Definition
Inductive bias represents the inherent preferences or constraints built into a machine learning model that allow it to generalize from training data to unseen data. Without such biases, a model cannot distinguish between valid patterns and noise. In the context of ethics and safety, understanding inductive bias is crucial because biased assumptions can lead to discriminatory outcomes or unfair predictions, necessitating careful auditing and mitigation strategies to ensure equitable AI behavior.
Summary
The set of assumptions a learning algorithm uses to predict outputs for inputs not seen during training.
Key Concepts
- Generalization
- Assumptions
- Model Constraints
- Fairness
Use Cases
- Designing fair algorithms
- Preventing model overfitting
- Auditing for bias in AI systems