What are the best ways to design fair and unbiased ML models?

1 Define fairness goals

The first step to design fair and unbiased ML models is to define what fairness means for your specific problem and domain. Fairness is not a universal concept, but rather a relative and contextual one, that depends on the values, norms, and expectations of the stakeholders involved. Therefore, you need to identify and communicate your fairness goals, such as ensuring equal outcomes, opportunities, or treatment for different groups, or minimizing harm or disparity for vulnerable or marginalized groups. You also need to consider the trade-offs and limitations of your fairness goals, such as the potential conflicts between different notions of fairness, or the ethical and legal implications of your choices.

2 Analyze data sources

The second step to design fair and unbiased ML models is to analyze your data sources, which are the main input for your models. Data sources can introduce or reflect biases, such as sampling bias, measurement bias, or representation bias, that can affect the quality and validity of your models. Therefore, you need to assess and document your data sources, such as their origin, collection, processing, and labeling methods, and their relevance, accuracy, and completeness for your problem. You also need to identify and address any data issues, such as missing values, outliers, noise, or imbalances, that can affect the fairness of your models.

4 Validate and monitor models

The fourth step to design fair and unbiased ML models is to validate and monitor your models, which are the output of your algorithms. Models can produce or reveal biases, such as prediction bias, outcome bias, or feedback bias, that can affect the reliability and accountability of your models. Therefore, you need to validate and monitor your models, such as their accuracy, precision, recall, or fairness metrics, and their errors, uncertainties, or risks, and compare them against your fairness goals and expectations. You also need to collect and incorporate feedback from your users, customers, or experts, and update or improve your models accordingly.

5 Explain and communicate models

The fifth step to design fair and unbiased ML models is to explain and communicate your models, which are the interface of your models. Models can hide or obscure biases, such as complexity bias, transparency bias, or trust bias, that can affect the understanding and acceptance of your models. Therefore, you need to explain and communicate your models, such as their inputs, outputs, logic, or limitations, and their rationale, evidence, or counterfactuals, and make them understandable, interpretable, and actionable for your audience. You also need to disclose and justify your fairness decisions, such as your data sources, algorithms, or validation methods, and their implications and trade-offs.

6 Follow ethical principles

The sixth and final step to design fair and unbiased ML models is to follow ethical principles, which are the foundation of your models. Ethical principles can guide or challenge biases, such as moral bias, social bias, or cognitive bias, that can affect the values and goals of your models. Therefore, you need to follow ethical principles, such as fairness, accountability, transparency, or privacy, and align them with your problem, domain, and stakeholders. You also need to adhere to ethical standards, such as codes of conduct, guidelines, or frameworks, and comply with ethical regulations, such as laws, policies, or rules, that apply to your models.

7 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?