A Comprehensive Framework to Operationalize Social Stereotypes for Responsible AI Evaluations

TL;DR

This paper proposes a unified framework for operationalizing social stereotypes in AI evaluations, integrating social psychological insights with NLP methods to improve responsible AI practices.

Contribution

It introduces a comprehensive framework that captures key stereotype components, enabling more holistic and effective responsible AI evaluations.

Findings

Framework identifies target group, attributes, relationships, perceivers, and context.

Provides guidelines for responsible use of stereotype operationalization.

Enhances understanding of stereotypes' impact on AI outcomes.

Abstract

Societal stereotypes are at the center of a myriad of responsible AI interventions targeted at reducing the generation and propagation of potentially harmful outcomes. While these efforts are much needed, they tend to be fragmented and often address different parts of the issue without adopting a unified or holistic approach to social stereotypes and how they impact various parts of the machine learning pipeline. As a result, current interventions fail to capitalize on the underlying mechanisms that are common across different types of stereotypes, and to anchor on particular aspects that are relevant in certain cases. In this paper, we draw on social psychological research and build on NLP data and methods, to propose a unified framework to operationalize stereotypes in generative AI evaluations. Our framework identifies key components of stereotypes that are crucial in AI evaluation, including the target group, associated attribute, relationship characteristics, perceiving group, and context. We also provide considerations and recommendations for its responsible use.