Data Bias

Data Bias is unfair representation in training data that causes AI systems to perform unevenly across groups or contexts. Sources include historical data reflecting past discrimination (lending records biased by decades of redlining), sampling that under-represents certain populations (medical studies focused on male patients), labeling decisions influenced by labeler demographics, and missing data when certain groups don't use a service. Famous examples include early facial-recognition systems trained predominantly on light-skinned faces, voice-recognition systems trained mostly on male voices, and language models trained on internet content reflecting demographic skew. Mitigation strategies include rebalancing datasets, collecting additional data from underrepresented groups, using fairness-aware learning algorithms, and rigorously auditing model performance per group. AI governance frameworks require data documentation (datasheets for datasets) and fairness analysis as part of AI compliance and AI risk management. Detecting and mitigating data bias is foundational to responsible AI and AI ethics in every enterprise AI program at scale.

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