Understanding Learning Bias
Learning bias in machine learning arises from the inherent assumptions, design decisions, and optimization goals embedded in model development. These biases can shape how a model interprets and learns patterns from data, often resulting in uneven performance across different groups or contexts. Central to this issue is the choice of objective function, which determines the criteria the model prioritizes during training. For example, while optimizing for accuracy might appear straightforward, it can inadvertently favor majority groups or well-represented data patterns, sidelining minority groups or rare cases. Understanding and addressing learning bias is essential for creating equitable and robust AI systems.
Example of Learning Bias in Machine Learning
Example 1: Differential Privacy and Underrepresented Groups
Imagine a hospital developing a machine learning model to predict disease diagnoses based on patient data. To protect sensitive patient information, the hospital trains the model using differential privacy techniques to ensure that no individual patient’s data can be easily identified or extracted from the model.
By reducing the influence of individual data points, the model struggles to learn patterns from smaller, underrepresented groups (e.g., patients with rare diseases). This leads to worse diagnostic accuracy for these groups compared to a non-private model.
Example 2: Compact Models and Performance Disparities
Consider a company compressing its ML model to deploy it on mobile devices using pruning (removing less critical parts of the model to save memory and computation). The pruned model becomes lightweight and faster for deployment.
The model prioritises learning patterns from the most frequent features in the data. For example, it performs well on common diseases but loses accuracy for rare or less-represented conditions, amplifying disparities in predictions for certain groups.
Impact of Learning Bias in Machine Learning
- Privacy Trade-offs
Positive Impact: Differential privacy protects sensitive information by limiting the model’s ability to expose details about individual data points.
Negative Impact: Reduced ability to learn from underrepresented groups in the data, leading to lower performance and accuracy for these populations.
- Fairness Challenges
Negative Impact: Models optimized for specific goals (e.g., compactness or accuracy) may inadvertently amplify performance disparities, disproportionately affecting underrepresented groups.
Example: A compact model may prioritize learning patterns from frequent features while ignoring those associated with minority groups.
- Accuracy vs. False Positives
Negative Impact: Optimizing for overall accuracy (e.g., using cross-entropy loss) may lead to an imbalance in errors, such as excessive false positives or false negatives, depending on the context.
Example: False positives for rare diseases can cause unnecessary anxiety and resource use, while false negatives could lead to missed diagnoses.
- Underrepresentation Amplification
Negative Impact: Methods like pruning or privacy-focused training can exacerbate the existing imbalance in the dataset by de-prioritizing less common features or groups.
Example: Fewer accurate predictions for rare diseases or minority groups, perpetuating inequities.
- Operational and Ethical Consequences
Negative Impact: Deployment of biased or imbalanced models can harm user trust, reduce the effectiveness of the system, and raise ethical concerns, especially in high-stakes domains like healthcare, finance, or criminal justice.
- Reduced Generalisability
Negative Impact: A model designed for one specific goal may fail to generalize well to other scenarios or populations, limiting its usability in broader contexts.
Designing Mitigations Learning Bias in Machine Learning
Here are some guidelines for learning bias mitigation from Fahse et al (2021) research:
- Adjusting the Objective Function (Targeting Learning Bias):
- Instead of optimising only for overall accuracy, introduce a fairness constraint into the objective function. For example, ensure the model’s accuracy is similar for both urban and rural groups.
- This can be achieved by weighting rural examples more heavily in the loss function to balance their influence during training.
- Addressing Data Representation (Preventing Bias Amplification):
- Change the sampling strategy (s) to ensure rural and urban applicants are equally represented in the training data.
- This ensures the model is exposed to sufficient examples of rural applicants, allowing it to learn patterns from both groups equally.
Key Impact of the Mitigation
- By rebalancing the dataset and redefining the objective function, the model achieves better performance equity, reducing harmful impacts on rural applicants.
- This approach addresses the learning bias while also preventing harm caused by underrepresentation, ensuring fairer loan approval decisions across all groups.
Tackling representation bias requires a systematic, proactive approach.
You can get started with these resources:
Free Resources for Learning Bias Mitigation
Best practices and design considerations for mitigating Learning Bias from problem definition to model deployment. (click Free Downloads)
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Mitigate and resolve 15 Types of Bias specific to your project with detailed guidance from problem definition to model monitoring. Packed with practical methods, research-based strategies, and critical questions specific to your use case. Customised checklists and +75 design cards for every phase in the AI/ ML pipeline
Sources
Fahse, T., Huber, V. and van Giffen, B., 2021. Managing bias in machine learning projects. In Innovation Through Information Systems: Volume II: A Collection of Latest Research on Technology Issues (pp. 94-109). Springer International Publishing.