AI and the Social Contract

As artificial intelligence (AI) systems increasingly shape public governance, they challenge foundational principles of political legitimacy. This paper evaluates AI governance against five canonical social contract theories—Hobbes, Locke, Rousseau, Rawls, and Nozick—while examining how structural features of AI strain these theories’ durability. Using a structured comparative framework, the study applies three forms of legitimacy (procedural, moral-substantive, and recognitional) and three types of consent (explicit, tacit, and hypothetical) as normative benchmarks. Applying each theory, the analysis finds AI governance is marked by deficits in accountability, participation, rights protection, fairness, and freedom from coercion, while AI’s opacity, global influence, and hybrid public-private control reveal blind spots within the social contract tradition itself. Though no single theory offers a complete solution and each contains specific weaknesses, the paper develops a hybrid model integrating Hobbesian accountability, Lockean rights protections, Rousseauian participation norms, Rawlsian fairness, and Nozickian safeguards against coercion. The paper concludes by distilling normative priorities for aligning governance with these hybrid contractarian standards: embedding participatory mechanisms, encouraging pluralistic ethical perspectives, ensuring institutional transparency, and strengthening democratic oversight. These interventions aim to reconfigure the social contract—and AI—for an era in which algorithmic systems increasingly mediate the exercise of political authority.

Authors

Institutions

Publication Details

Journal
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Published
2025-10-15
DOI
https://doi.org/10.1609/aies.v8i1.36575
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI and the Social Contract

Daniel Schiff, Chee Kyung Chung
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Ethics and Social Impacts of AI
article

AI and the Social Contract

Daniel Schiff, Chee Kyung Chung
article en

Abstract

As artificial intelligence (AI) systems increasingly shape public governance, they challenge foundational principles of political legitimacy. This paper evaluates AI governance against five canonical social contract theories—Hobbes, Locke, Rousseau, Rawls, and Nozick—while examining how structural features of AI strain these theories’ durability. Using a structured comparative framework, the study applies three forms of legitimacy (procedural, moral-substantive, and recognitional) and three types of consent (explicit, tacit, and hypothetical) as normative benchmarks. Applying each theory, the analysis finds AI governance is marked by deficits in accountability, participation, rights protection, fairness, and freedom from coercion, while AI’s opacity, global influence, and hybrid public-private control reveal blind spots within the social contract tradition itself. Though no single theory offers a complete solution and each contains specific weaknesses, the paper develops a hybrid model integrating Hobbesian accountability, Lockean rights protections, Rousseauian participation norms, Rawlsian fairness, and Nozickian safeguards against coercion. The paper concludes by distilling normative priorities for aligning governance with these hybrid contractarian standards: embedding participatory mechanisms, encouraging pluralistic ethical perspectives, ensuring institutional transparency, and strengthening democratic oversight. These interventions aim to reconfigure the social contract—and AI—for an era in which algorithmic systems increasingly mediate the exercise of political authority.

Proceedings of the AAAI/ACM Conference on AI Ethics and SocietyVol. 8(1)
Purdue University West Lafayette (US)
Purdue University
Openalex Percentile: Top 71%
Ethics and Social Impacts of AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.