Bias and fairness
Within ML, bias and fairness are not just ethical considerations but critical factors that can significantly impact the effectiveness of your ML models. We have already encountered bias in how it is related to underfitting and overfitting. We will now explore bias in the context of how the model fully and accurately represents all groups within the data – for example, different demographic groups within a dataset of customers.
Understanding bias
Bias in ML refers to a model’s tendency to systematically make errors due to limitations in the training data or the model’s design. This could be due to various reasons, including the following:
- Inadequate or unrepresentative training data: If your dataset doesn’t fully capture the complexities and diversity of the real world, your model might make inaccurate assumptions
- Inherent prejudices in the data collection process: If there are historical biases embedded in the way data...
