Bias
term_id: bias
Category: ethics_safety
Definition
In AI ethics, bias refers to systematic and unfair discrimination in algorithmic decision-making, often resulting from skewed training data or flawed model design. This can lead to adverse impacts on protected groups based on race, gender, or age. Addressing bias is crucial for ensuring fairness, transparency, and accountability in AI systems, requiring diverse datasets and rigorous auditing processes to mitigate unintended discriminatory effects during deployment.
Summary
Systematic prejudice in AI models that leads to unfair outcomes against certain groups or individuals.
Key Concepts
- Fairness
- Dataset Skew
- Algorithmic Discrimination
- Ethical AI
Use Cases
- Auditing hiring algorithms
- Reviewing loan approval systems
- Analyzing facial recognition accuracy across demographics