Rethinking Bias and Fairness in AI Through the Lens of Gender Studies

This paper examines the main approaches that have been put forth to contrast the emergence of biases in AI systems, namely causal, counterfactual reasoning, and constructivist methodology. The objective is to demonstrate the necessity of supplementing this technical solution with a more comprehensive social analysis of the genesis of discriminatory practices. To investigate this sphere, we leverage results from the field of Gender Studies. In particular, we apply the theory of gender performativity as theorized by Judith Butler. This illustrates how AI functions within the social fabric, manifesting patriarchal configurations of gender through an analysis of the notorious case of the COMPAS system for predictive justice. This approach enables an expansion of the interpretation of the concept of fairness, thereby reflecting the complex dynamics of gender production. In conclusion, the gender dimension needs to be reconsidered not as an individual feature but as a performative process. Moreover, it enables the identification of pivotal issues that must be addressed during the design, development, testing, and evaluation phases of AI systems.