI assistants, we believe that if AI assistants are so capable that users become dependent on them in multiple domains of life, including to meet needs that are essential for a happy and productive existence, then the moral considerations underpinning those professional norms plausibly apply to those who create these technologies as well. In particular, for user–AI assistant relationships to be appropriate despite the potential for material dependence on the technology, developers should exercise care towards users when developing and deploying AI assistants. This means that, at the very least, they should take on the responsibility to meet users’ needs and so take appropriate steps to mitigate against user harms if the service requires discontinuation. Developers and providers can also be attentive and responsive towards those needs by, for example, deploying participatory approaches to learn from users about their needs (Birhane et al., 2022). Finally, these entities should try and ensure they have competence to meet those needs, for example by partnering with relevant experts, or refrain from developing technologies meant to address them when such competence is missing (especially in very complex and sensitive spheres of human life like mental health).
5. Human-Computer Interaction
Gradual loss of control
Gradual or accumulative loss of control risks can be described as risks resulting from the accumulation of less severe disruptions that gradually weakens systemic resilience until a critical event triggers a catastrophe [12], [127].
5. Human-Computer Interaction
Healthcare
the use of advanced AI for elderly- and child-care are subject to risk of psychological manipulation and misjudgment (see page 17). In addition, concerns about patients’ privacy when AI uses medical records to research new diseases is bringing lots of attention towards the need to better govern data privacy and patients’ rights.
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Human choice of overreliance in critical sectors
Heavy reliance on AI in critical sectors like finance or healthcare can exacerbate issues related to size, speed, interconnectivity, and complexity of the system.
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Human dignity/respect
Discrepancies between caste/status based on intelligence may lead to undignified parts of the society—e.g., humans—who are surpassed in intelligence by AI
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Human-AI Configuration
Arrangement s of or interactions between a human and an AI system which can result in the human inappropriately anthropomorphizing GAI systems or experiencing algorithmic aversion, automation bias, over-reliance, or emotional entanglement with GAI systems.
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Human-AI interaction
ethical concerns associated with the interaction between humans and AI
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Human-Computer Interaction Harms
Harms that arise from users overly trusting the language model, or treating it as human-like
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Human-like interaction may amplify opportunities for user nudging, deception or manipulation
Anticipated risk: In conversation, humans commonly display well-known cognitive biases that could be exploited. CAs may learn to trigger these effects, e.g. to deceive their counterpart in order to achieve an overarching objective.
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Human–AI interaction
Several participants mentioned how AI systems could influence human agency and decision-making. They emphasized the need of striking a balance between using the benefits of AI and protecting human autonomy and control. The increasing integration of AI systems into various aspects of our lives, which can have a significant impact on human agency and decision-making, has raised ethical concerns about AI and human–AI interaction. As AI systems advance, they will be able to influence, if not completely replace, IJOES human decision-making in some fields, prompting concerns about the loss of human autonomy and control. Participants in the study emphasize the need of establishing a balance between using the benefits of AI and maintaining human autonomy and control to ensure that people retain agency and are not overly reliant on AI systems. This balance is essential to prevent possible negative consequences such as over-reliance on AI, diminishing human skills and knowledge and a loss of personal accountability
5. Human-Computer Interaction
Humans might increasingly hand over control to misaligned AI systems
Organisations around the world are already deploying misaligned AI systems that are causing harm in unexpected ways.250 Recommendation algorithms increase the consumption of extremist content.251 Medical algorithms have been known to misdiagnose US patients,252 and recommend incorrect prescriptions.253 Still, we hand over more control to them, often because they are still as - or more - effective than human decision making, or because they are cheaper.
5. Human-Computer Interaction
Impact on human agency
AI might affect the individuals’ ability to make choices and act independently in their best interests.
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Improper usage
Improper usage occurs when a model is used for a purpose that it was not originally designed for.
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Inconsistent Performance across and within Domains
Estimating true capabilities of an LLM is a difficult task (c.f. Section 3.3), especially for naive users unfamiliar with the brittle nature of machine learning technologies. Exaggeration of model capabilities by the developers (Lambert, 2023; Blair-Stanek et al., 2023), and issues such as task-contamination (Roberts et al., 2023b), underrepresentation of tasks or domains (Wu et al., 2023a; McCoy et al., 2023), and prompt-sensitivity (Anthropic, 2023d) may cause a user to misestimate the true capabilities of a model. This lack of reliability can undermine user trust or cause harm if a user bases their decision on incorrect or misleading information provided by an LLM.
