From Melting Pots to Misrepresentations: Exploring Harms in Generative AI

TL;DR

This paper critically examines social harms, including discrimination and stereotyping, in generative AI models like Gemini and GPT, emphasizing their impact on marginalized groups and proposing future research directions.

Contribution

It offers a comprehensive summary of social harms in generative AI and introduces open-ended questions to guide future research on mitigating these issues.

Findings

Generative AI models often exhibit biases favoring majority demographics.

Marginalized groups face stereotyping and neglect in AI-generated content.

Current research highlights the need for addressing social harms in AI development.

Abstract

With the widespread adoption of advanced generative models such as Gemini and GPT, there has been a notable increase in the incorporation of such models into sociotechnical systems, categorized under AI-as-a-Service (AIaaS). Despite their versatility across diverse sectors, concerns persist regarding discriminatory tendencies within these models, particularly favoring selected `majority' demographics across various sociodemographic dimensions. Despite widespread calls for diversification of media representations, marginalized racial and ethnic groups continue to face persistent distortion, stereotyping, and neglect within the AIaaS context. In this work, we provide a critical summary of the state of research in the context of social harms to lead the conversation to focus on their implications. We also present open-ended research questions, guided by our discussion, to help define future research pathways.