Description: Generative AI, known for its ability to generate new content and insights, is reshaping the landscape of human-computer interaction and decision making. Alongside its many benefits, however, generative AI presents unique challenges, particularly in terms of its differentiated impact on people of different genders. This paper seeks to understand how gender bias manifests across the value chain of generative AI, through a comprehensive review of academic work and reports by international organizations. It makes recommendations for policymakers, developers, and deployers for mitigating gender bias and reducing the bias-related harms that emanate from generative AI.
Attribution: Meghna Bal, Mohit Chawdhry and Noyanika Batta. A Literature Review on Gender Bias in Generative AI: Implications for India and Recommendations for the Way Forward. April 2024, Esya Centre.