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Source note3 min read

Regulating for AI Legitimacy

Source

arXiv

arxiv.org/abs/2607.24391v1

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Plain-Language Brief

The article discusses the issue of AI governance, specifically focusing on the question of legitimacy rather than just alignment. The author argues that AI systems already have significant influence in areas like speech ranking and applicant filtering, but the question of who has the right to set and enforce their objectives remains unanswered. The article identifies three main sites where AI legitimacy can falter: opacity, private power, and administrative automation. It suggests that law can contribute by ensuring rules are integrated into familiar and credible venues, presented in locally acceptable forms, and subject to contestation with remedies.

What the Document Says

  • AI systems have significant influence in various areas like speech ranking and applicant filtering.
  • The dominant frame for AI governance is alignment, but the article argues that legitimacy should be considered as a separate regulatory objective.
  • Legitimacy here refers to the belief among those subject to power that it is exercised rightfully.
  • Opacity, private power, and administrative automation are identified as sites where AI legitimacy can falter.
  • The article suggests that law can contribute to AI legitimacy by ensuring rules are integrated, presented in familiar forms, and subject to contestation.
  • Thin legality (publicity, stability, consistent application) and thick legality (public authorship of the rules that bind) are discussed as potential solutions.

Key Concepts Mentioned

  • AI governance
  • alignment
  • legitimacy
  • opaqueness
  • private power
  • administrative automation
  • thin legality
  • thick legality

Claims Made by the Document

  • AI systems already have significant influence in various areas.
  • Legitimacy is an autonomous regulatory objective for AI governance.
  • Performance does not necessarily produce the belief among those subject to power that it is exercised rightfully.
  • Opacity, private power, and administrative automation can lead to a lack of AI legitimacy.

Evidence Used

  • The dominance of AI in various areas is implied throughout the text.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

AI systems already govern. They rank speech and allocate attention, filter applicants and triage claims. The dominant frame for AI governance, alignment, asks whether such systems pursue the right objectives safely. It cannot answer a prior question: by what right are those objectives set and enforced? This Article argues that legitimacy is an autonomous regulatory objective, distinct from alignment and not secured by it. Legitimacy here is sociological: the belief among those subject to power that it is exercised rightfully. Performance does not produce that belief. We already have the proof of concept. Social media and search delivered enormous gains on every familiar metric and still triggered a legitimacy crisis, because publics questioned who authorized a handful of firms to set the rules of speech, visibility, and knowledge. It is possible to build a benevolent AI and still face a political crisis over its authority. The Article maps three sites where AI legitimacy falters: opacity, which blocks audiences from forming justified beliefs; private power, where firms exercise public-facing authority without recognizable authorization; and administrative automation, which strains reason-giving, participation, and review inside the state. It then asks what law can contribute. Thin legality (publicity, stability, consistent application) signals non-arbitrariness and buys real recognition, but invites legitimacy-washing when form drifts from practice. Thick legality supplies what form cannot: public authorship of the rules that bind. Three portable principles follow. Integration seats consequential AI rule-setting in venues a polity already treats as authoritative. Familiarity presents rules and reasons in locally credible forms. Contestation guarantees a credible second look with real remedies.