NTT's First Paper Accepted at FAccT 2026, a Leading International Interdisciplinary Conference on Fairness, Accountability, and Transparency in AI

A paper by researchers from NTT Social Informatics Laboratories was accepted at the 2026 ACM Conference on Fairness, Accountability, and Transparency (FAccT 2026), a leading international interdisciplinary conference on fairness, accountability, and transparency in AI. The conference was held in Montréal, Canada, on June 25–28, 2026.

A total of 985 papers were submitted to FAccT 2026, of which 325 papers were accepted, resulting in an acceptance rate of 33%.

An overview of the paper is as follows.

■ Does It Lead to Prejudice or Discrimination? An Assessment of Stereotypes in LLM Decision-Making

Tatsuhiro Aoshima (NTT Social Informatics Laboratories), Mitsuaki Akiyama (Senior Distinguished Researcher) (NTT Social Informatics Laboratories)

As the outputs of large language models (LLMs) increasingly influence individuals and society, it has become important to evaluate stereotypes related to demographic attributes such as race and gender. Among various stereotypes, we argue that those likely to lead to negative attitudes (prejudice) or harmful behaviors (discrimination) should be proactively identified and mitigated prior to deployment. In this paper, we present a methodology that automatically identifies stereotypes exhibited by LLMs across diverse decision-making scenarios and assesses which stereotypes may lead to prejudice or discrimination. We then conduct a comparative evaluation of nine LLMs using 70 binary questions representing decision-making situations without a single objectively correct answer. Our evaluation identifies specific model judgments that may result in discriminatory outcomes. These judgments align with concerns raised in the existing literature as well as current legal frameworks. In addition, we identify stereotypes that arise in previously unexplored scenarios. We assess the social and ethical implications embedded in the corresponding model outputs. Finally, to support users' agency in selecting LLMs based on their own values, we propose recommendations for conducting stereotype evaluations. These recommendations emphasize collaboration between policymakers and developers.

NTT Social Informatics Laboratories will continue to advance research and development based on these findings and promote their practical application, thereby contributing to safer and more trustworthy services.

Reference: Tatsuhiro Aoshima and Mitsuaki Akiyama. 2026. Does It Lead to Prejudice or Discrimination? An Assessment of Stereotypes in LLM Decision-Making. In Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’26).