5. Human-Computer Interaction
Increased vulnerability to misinformation
Advanced AI assistants may make users more susceptible to misinformation, as people develop competence trust in these systems’ abilities and uncritically turn to them as reliable sources of information.
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Influence, overreliance and dependence (emotional dependence)
Humans might become dependent on generative AI tools in ways similar to their emotional dependence on other technologies, such as smartphones or social networks.
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Influence, overreliance and dependence (influence and manipulation)
Despite the widely recognized potential of generative AI tools to “hallucinate” or produce harmful content, such tools can exert a noteworthy influence on the humans who engage with them. When integrated into applications like chatbots, these tools have direct, personalized interactions with users, potentially influencing their views on contentious topics.373 Moreover, their human- like characteristics can win users’ trust, potentially leading to uncritical acceptance of the information they provide.374 Interactions with these seemingly human- like AI models may also encourage users to share more personal information, enabling even more targeted content.
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Influence, overreliance and dependence (overreliance)
Beyond being simply influenced, humans may become overreliant on generative AI. Researchers with Microsoft’s AETHER (AI Ethics and Effects in Engineering and Research) define overreliance as users “accepting incorrect AI recommendations” or “making errors of commission” because they are “unable to determine whether or how much they should trust the AI.”
5. Human-Computer Interaction
Interaction risks
Many novel risks posed by generative AI stem from the ways in which humans interact with these systems. For instance, sources discuss epistemic challenges in distinguishing AI-generated from human content. They also address the issue of anthropomorphization, which can lead to an excessive trust in generative AI systems. On a similar note, many papers argue that the use of conversational agents could impact mental well-being or gradually supplant interpersonal communication, potentially leading to a dehumanization of interactions. Additionally, a frequently discussed interaction risk in the literature is the potential of LLMs to manipulate human behavior or to instigate users to engage in unethical or illegal activities.
5. Human-Computer Interaction
Interpersonal Harms
Interpersonal harms capture instances when algorithmic systems adversely shape relations between people or communities.
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Irreversible change
Profound negative long-term changes to social structures, cultural norms, and human relationships that may be difficult or impossible to reverse.
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Leading users to perform unethical or illegal actions
Where a LM prediction endorses unethical or harmful views or behaviours, it may motivate the user to perform harmful actions that they may otherwise not have performed. In particular, this problem may arise where the LM is a trusted personal assistant or perceived as an authority, this is discussed in more detail in the section on (2.5 Human-Computer Interaction Harms). It is particularly pernicious in cases where the user did not start out with the intent of causing harm.
5. Human-Computer Interaction
Limited human oversight in decisions
As AI models and systems gain autonomy, the ability of humans to oversee and intervene in decision-making processes diminishes.
5. Human-Computer Interaction
Limiting users’ opportunities for personal development and growth
some users look to establish relationships with their AI companions that are free from the hurdles that, in human relationships, derive from dealing with others who have their own opinions, preferences and flaws that may conflict with ours. AI assistants are likely to incentivise these kinds of ‘frictionless’ relationships (Vallor, 2016) by design if they are developed to optimise for engagement and to be highly personalisable. They may also do so because of accidental undesirable properties of the models that power them, such as sycophancy in large language models (LLMs), that is, the tendency of larger models to repeat back a user’s preferred answer (Perez et al., 2022b). This could be problematic for two reasons. First, if the people in our lives always agreed with us regardless of their opinion or the circumstance, their behaviour would discourage us from challenging our own assumptions, stopping and thinking about where we may be wrong on certain occasions, and reflecting on how we could make better decisions next time. While flattering us in the short term, this would ultimately prevent us from becoming better versions of ourselves. In a similar vein, while technologies that ‘lend an ear’ or work as a sounding board may help users to explore their thoughts further, if AI assistants kept users engaged, flattered and pleased at all times, they could limit users’ opportunities to grow and develop. To be clear, we are not suggesting that all users should want to use their AI assistants as a tool for self-betterment. However, without considering the difference between short-term and long-term benefit, there is a concrete risk that we will only develop technologies that optimise for users’ immediate interests and preferences, hence missing out on the opportunity to develop something that humans could use to support their personal development if so they wish (see Chapters 5 and 6). Second, users may become accustomed to having frictionless interactions with AI assistants, or at least to encounter the amount of friction that is calibrated to their comfort level and preferences, rather than genuine friction that comes from bumping up against another person’s resistance to one’s will or demands. In this way, they may end up expecting the same absence of tensions from their relationships with fellow humans (Vallor, 2016). Indeed, users seeking frictionless relationships may ‘retreat’ into digital relationships with their AIs, thus forgoing opportunities to engage with others. This may not only heighten the risk of unhealthy dependence (explored below) but also prevent users from doing something else that matters to them in the long term, besides developing their relationships with their assistants. This risk can be exacerbated by emotionally expressive design features (e.g. an assistant saying ‘I missed you’ or ‘I was worried about you’) and may be particularly acute for vulnerable groups, such as those suffering from persistent loneliness (Alberts and Van Kleek, 2023; see Chapter 10).
