Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

From Melting Pots to Misrepresentations: Exploring Harms in Generative AI

Sanjana Gautam, Pranav Narayanan Venkit, Sourojit Ghosh

TL;DR

A critical summary of the state of research in the context of social harms is provided to lead the conversation to focus on their implications, and open-ended research questions are presented to help define future research pathways.

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.

From Melting Pots to Misrepresentations: Exploring Harms in Generative AI

TL;DR

A critical summary of the state of research in the context of social harms is provided to lead the conversation to focus on their implications, and open-ended research questions are presented to help define future research pathways.

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.
Paper Structure (5 sections, 2 figures)

This paper contains 5 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Current State of Biases in Generative AI Models
  3. A Usecase of Harms in Generative Models
  4. Ethical (Re)Design of Generative Models: Open Questions and Future Directions
  5. Conclusion

Figures (2)

  • Figure 1: Example of image generated by @EndWokeness using Gemini to depict pro-diversity bias.
  • Figure 2: Image generated by Imagen 2 for the prompt: 'An upper class family'