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.