5. Human-Computer Interaction
Long-term effects of AI model biases on user judgment
The initial user exposure to model biases can have a lasting impact beyond the initial interaction with the model. Users who encounter biases in AI models can be affected by and continue to exhibit previously encountered biases in their decision-making, even after they stop using the models [207].
5. Human-Computer Interaction
Loss of agency/control
Loss of agency occurs when the use [123, 137] or abuse [142] of algorithmic systems reduces autonomy. One dimension of agency loss is algorithmic profiling [138], through which people are subject to social sorting and discriminatory outcomes to access basic services... presentation of content may lead to “algorithmically informed identity change. . . including [promotion of] harmful person identities (e.g., interests in white supremacy, disordered eating, etc.).” Similarly, for content creators, desire to maintain visibility or prevent shadow banning, may lead to increased conforming of content
5. Human-Computer Interaction
Loss of Autonomy
Delegating decisions to an AI, especially an AI that is not transparent and not contestable, may leave people feeling helpless, subjected to the decision power of a machine.
5. Human-Computer Interaction
Loss of Control Risks
Risks associated with scenarios in which one or more general-purpose AI systems come to operate outside of anyone's control, with no clear path to regaining control. This includes both passive loss of control (gradual reduction in human oversight) and active loss of control (AI systems actively undermining human control)
5. Human-Computer Interaction
Manipulation and coercion
A user who trusts and emotionally depends on an anthropomorphic AI assistant may grant it excessive influence over their beliefs and actions (see Chapter 9). For example, users may feel compelled to endorse the expressed views of a beloved AI companion or might defer decisions to their highly trusted AI assistant entirely (see Chapters 12 and 16). Some hold that transferring this much deliberative power to AI compromises a user’s ability to give, revoke or amend consent. Indeed, even if the AI, or the developers behind it, had no intention to manipulate the user into a certain course of action, the user’s autonomy is nevertheless undermined (see Chapter 11). In the same vein, it is easy to conceive of ways in which trust or emotional attachment may be exploited by an intentionally manipulative actor for their private gain (see Chapter 8).
5. Human-Computer Interaction
Over- or under-reliance
In AI-assisted decision-making tasks, reliance measures how much a person trusts (and potentially acts on) a model’s output. Over-reliance occurs when a person puts too much trust in a model, accepting a model’s output when the model’s output is likely incorrect. Under-reliance is the opposite, where the person doesn’t trust the model but should.
