Access and Opportunity risks

Entrenchment and exacerbation of existing inequalities

"The most serious access-related risks posed by advanced AI assistants concern the entrenchment and exacerbation of existing inequalities (World Inequality Database) or the creation of novel, previously unknown, inequities. While advanced AI assistants are novel technology in certain respects, there are reasons to believe that – without direct design interventions – they will continue to be affected by inequities evidenced in present-day AI systems (Bommasani et al., 2022a). Many of the access-related risks we foresee mirror those described in the case studies and types of differential access. In this section, we link them more tightly to elements of the definition of an advanced AI assistant to better understand and mitigate potential issues – and lay the path for assistants that support widespread and inclusive opportunity and access. We begin with the existing capabilities set out in the definition (see Chapter 2) before applying foresight to those that are more novel and emergent. Current capabilities: Artificial agents with natural language interfaces. Artificial agents with natural language interfaces are widespread (Browne, 2023) and increasingly integrated into the social fabric and existing information infrastructure, including search engines (Warren, 2023), business messaging apps (Slack, 2023), research tools (ATLAS.ti, 2023) and accessibility apps for blind and low-vision people (Be My Eyes, 2023). There is already evidence of a range of sociotechnical harms that can arise from the use of artificial agents with natural language interfaces when some communities have inferior access to them (Weidinger et al., 2021). As previously described, these harms include inferior quality of access (in situation type 2) across user groups, which may map onto wider societal dynamics involving race (Harrington et al., 2022), disability (Gadiraju et al., 2023) and culture (Jenka, 2023). As developers make it easier to integrate these technologies into other tools, services and decision-making systems (e.g. Marr, 2023; Brockman et al., 2023; Pinsky, 2023), their uptake could make existing performance inequities more pronounced or introduce them to new and wider publics."

Current access risks

"At the same time, and despite this overall trend, AI systems are also not easily accessible to many communities. Such direct inaccessibility occurs for a variety of reasons, including: purposeful non-release (situation type 1; Wiggers and Stringer, 2023), prohibitive paywalls (situation type 2; Rogers, 2023; Shankland, 2023), hardware and compute requirements or bandwidth (situation types 1 and 2; OpenAI, 2023), or language barriers (e.g. they only function well in English (situation type 2; Snyder, 2023), with more serious errors occurring in other languages (situation type 3; Deck, 2023). Similarly, there is some evidence of ‘actively bad’ artificial agents gating access to resources and opportunities, affecting material well-being in ways that disproportionately penalise historically marginalised communities (Block, 2022; Bogen, 2019; Eubanks, 2017). Existing direct and indirect access disparities surrounding artificial agents with natural language interfaces could potentially continue – if novel capabilities are layered on top of this base without adequate mitigation (see Chapter 3)."

Future access risks

"AI assistants currently tend to perform a limited set of isolated tasks: tools that classify or rank content execute a set of predefined rules or provide constrained suggestions, and chatbots are often encoded with guardrails to limit the set of conversation turns they execute (e.g. Warren, 2023; see Chapter 4). However, an artificial agent that can execute sequences of actions on the user’s behalf – with ‘significant autonomy to plan and execute tasks within the relevant domain’ (see Chapter 2) – offers a greater range of capabilities and depth of use. This raises several distinct access-related risks, with respect to liability and consent, that may disproportionately affect historically marginalised communities. To repeat, in cases where an action can only be executed with an advanced AI assistant, not having access to the technology (e.g. due to limited internet access, not speaking the ‘right’ language or facing a paywall) means one cannot access that action (consider today’s eBay and Ticketmaster bots). Communication with many utility or commercial providers currently requires (at least initial) interaction with their artificial agents (Schwerin, 2023; Verma, 2023a). It is not difficult to imagine a future in which a user needs an advanced AI assistant to interface with a more consequential resource, such as their hospital for appointments or their phone company to obtain service. Cases of inequitable performance, where the assistant systematically performs less well for certain communities (situation type 2), could impose serious costs on people in these contexts. Moreover, advanced AI assistants are expected to be designed to act in line with user expectations. When acting on the user’s behalf, an assistant will need to infer aspects of what the user wants. This process may involve interpretation to decide between various sources of information (e.g. stated preferences and inference based on past feedback or user behaviour) (see Chapter 5). However, cultural differences will also likely affect the system’s ability to make an accurate inference. Notably, the greater the cultural divide, say between that of the developers and the data on which the agent was trained and evaluated on, and that of the user, the harder it will be to make reliable inferences about user wants (e.g. Beede et al., 2020; Widner et al., 2023), and greater the likelihood of performance failures or value misalignment (see Chapter 11). This inference gap could make many forms of indirect opportunity inaccessible, and as past history indicates, there is the risk that harms associated with these unknowns may disproportionately fall upon those already marginalised in the design process."

Emergent access risks

"Emergent access risks are most likely to arise when current and novel capabilities are combined. Emergent risks can be difficult to foresee fully (Ovadya and Whittlestone, 2019; Prunkl et al., 2021) due to the novelty of the technology (see Chapter 1) and the biases of those who engage in product design or foresight processes D’Ignazio and Klein (2020). Indeed, people who occupy relatively advantaged social, educational and economic positions in society are often poorly equipped to foresee and prevent harm because they are disconnected from lived experiences of those who would be affected. Drawing upon access concerns that surround existing technologies, we anticipate three possible trends: • Trend 1: Technology as societal infrastructure. If advanced AI assistants are adopted by organisations or governments in domains affecting material well-being, ‘opting out’ may no longer be a real option for people who want to continue to participate meaningfully in society. Indeed, if this trend holds, there could be serious consequences for communities with no access to AI assistants or who only have access to less capable systems (see also Chapter 14). For example, if advanced AI assistants gate access to information and resources, these resources could become inaccessible for people with limited knowledge of how to use these systems, reflecting the skill-based dimension of digital inequality (van Dijk, 2006). Addressing these questions involves reaching beyond technical and logistical access considerations – and expanding the scope of consideration to enable full engagement and inclusion for differently situated communities. • Trend 2: Exacerbating social and economic inequalities. Technologies are not distinct from but embedded within wider sociopolitical assemblages (Haraway, 1988; Harding, 1998, 2016). If advanced AI assistants are institutionalised and adopted at scale without proper foresight and mitigation measures in place, then they are likely to scale or exacerbate inequalities that already exist within the sociocultural context in which the system is used (Bauer and Lizotte, 2021; Zajko, 2022). If the historical record is anything to go by, the performance inequities evidenced by advanced AI assistants could mirror social hierarchies around gender, race, disability and culture, among others – asymmetries that deserve deeper consideration and need to be significantly addressed (e.g. Buolamwini and Gebru, 2018). • Trend 3: Rendering more urgent responsible AI development and deployment practices, such as those supporting the development of technologies that perform fairly and are accountable to a wide range of parties. As Corbett and Denton (2023, 1629) argue: ‘The impacts of achieving [accountability and fairness] in almost any situation immediately improves the conditions of people’s lives and better society’. However, many approaches to developing AI systems, including assistants, pay little attention to how context shapes what accountability or fairness means (Sartori and Theodorou, 2022), or how these concepts can be put in service of addressing inequalities related to motivational access (e.g. wanting/trust in technology) or use (e.g. different ways to use a technology) (van Dijk, 2006). Advanced AI assistants are complex technologies that will enable a plurality of data and content flows that necessitate in-depth analysis of social impacts. As many sociotechnical and responsible AI practices were developed for conventional ML technologies, it may be necessary to develop new frameworks, approaches and tactics (see Chapter 19). We explore practices for emancipatory and liberatory access in the following section."