From Melting Pots to Misrepresentations: Exploring Harms in Generative AI
This work addresses social harms in AI-as-a-Service systems, which is an incremental contribution to ongoing discussions on bias and representation.
The paper tackles the problem of discriminatory tendencies in generative AI models, particularly favoring majority demographics and harming marginalized groups through distortion and stereotyping, by providing a critical summary of existing research and proposing open-ended questions for future work.
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.