Page 57 - AI & Machine Learning for Beginners: A Guided Workbook

Real-World Cases of AI Bias

Despite AI’s potential to enhance decision-making, bias in AI
systems has led to real-world consequences, reinforcing
stereotypes and unfair practices. Here are a few notable examples:

Hiring Bias in AI – A resume-screening AI trained on past

hiring data favored male applicants, unintentionally
discriminating against qualified women.

Facial Recognition Errors – Some facial recognition models

have struggled to accurately identify darker-skinned
individuals, leading to misidentifications and concerns about racial
bias in security applications.

Healthcare Disparities – AI models predicting patient risk

levels have sometimes underestimated health issues for certain
demographic groups, limiting their access to proper medical care.

The Path Forward

Ensuring diverse, unbiased training data, conducting ethical AI
audits, and promoting transparency in AI decision-making are
essential steps toward building fair and responsible AI systems.

The Challenge of Bias in AI Systems – Part 2: Concrete
Examples

Despite AI’s promise of efficiency and fairness, bias in AI systems
has led to real-world disparities, often reflecting societal
inequalities embedded in training data.

Facial Recognition Bias Some AI-powered facial recognition

systems have shown higher error rates when identifying
individuals with darker skin tones or women due to a lack of
diverse training data. This has led to misidentifications in
security screenings, raising concerns about fairness and accuracy.

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