Work

Towards Safe and Ethical AI

Year: 2025

Type: article

Abstract: As large pre-trained language models grow prevalent, efforts in preventing biased and hateful outputs related to race and gender are increasingly critical. Since initiatives are scattered and fragmented, this review outlines the latest methods for measuring safe, ethical AI and discusses their limitations. By spotlighting the proper utilization and challenges of state-of-the-art methods, this review seeks to foster continuing discourse and innovation among both technical developers and non-technical policymakers.

Source: GRACE: Global Review of AI Community Ethics

DOI: 10.60690/1v7dy054

Authors: Lee, Johann, Lee, Darynne

Institutions: Cornell University, Stanford University

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Gender equality

Open Access status: diamond

OpenAlex ID: W7106548857

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