Summary:
- The article examines the socio-technical origins of AI bias, arguing that algorithmic prejudices are rooted in the cumulative chain of human decisions made during data collection, model design, and implementation rather than being purely technical glitches.
- It highlights the necessity of interdisciplinary approaches—incorporating sociology, ethics, and computer science—to audit AI systems and mitigate systemic inequities embedded within the human-led development lifecycle.