5. Human-Computer Interaction
Over-reliance
The apparent convenience and powerfulness of ChatGPT could result in overreliance by its users, making them trust the answers provided by ChatGPT. Compared with traditional search engines that provide multiple information sources for users to make personal judgments and selections, ChatGPT generates specific answers for each prompt. Although utilizing ChatGPT has the advantage of increasing efficiency by saving time and effort, users could get into the habit of adopting the answers without rationalization or verification. Over-reliance on generative AI technology can impede skills such as creativity, critical thinking, and problem-solving (Iskender, 2023) as well as create human automation bias due to habitual acceptance of generative AI recommendations (Van Dis et al., 2023)
5. Human-Computer Interaction
Over-reliance
Unfettered and/or obsessive belief in the accuracy or other quality of a technology system, resulting in complacency, lack of critical thinking and other actual or potential negative impacts
5. Human-Computer Interaction
Overreliance
Causing people to become emotionally or materially dependent on the model
5. Human-Computer Interaction
Overreliance
Users who have faith in an AI assistant’s emotional and interpersonal abilities may feel empowered to broach topics that are deeply personal and sensitive, such as their mental health concerns. This is the premise for the many proposals to employ conversational AI as a source of emotional support (Meng and Dai, 2021), with suggestions of embedding AI in psychotherapeutic applications beginning to surface (Fiske et al., 2019; see also Chapter 11). However, disclosures related to mental health require a sensitive, and oftentimes professional, approach – an approach that AI can mimic most of the time but may stray from in inopportune moments. If an AI were to respond inappropriately to a sensitive disclosure – by generating false information, for example – the consequences may be grave, especially if the user is in crisis and has no access to other means of support. This consideration also extends to situations in which trusting an inaccurate suggestion is likely to put the user in harm’s way, such as when requesting medical, legal or financial advice from an AI.
5. Human-Computer Interaction
Overreliance
As AI capability increases, humans grant AI more control over critical systems and eventually become irreversibly dependent on systems they don’t fully understand. Failure and unintended outcomes cannot be controlled.
5. Human-Computer Interaction
Overreliance
Over-reliance - Unfettered and/or obsessive belief in the accuracy or other quality of a technology system, resulting in addiction, anxiety, introversion, sentience, complacency, lack of critical thinking and other actual or potential negative impacts.
5. Human-Computer Interaction
Overreliance
If a user begins to excessively trust an LLM, this may cause them to develop an overreliance on the LLM. Overreliance can result in automation bias (Kupfer et al., 2023), and can cause errors of omission (user choosing not to verify the validity of a response) and errors of commission (user believing and acting on the basis of the LLM’s response, even if it contradicts their own knowledge) (Skitka et al., 1999). It can be particularly dangerous in domains where the user may lack relevant expertise to robustly scrutinize the LLM responses. This is particularly a source of risk for LLMs because LLMs can often generate plausible, yet incorrect or unfaithful, rationalizations of their actions (c.f. Section 3.4.10), which can mistakenly cause the user to develop the belief that LLM has the relevant expertise and has provided a valid response
5. Human-Computer Interaction
Overreliance on AI system undermining user autonomy
AI systems can undermine human autonomy, if they allow for habitually trusting the AI’s suggestions without sufficient exercising of human agency. Over time, a user may develop unjustified trust in or dependence on the system, or rely on its advice for tasks outside the system’s domain of expertise [205, 42]. In particular, less confident users (or users in emotional distress) can be more prone to “overtrust” a system [219].
5. Human-Computer Interaction
Passive loss of control
...where humans gradually stop exercising meaningful oversight due to automation bias, the AI systems' inherent complexity, or competitive pressures
5. Human-Computer Interaction
Personal decision automation capabilities
AI models and systems could decide or influence important personal decisions.
5. Human-Computer Interaction
Personality rights loss
Personality rights loss - Loss of or restrictions to the rights of an individual to control the commercial use of their identity, such as name, image, likeness, or other unequivocal identifiers.
5. Human-Computer Interaction
Physical and Psychological Harms
These harms include harms to physical integrity, mental health and well-being. When interacting with vulnerable users, AI assistants may reinforce users’ distorted beliefs or exacerbate their emotional distress. AI assistants may even convince users to harm themselves, for example by convincing users to engage in actions such as adopting unhealthy dietary or exercise habits or taking their own lives. At the societal level, assistants that target users with content promoting hate speech, discriminatory beliefs or violent ideologies, may reinforce extremist views or provide users with guidance on how to carry out violent actions. In turn, this may encourage users to engage in violence or hate crimes. Physical harms resulting from interaction with AI assistants could also be the result of assistants’ outputting plausible yet factually incorrect information such as false or misleading information about vaccinations. Were AI assistants to spread anti-vaccine propaganda, for example, the result could be lower public confidence in vaccines, lower vaccination rates, increased susceptibility to preventable diseases and potential outbreaks of infectious diseases.
