UNPACKING BIAS IN AI: EXPLORING HOW AND WHY IT OCCURS

​​Before we can delve into the intricacies and specifics of AI ethics and biases, it is crucial to establish a foundational understanding of the biases inherent in AI and the reasons for their existence. This blog post draws insights from two informative articles to shed light on this complex issue. The first article, authored by Karen Hao and published in MIT Technology Review in February 2019, underscores that AI bias cannot be solely attributed to biased training data; instead, it has nuanced origins throughout the deep-learning process. The second article by Jake Silberg and James Manyika at McKinsey, published in June 2019, explores opportunities to mitigate human biases through AI and the pressing need to improve AI systems to prevent the perpetuation of human and societal biases. These articles emphasize that while AI holds the potential to alleviate biases, it also carries the risk of exacerbating them if not managed carefully, making it essential to understand the mechanics of AI bias.

Cause of AI Bias:
AI bias is a multifaceted challenge originating from various sources beyond biased training data. It arises from human biases that impact decision-making, both consciously and unconsciously. Biased data, reflecting historical prejudices or societal inequities, can perpetuate these biases when used to train AI models. Additionally, biases can infiltrate data collection processes, such as oversampling specific demographics due to over-policing. The choices made during algorithm development, like selecting attributes for consideration, can introduce bias, affecting model predictions and fairness. Defining fairness in AI is complex, as there are various definitions with inherent trade-offs between them, making it challenging for AI systems to conform to multiple fairness metrics simultaneously.

Addressing AI Bias:
Mitigating AI bias is an ongoing challenge that demands careful consideration. It involves various strategies and considerations. Using AI to reduce human bias is one approach, enabling more objective decision-making by relying on relevant data rather than subjective factors. Transparency and accountability are vital, requiring organizations to establish processes for testing and mitigating bias in AI systems, including auditing data and models for fairness. Collaboration between humans and AI is essential, with human judgment complementing AI recommendations in decision-making processes. Interdisciplinary collaboration across fields, including ethics and social sciences, is necessary to develop standards for bias and fairness. Encouraging diversity in the AI community can provide unique insights and perspectives in addressing bias issues.

Conclusion:
AI bias is a multifaceted challenge that necessitates a comprehensive approach. While AI has the potential to reduce human biases, it also carries the risk of amplifying them. Achieving fairness and ethics in AI requires ongoing research, interdisciplinary collaboration, transparency, and accountability.

Sources:
Karen Hao, "This is how AI bias really happens—and why it’s so hard to fix," MIT Technology Review, February 4, 2019.
Jake Silberg and James Manyika, "Tackling bias in artificial intelligence (and in humans)," McKinsey Global Institute, June 6, 2019.