# Algorithmic Bias > [!metadata]- Metadata > **Published:** [[2025-02-09|Feb 09, 2025]] > **Tags:** #🌐 #learning-in-public #artificial-intelligence #ethical-ai #bias-mitigation #cognitive-science Algorithmic bias occurs when AI systems produce prejudiced outcomes due to flawed assumptions or biases in their training data. These biases can lead to unfair or discriminatory results, especially in critical areas like hiring, lending, and law enforcement. ## Sources of Bias 1. **Data Bias**: If the data used to train AI models reflects existing societal prejudices, the AI can learn and perpetuate these biases. For instance, if a hiring algorithm is trained on resumes predominantly from men, it may develop a bias against female applicants. 2. **Algorithmic Bias**: Biases can also stem from the algorithms themselves, especially if they are designed without considering [[Fairness Definitions|fairness]]. This can result in systematically prejudiced outcomes. ## Real-World Impact The consequences of algorithmic bias are significant and far-reaching: - **Healthcare**: Biased AI can lead to disparities in treatment recommendations - **Criminal Justice**: Can result in unfair sentencing or parole decisions (see [[COMPAS Algorithm Case Study]]) - **Employment**: AI-powered hiring tools can perpetuate workplace discrimination - **Financial Services**: Can lead to unfair lending practices and financial exclusion These biases not only perpetuate existing inequalities but can also erode public trust in AI systems. ## Mitigation Strategies Several approaches can be used to address algorithmic bias: 1. [[Adversarial Debiasing]] 2. [[Bias Mitigation Techniques]] 3. [[Causal Inference]] for understanding bias pathways 4. Regular algorithmic audits and monitoring ## Notable Examples 1. [[COMPAS Algorithm Case Study]] - Criminal justice system bias 2. [[Facial Recognition Bias]] - Gender and racial bias in facial recognition systems 3. [[Workday AI Discrimination Case]] - Employment screening bias [Learn more about algorithmic bias and its impact on society](@https://annenberg.usc.edu/research/center-public-relations/usc-annenberg-relevance-report/ethical-dilemmas-ai)