Author ORCID Identifier
Amol S. Dhaigude: 0000-0003-4563-4026
Abhishek Kumar Jha: 0000-0001-6478-7525
Abstract
In early 2024, Google’s generative AI model Gemini faced global outrage after producing racially inaccurate and historically inconsistent images, such as depicting non-White figures in Nazi-era contexts. The incident exposed deep flaws in AI training, governance, and ethical oversight. It also ignited debate on the limits of fairness and factual accuracy in generative AI systems. Google’s “diversity injection” mechanism, designed to counter data bias, lacked contextual safeguards and led to distorted historical depictions. The backlash caused a $90 billion market value loss and damaged Google’s credibility. Google leadership was forced to address mounting criticism while balancing innovation and responsibility. This case explores how ethical intentions can lead to systemic failures when governance and accountability mechanisms are weak. It highlights the organizational, technical, and societal challenges of deploying responsible AI. The case also examines how tech companies can rebuild trust through transparent design and crisis management.
Recommended Citation
Dhaigude, A. S., Jha, A. K., Ravichandran, A., Marimuthu, M., & Kailaasam, M. (In press). Google’s Gemini Image Generation: AI Bias and the Rewriting of History. Communications of the Association for Information Systems, 59, pp-pp. Retrieved from https://aisel.aisnet.org/cais/vol59/iss1/19
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