Coded Discrimination: Gender Blindspots in Artificial Intelligence
DOI:
https://doi.org/10.25175/jrd/2024/v43/i4/173424Keywords:
Artificial Intelligence, Gender Bias, Ethical, Societal Prejudices, Workforce Diversity.Abstract
The reliance on artificial intelligence (AI) for decision-making has highlighted significant concerns regarding gender bias, as these systems often perpetuate societal prejudices embedded in their training data. Despite a sixfold increase in AI talent from 2016 to 2022, female representation in the AI workforce remains low at 30 per cent. This paper examines how AI conceptualises gender, leading to biases in various domains, and suggests remedies. It emphasises the need for diversity in AI development teams and datasets, fairness in AI systems, and transparency in AI decision-making processes. Studies reveal that gender imbalances significantly affect AI performance, particularly in areas like medical imaging. The paper calls for collaboration between gender theorists and AI technologists to enhance AI inclusivity and equity. Ethical AI development must consider long-term impacts on individuals’ self-worth and societal dynamics, aiming for responsible and unbiased AI systems that contribute positively to society.
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