Once we learned how to evaluate models, it’s crucial to recognize that a high-performing model can still be unfair. This lesson explores bias and fairness, showing how models can inherit biases from data and how we can work to ensure equitable outcomes across different groups.
In ML, bias means systematic errors that lead to unfair outcomes. It often comes from the data. For example, if a face recognition model is trained mostly on light-skinned faces, it may fail on darker-skinned faces.
This kind of bias means the model works unevenly across different groups, resulting in discriminatory or inaccurate predictions for certain populations.
Real examples include a hiring tool that learned to prefer male candidates because the past data had more men, or a criminal risk model that flagged certain minority groups unfairly due to biased historical data.
These biases happen when the training data reflects historical prejudices or imbalances. The model learns and perpetuates these unfair patterns.
Fairness means the model should treat different groups equitably. Ideally, it should have similar accuracy for different genders, races, etc.
Achieving fairness can be challenging. It requires checking performance across groups and, if needed, adjusting the model or data to treat them equally.
To reduce bias, collect diverse and representative data. For instance, include equal representation of different skin tones in a face dataset.
Sometimes we remove sensitive attributes (like race) from the model, or we re-balance the data. Addressing bias is crucial for creating ethical and trustworthy ML systems.
Which Of These Is An Example Of Dataset Bias Affecting A Model?
If a model systematically favors one group over another, this indicates an issue of ______.
Think of a scenario (like an image tagging system). How might biased data lead it to mislabel images of certain people or objects? What steps could you take (data collection or model changes) to make it more fair?
Excellent work! You now understand bias
and fairness in machine learning and how to build more ethical AI systems.