Justice After Identity: Large Language Models and the View from Everywhere

Key points

• The search for a common view of justice and fairness has challenged human collective activity, as our diverging judgments are unavoidably shaped by the self-interests of social position, personal benefit, cultural inheritance, and historical circumstance.1
• John Rawls famously attempted to overcome this limitation through popularizing a philosophical tradition known by the phrase "the original position" - a thought experiment by which people select principles of justice without knowing the identities or advantages they will possess.1
• Perhaps the ethical judgments of LLMs could approximate an integrative original position - a "view from everywhere" generated not by excluding social identities but by computationally incorporating their diversity.It is unlikely that humankind will "hand over the keys" to computational systems by simply delegating complete agentic control of distributive and procedural collective processes.1
• We present data comparing human, base model, and frontier/fine-tuned model judgments about classic moral dilemmas while systematically varying identity relationships and Rawlsian constraints on identity.1

Sources (1)

• [1]Justice After Identity: Large Language Models and the View from Everywhere
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 08:01 PM

  Artificial intelligence engaged to calculate algorithmic and agentic fairness introduces a novel possibility.

  The search for a common view of justice and fairness has challenged human collective activity, as our diverging judgments are unavoidably shaped by the self-interests of social position, personal benefit, cultural inheritance, and historical circumstance.

Extractive summary: sentences quoted from the sources.