Feminism And Algorithmic Bias: Reimagining Gender Justice In The Age Of Artificial Intelligence
- IJLLR Journal
- Apr 25
- 2 min read
Heenal Arvind Jain &Aayushi Deepak Dhanuka, KES Shri’ Jayantilal H. Patel Law College
ABSTRACT
The following paper examines the connection between algorithmic decision- making and feminist legal theory by analyzing the potential impacts that AI technologies might have on existing gender inequalities. Even though algorithms are often perceived to be objective, the following paper argues that the contexts within which they are designed and employed play an important role in their functioning. The paper employs feminist theories of discrimination and oppression to show how seemingly objective technology can produce discriminatory consequences, particularly towards women and other marginalized groups.
Some of the sources of algorithmic biases include the presence of biased training data sets, the employment of proxies, and designs that resemble social stratification. In addition, it focuses on the limitations of traditional antidiscrimination approaches that often fail to adequately address cases of algorithmic bias because of their inability to address systematic and indirect forms of biases. The study considers the effectiveness of legal measures employed to counteract algorithmic discrimination and their ability to address questions of responsibility, accountability, and transparency by means of a comparative analysis of measures employed in the US, India, and the EU.
Ultimately, what the paper is able to show is that addressing algorithmic discrimination entails a lot more than just addressing technical concerns, and instead requires embracing a richer understanding of equality. In addition, the paper stresses that inclusion and participation must be at the center of decision-making processes in terms of developing policy and regulatory regimes, as well as taking steps to include feminism in AI governance.
Keywords: Algorithmic Bias, Feminist Legal Theory, Gender Justice, Artificial Intelligence, Structural Inequality, Intersectionality, AI Governance, Discrimination Law.