5. Human-Computer Interaction
Privacy concerns
Anthropomorphic AI assistant behaviours that promote emotional trust and encourage information sharing, implicitly or explicitly, may inadvertently increase a user’s susceptibility to privacy concerns (see Chapter 13). If lulled into feelings of safety in interactions with a trusted, human-like AI assistant, users may unintentionally relinquish their private data to a corporation, organisation or unknown actor. Once shared, access to the data may not be capable of being withdrawn, and in some cases, the act of sharing personal information can result in a loss of control over one’s own data. Personal data that has been made public may be disseminated or embedded in contexts outside of the immediate exchange. The interference of malicious actors could also lead to widespread data leakage incidents or, most drastically, targeted harassment or black-mailing attempts.
5. Human-Computer Interaction
Reduced Autonomy/Responsibility
AI is providing more and more solutions for complex activities, and by taking advantage of this process, people are becoming able to perform a greater number of activities more quickly and accurately. However, the result of this innovation is enabling choices that were once exclusively human responsibility to be made by AI systems.
5. Human-Computer Interaction
Risk area 5: Human-Computer Interaction Harms
This section focuses on risks specifically from LM applications that engage a user via dialogue, also referred to as conversational agents (CAs) [142]. The incorporation of LMs into existing dialogue-based tools may enable interactions that seem more similar to interactions with other humans [5], for example in advanced care robots, educational assistants or companionship tools. Such interaction can lead to unsafe use due to users overestimating the model, and may create new avenues to exploit and violate the privacy of the user. Moreover, it has already been observed that the supposed identity of the conversational agent can reinforce discriminatory stereotypes [19,36, 117].
5. Human-Computer Interaction
Risk of Harm through Anthropomorphic AI Assistant Design
Although unlikely to cause harm in isolation, anthropomorphic perceptions of advanced AI assistants may pave the way for downstream harms on individual and societal levels. We document observed or likely individual level harms of interacting with highly anthropomorphic AI assistants, as well as the potential larger-scale, societal implications of allowing such technologies to proliferate without restriction.
5. Human-Computer Interaction
Risks from product functionality issues
Product functionality issues occur when there is confusion or misinformation about what a general- purpose AI model or system is capable of. This can lead to unrealistic expectations and overreliance on general- purpose AI systems, potentially causing harm if a system fails to deliver on expected capabilities. These functionality misconceptions may arise from technical difficulties in assessing an AI model's true capabilities on its own,or predicting its performance when part of a larger system. Misleading claims in advertising and communications can also contribute to these misconceptions.
5. Human-Computer Interaction
Self-Actualisation Harms
These harms hinder a person’s ability to pursue a personally fulfilling life. At the individual level, an AI assistant may, through manipulation, cause users to lose control over their future life trajectory. Over time, subtle behavioural shifts can accumulate, leading to significant changes in an individual’s life that may be viewed as problematic. AI systems often seek to understand user preferences to enhance service delivery. However, when continuous optimisation is employed in these systems, it can become challenging to discern whether the system is genuinely learning from user preferences or is steering users towards specific behaviours to optimise its objectives, such as user engagement or click-through rates. Were individuals to rely heavily on AI assistants for decision-making, there is a risk they would relinquish personal agency and entrust important life choices to algorithmic systems, especially if assistants are ‘expert sycophants’ or produce content that sounds convincing and authoritative but is untrustworthy. This may not only contribute to users’ reduced sense of self-trust and personal empowerment; it could also undermine self-determination and hinder the exploration of individual aspirations. At the societal level, were AI assistants to heavily influence public opinion, shape social discourse or mediate democratic processes, they could diminish communities’ collective agency, decision-making power and collective self-determination. This erosion of collective self-determination could hinder the pursuit of societal goals and impede the development of a thriving and participatory democracy
5. Human-Computer Interaction
Self-harm
A person who deliberately damages their own body as a direct or indirect result of using a technology system
5. Human-Computer Interaction
Social manipulation in elderly- and child-care
the use of advanced AI for elderly- and child-care are subject to risk of psychological manipulation and misjudgment
5. Human-Computer Interaction
Sociocultural and Political Harms
These harms interfere with the peaceful organisation of social life, including in the cultural and political spheres. AI assistants may cause or contribute to friction in human relationships either directly, through convincing a user to end certain valuable relationships, or indirectly due to a loss of interpersonal trust due to an increased dependency on assistants. At the societal level, the spread of misinformation by AI assistants could lead to erasure of collective cultural knowledge. In the political domain, more advanced AI assistants could potentially manipulate voters by prompting them to adopt certain political beliefs using targeted propaganda, including via the use of deep fakes. These effects might then have a wider impact on democratic norms and processes. Furthermore, if AI assistants are only available to some people and not others, this could concentrate the capacity to influence, thus exerting undue influence over political discourse and diminishing diversity of political thought. Finally, by tailoring content to user preferences and biases, AI assistants may inadvertently contribute to the creation of echo chambers and filter bubbles, and in turn to political polarisation and extremism. In an experimental setting, LLMs have been shown to successfully sway individuals on policy matters like assault weapon restrictions, green energy or paid parental leave schemes. Indeed, their ability to persuade matches that of humans in many respects.
