Definition
Bias in AI refers to systematic errors in AI outputs caused by skewed data, flawed design, or societal inequities reflected in datasets. It can lead to unfair or discriminatory outcomes.
Purpose
The purpose of studying bias is to identify and mitigate unfairness in AI systems. Organizations aim to build more equitable models by addressing these issues.
Importance
- Leads to discrimination in hiring, lending, or healthcare if unaddressed.
- Undermines trust in AI systems.
- Requires regulatory compliance in sensitive industries.
- Related to fairness and responsible AI practices.
How It Works
- Identify potential sources of bias (data collection, labeling, modeling).
- Analyze datasets for imbalance.
- Apply fairness-aware training methods.
- Test outputs with fairness metrics.
- Adjust design and retrain if necessary.
Examples (Real World)
- COMPAS risk assessment tool: criticized for racial bias.
- Amazon hiring algorithm: discarded due to gender bias.
- Facial recognition: known to misclassify certain demographic groups.
References / Further Reading
- AI Bias — NIST.
- Fairness and Machine Learning — Barocas, Hardt, & Narayanan (book).
- Algorithmic Bias — ACM FAccT Conference Proceedings.