Engineering a social contract: Rawlsian distributive justice through algorithmic game theory and artificial intelligence

Hutan Ashrafian proposes computational tools for Rawlsian policy selection: can AI and game theory help choose distributions that benefit the least advantaged?^[1]

Method and contribution

The article interprets Veil of Ignorance reasoning and the difference principle through maximin resource allocation. A fictional nation illustrates the proposal; discussion of life-course modeling sketches how data might inform choices. The thought experiment is not a deployed system or evaluated algorithm.^[1]

Alignment relevance and limits

The proposal connects Social Alignment to distributive justice. It acknowledges demanding assumptions about objective data, fairness, modeling life courses, and implementation resources. Privacy, security, accountability, and political legitimacy remain substantive conditions. Formalizing one ethical interpretation does not settle disagreement over justice or authorize automated government.

Compare the experimental Using the Veil of Ignorance to align AI systems with principles of justice and stakeholder-governance approach in Society-in-the-Loop: Programming the Algorithmic Social Contract. See Hutan Ashrafian and Engineering a Social Contract.

Historical context

The publisher records online publication of Engineering a Social Contract on December 22, 2022. The journal issue is November 2023. This milestone uses the version-of-record date, not the issue year.^[1]