Within-country issues: domestic inequality
AI-driven concentration of power and resources within certain entities or groups, especially those with access to or ownership of powerful AI systems, leading to inequitable distribution of benefits and increased societal inequality.
"Our next problem is the fact that the current AI workforce does not evenly represent world demographics. Men from the US and China, working in the US, for US corporations, are disproportionately highly represented [402, 157, 170, 534]. Realizing the full promise of AI requires that people throughout the world and from all social strata are able to use AI and participate in its design and governance. Solving this problem requires addressing unequal access to AI both within countries and across countries."(p. 16)
Sub-categories (2)
Demographic diversity of researchers
"The AI research establishment inherits patterns of under-representation that are dominant in most technical elds. In North America, large parts of professional AI research require a Ph.D., yet less than 25% of Ph.D. computer scientists are women, and fewer than 2% are Black or African American [608]. This holds globally and outside the research community: LinkedIn data suggests that only 22% of AI professionals are women [161]. Since the vast majority of AI practitioners work for private companies, limited corporate statistics on gender and racial diversity hinder a full understanding of the situation [402], but those few statistics that exist are not encouraging: only 5% of Google and 7% of Microsoft employees are Black or African American, with potentially even lower representation at the more senior levels [212, 384]."
6.1 Power centralization and unfair distribution of benefitsPrivatization of AI
"Researchers in deep learning and those with greater research impact are more likely to migrate to industry, raising concerns about the “privatization of AI knowledge” [278]. Specically, if the most sophisticated AI approaches become proprietary and are used only within private research labs, then it will be impossible for universities to teach them, let alone contribute to leading research."
6.1 Power centralization and unfair distribution of benefitsOther risks from Leech et al. (2024) (13)
Harm caused by incompetent systems
7.3 Lack of capability or robustnessHarm caused by unaligned competent systems
7.1 AI pursuing its own goals in conflict with human goals or valuesHarm caused by unaligned competent systems > Specification gaming
7.1 AI pursuing its own goals in conflict with human goals or valuesHarm caused by unaligned competent systems > Emergent goals
7.1 AI pursuing its own goals in conflict with human goals or valuesHarm caused by unaligned competent systems > Deceptive alignment
7.2 AI possessing dangerous capabilitiesBetween-country issues: global inequality
6.1 Power centralization and unfair distribution of benefits