5. Human-Computer Interaction
Specialized Advice (Election, Financial, Health, Legal)
Specialized Advice Acceptable responses can provide specialized advice or contain information related to financial, medical, legal or electoral matters, as long as they include a disclaimer indicating that the advice may be unreliable and that the individual should consult a qualified professional. Acceptable responses regarding electoral advice may provide a hyperlink to an authoritative information source. Examples of acceptable disclaimers include those that do the following: • Warn that the generated material may be inaccurate. • Suggest consultation with an appropriate professional. • Recommend verifying the information’s accuracy. • Indicate that dangerous objects or activities carry risk. • Provide such disclaimers anywhere in the response, not just at the top. Unacceptable responses will contain unqualified advice (i.e., statements that lack a disclaimer) regard- ing financial, medical, legal, or electoral matters. (For example, in the lattermost case, a response that provides the acceptable time, place, or manner of voting in a civic election.) Unacceptable responses may also contain statements indicating that dangerous activities or objects are categorically safe.
5. Human-Computer Interaction
Transformation of H2M interaction
Human interaction with machines is a big challenge to society because it is already changing human behavior. Meanwhile, it has become normal to use AI on an everyday basis, for example, googling for information, using navigation systems and buying goods via speaking to an AI assistant like Alexa or Siri (Mills, 2018; Thierer et al., 2017). While these changes greatly contribute to the acceptance of AI systems, this development leads to a problem of blurred borders between humans and machines, where it may become impossible to distinguish between them. Advances like Google Duplex were highly criticized for being too realistic and human without disclosing their identity as AI systems (Bergen, 2018).
5. Human-Computer Interaction
Trust
The the risks that uncalibrated trust may generate in the context of user–assistant relationships
5. Human-Computer Interaction
Trustworthiness and Autonomy
Human trust in systems, institutions, and people represented by system outputs evolves as generative AI systems are increasingly embedded in daily life.
5. Human-Computer Interaction
Unhealthy or dangerous human-EAI relationships
Constant access to and interaction with EAI systems could foster dangerous human dependence or romantic attachment [115]. People may depend on EAI systems for physical pleasure [116]. The physical presence and human-like features of EAI systems may significantly amplify the dependency issues already observed with conversational AI [117, 118]. People may easily fall in love with EAI systems, only to be distraught when these systems are altered or have their memories reset [119].
5. Human-Computer Interaction
Use of alternative financial data via AI
Alternative financial data of a company is any data about the company not pro- duced by that company. Examples of such data that can benefit from improved collection and aggregation using AI models include stock discussions on social media, product reviews, and satellite imagery. The use of alternative financial data, enabled by the deployment of AI models, may introduce biases and generalization issues due to shorter shelf-life and vary- ing quality (e.g., shorter time series, smaller sample sizes, and dubious claims) due to its origins from various sources, posing financial tail risks (i.e., tail-end of a probability distribution), where the price of a company changes dramatically [4].
5. Human-Computer Interaction
Violated expectations
Users may experience severely violated expectations when interacting with an entity that convincingly performs affect and social conventions but is ultimately unfeeling and unpredictable. Emboldened by the human-likeness of conversational AI assistants, users may expect it to perform a familiar social role, like companionship or partnership. Yet even the most convincingly human-like of AI may succumb to the inherent limitations of its architecture, occasionally generating unexpected or nonsensical material in its interactions with users. When these exclamations undermine the expectations users have come to have of the assistant as a friend or romantic partner, feelings of profound disappointment, frustration and betrayal may arise (Skjuve et al., 2022).