How do you test your AI/ML models for fairness and bias?

In AI/ML, fairness means ensuring models like those for credit risk don't embed existing biases. For example, if historical data shows a minority group in the U.S. often denied loans, a model might learn this pattern, unfairly impacting future decisions. It's vital to diversify data, audit models for bias, and implement fairness metrics. This approach ensures decisions are based on individual creditworthiness, not historical prejudices.

When thinking about fairness and bias, it's important to ground the discussion in what those words mean. Fairness: In AI, it is about considering the equitable treatment and outcomes for all groups and individuals, tailoring to the context, like demographic parity or equal opportunity. A robust fairness framework considers those thing in conversation with one another and their tradeoffs. Bias: This is a systematic skew in AI outputs, often disadvantaging certain groups. It can stem from biased data, algorithmic flaws, or problem framing. Mitigating bias is key to preventing reinforcement of societal inequalities. Fairness is an ongoing effort involving diverse data, ethical design, bias checks, and evaluation of social impact.

A few ways of identifying biased sources: Data Examination: Scrutinize the training and testing data. Look for: Representativeness: Does the data accurately reflect real-world diversity? Imbalances: Are some groups under- or over-represented? Historical Biases: Does the data contain biases prevalent in historical or societal contexts? Algorithm Analysis: Evaluate the model's learning process including: Model overcomplexity, Feature selection, Decision boundaries, Model performance, and especially feedback loops when biased predictions made by the model further bias the data it learns from. As well as continuous monitoring and updating: Continuous monitoring is crucial as data, needs, and opportunities evolve over time.

Identifying bias sources in AI/ML, like in a credit risk model, involves several steps: Data Analysis: Scrutinize historical data for patterns of inequality. For instance, if a U.S. bank's past loan data shows higher rejection rates for a minority group, this could signal bias. Algorithmic Review: Examine the model's decision-making process. Does it weigh certain demographic features too heavily? Outcome Evaluation: Regularly assess the model's decisions. Are certain groups consistently disadvantaged? Stakeholder Feedback: Engage with affected groups for insights.

Using a collection of metrics, you can quantitatively assess the fairness and bias of your model. Which metrics to use will depend on your business needs and use cases Disparity Measures such as False Positive/Negative Rates, Accuracy between groups, Parity Measures like Statistical Parity and Predictive Parity. Individual Fairness Measures, Counterfactual Fairness, Causal Fairness Measures, Equal Opportunity and Equality of Odds, and Impact Measures. And Societal Impact measures. These can be more difficult to capture but considering doing so in 1st, 2nd and 3rd order effects.

Model Fairness or Bias remediation strategies can include: Revise Data Collection: Ensure the data is diverse and representative. Address imbalances and missing segments. Feature Engineering: Reassess and modify the features used. Remove or adjust features that contribute to bias. Algorithm Adjustment: Implement algorithms designed to reduce bias, like fairness-aware modeling techniques. Regular Auditing: Continuously monitor the model for biases as new data comes in. Post-processing Techniques: Adjust model to ensure fairer outcomes across different groups. Transparency and Documentation: Maintain clear records of data sources, model decisions, and mitigation strategies.

To test for fairness first define fairness criteria: Determine what fairness means in your context. This may vary based on the domain, legal requirements, and ethical considerations. Identify Sensitive Attributes: Identify attributes like race, gender, age, etc., which are relevant for assessing fairness. Collect and Analyze Data: Ensure the dataset is representative and diverse. Analyze it for potential biases and imbalances like equality of opportunity, demographic parity, or individual fairness. Other techniques include: disaggregated analysis, test for counterfactual fairness, conduct adversarial testing. review model decisions, implement fairness-aware modeling techniques, seek external review, Document Everything.

We had to test a chatbot which returns results based on user inputs. The logic behind was that, the user input would be converted into an sql query and the relevant data retrieved. To test bias, we asked the same question to the chatbot first with no bias keywords and next with some biased keyword. Ideally it should not show a difference. On the other hand, it would be wise to not entertain user inputs which could be offensive or provocative, by giving a standard rejection response.