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Title of the Book: AI Technologies: Know the Inventors, Understand the Inventions, Become Entrepreneurs – 100 AI and Technology Pioneers Who Changed the World

Chapter: 2

DOI: https://doi.org/10.59646/865/02

Authors: T. A. Krithika, J. Lethisia Nithiya and R. Abinaya, Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, GST Road, Vandalur, Chennai, Tamil Nadu, India

Abstract

Modern artificial intelligence historically prioritized architectural scaling over data integrity, operating under the assumption that increasing parameter capacity on uncurated corpora would inevitably yield robust generalized intelligence. Dr. Timnit Gebru dismantled this paradigm by pioneering empirical data-centric AI and systemic algorithmic accountability. Through landmark contributions—including the intersectional Gender Shades study on demographic disparities in facial analysis, the standardized “Datasheets for Datasets” framework, and foundational critiques of uncurated large language models—Gebru demonstrated that model performance is bounded by dataset curation, historical sampling biases, and social asymmetries. This chapter examines Gebru’s scholarly journey, analyzes the mathematical formulations of algorithmic fairness, and explores how data accountability has transitioned into a global enterprise sector spanning continuous compliance auditing, synthetic data generation, and operational governance platforms.

Keywords: Algorithmic Bias, Data-Centric AI, Intersectional Auditing, Datasheets for Datasets, Ethical AI Governance

References

1. Bender, E. M., Gebru, T., McMillan-Major, A., & Mitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM FAccT Conference, 610–623.
2. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research (PMLR), 81, 77–91.
3. Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data, 5(2), 153–163.
4. Crenshaw, K. (1989). Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum, 1989(1), 139–167.
5. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (ITCS), 214–226.
6. Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L., & Fei-Fei, L. (2017). Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. Proceedings of the National Academy of Sciences, 114(50), 13108–13113.
7. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92.
8. Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems (NeurIPS 2016), 29, 3315–3323.
9. Kleinberg, J., Mullainathan, S., & Raghavan, M. (2016). Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807.
10. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT*), 220–229.
11. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
12. Raji, I. D., & Buolamwini, J. (2019). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI products. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES), 429–435.