For Auditors - Machine Learning Solutions - Fairness questions to ask

Below is a table outlining questions to ask at various stages of the machine learning (ML) lifecycle to ensure fairness by design:

Stage of ML Lifecycle	Questions to Ask
Data Collection	Are there any biases or imbalances in the training data? Does the data accurately represent the diverse population it will be applied to?
Data Preprocessing	How are missing values handled, and could this introduce bias? Are there any sensitive attributes that should be identified and treated appropriately (e.g., gender, race)?
Feature Engineering	Are the features being used in the model relevant and non-discriminatory? Could the features inadvertently encode bias or unfairly influence the model's predictions?
Model Training	What fairness metrics are being used to evaluate the model during training? Is the training process taking into account fairness constraints and objectives?
Model Evaluation	How does the model perform across different demographic groups? Are there significant differences in model performance that might indicate bias?
Model Deployment	How will the model's predictions impact different groups in the real world? Are there mechanisms in place to monitor the model's performance for fairness post-deployment?
Ongoing Monitoring & Updates	How will the model be monitored for fairness and performance over time? What procedures are in place to address any fairness issues that arise after deployment?

These questions can help guide discussions and decisions to ensure that fairness considerations are integrated throughout the entire ML lifecycle.

Conclusion

In wrapping up, the blog emphasizes the importance of integrating fairness considerations throughout the machine learning lifecycle. The outlined table provides a systematic framework, prompting critical questions at every stage - from data collection to ongoing monitoring and updates. By attentively addressing these inquiries, teams can effectively mitigate biases and uphold fairness in model development and deployment.

This proactive approach not only builds trust in AI systems but also fosters inclusivity, contributing to more responsible and ethical AI practices.