How can you ensure ML models are fair in hiring?

1 What is bias in ML models?

Bias in ML models means that the model produces unfair or inaccurate outcomes for certain groups of people based on their characteristics, such as race, gender, age, or disability. Bias can arise from various sources, such as the data, the algorithm, the metrics, or the context of the application. Bias can harm the individuals and groups affected by the model, as well as the reputation and performance of the organization using the model.

2 How to measure bias in ML models?

To measure bias in ML models, you need to define what fairness means for your specific use case and context. There is no one-size-fits-all definition or metric of fairness, but some common types are statistical parity, equal opportunity, and equalized odds. You can use tools like Fairlearn, AI Fairness 360, or TensorFlow Fairness Indicators to calculate and visualize these metrics for your ML models. For example, statistical parity ensures that the model treats different groups equally in terms of the outcome distribution (e.g. hiring the same proportion of men and women). Equal opportunity means the model treats different groups equally in terms of the true positive rate (e.g. hiring qualified candidates from different groups at the same rate). Lastly, equalized odds ensures that the model treats different groups equally in terms of both the true positive and false positive rates (e.g. hiring and rejecting candidates from different groups at the same rate, given their qualifications).

3 How to mitigate bias in ML models?

To mitigate bias in ML models, you need to identify and address the sources of bias throughout the ML lifecycle, from data collection to model deployment. To do this, you should ensure that your data is representative of the population and domain you are targeting. Collect data from diverse and inclusive sources, and use techniques like sampling, weighting, or augmentation to balance the data. Additionally, remove or transform sensitive or irrelevant features that may introduce bias, such as name, gender, or race. When selecting a model, choose an appropriate algorithm and architecture for your problem and data. Compare different models and evaluate their performance and fairness metrics. Then adjust the model outputs or thresholds to achieve a desired level of fairness. Finally, continuously monitor and audit your model in production to detect and correct any drifts or changes in the data, the model, or the environment. Collect feedback and data from users and stakeholders to improve your model.

4 How to communicate and document your ML model fairness?

To communicate and document the fairness of your ML model, transparency and accountability should be top priorities. Explain how you defined and measured fairness, and list any trade-offs or limitations you faced. Make sure to provide clear and understandable explanations of how your model works, how it makes predictions, and what factors influence the outcomes. Additionally, document your data sources, methods, assumptions, results, and evaluations of your model to make them accessible and auditable. Finally, engage with the users and stakeholders of your model to solicit their feedback and input on your model fairness and impact.

5 How to learn more about ML model fairness and bias?

To learn more about ML model fairness and bias, you can read books, articles, and blogs on the topic, such as Fairness and Machine Learning by Barocas, Hardt, and Narayanan or the Google Responsible AI Practices guide. Additionally, you can take online courses, webinars, or workshops on the topic, such as the Coursera course on Machine Learning for Everyone with Eric Siegel or the Microsoft AI Business School module on Responsible AI. You can also join communities, forums, or events on the topic, like the Partnership on AI, the AI Ethics and Society conference, or the Fairness, Accountability, and Transparency in Machine Learning workshop.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?