How do you test your AI/ML models for fairness and bias?
AI and ML models are powerful tools for solving complex problems, but they can also introduce or amplify unfairness and bias in their outputs. This can have negative consequences for individuals and groups who are affected by the decisions or recommendations of these models. For example, a biased model could deny someone a loan, a job, or a medical treatment based on their race, gender, or other characteristics. Therefore, it is important to test your AI/ML models for fairness and bias before deploying them in the real world. In this article, you will learn some basic concepts and methods for doing so.
Fairness and bias are not easy to define or measure, as they depend on the context, the stakeholders, and the ethical principles involved. However, a general way to think about them is to compare how different groups or individuals are treated by the model, and whether this treatment is justified, consistent, and aligned with the intended goals and values. Bias is a deviation from fairness, and it can occur at different stages of the model development, such as data collection, preprocessing, training, evaluation, or deployment.
-
Shrutika P.
VP Lead Data Scientist @ IDFC FIRST Bank | Fraud Detection | Financial Crime
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.
-
John Lunsford, PhD
Leading 0-1 work for personal safety and AI. Cornell | MIT | Oxford
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.
In order to test AI/ML models for fairness and bias, it is necessary to identify the potential sources of bias that could affect the model. Data bias is a common source of bias which occurs when the data used to train or evaluate the model is not representative, balanced, or accurate. It might be missing, skewed, outdated, noisy, or influenced by human errors or prejudices. Algorithmic bias also occurs when the model learns or amplifies biased patterns or associations from the data. This can lead to overfitting, underfitting, poor generalization, or using inappropriate features or metrics. Lastly, deployment bias can result when the model is used or interpreted in ways that are not consistent with its intended purpose or scope. This could include applying it to new populations, domains, or scenarios, or misusing its outputs by users or decision-makers.
-
John Lunsford, PhD
Leading 0-1 work for personal safety and AI. Cornell | MIT | Oxford
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.
-
Shrutika P.
VP Lead Data Scientist @ IDFC FIRST Bank | Fraud Detection | Financial Crime
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.
Once you have identified potential sources of bias, it is necessary to determine the fairness or bias of your model. To do this, you can compare the outcomes or performance of the model across different groups or individuals. Common methods and metrics include disparity measures, which measure the difference or ratio of outcomes or performance between groups; parity measures, which measure the extent to which the outcomes or performance of the model are equal or proportional between groups; and individual measures, which measure the fairness or bias of the model for each individual. For example, you could measure the false positive rate, equal opportunity, individual fairness, counterfactual fairness, or causal fairness of the model for different groups or individuals.
-
John Lunsford, PhD
Leading 0-1 work for personal safety and AI. Cornell | MIT | Oxford
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.
If you find that your model is unfair or biased, you should take action to reduce the bias. This could involve modifying the data, the model, or the deployment. Common strategies and techniques include improving the quality, diversity, or balance of the data used to train or evaluate the model; adjusting the parameters, features, or algorithms of the model to reduce biased patterns; and changing the way the model is used or interpreted in the real world. Examples include collecting more data, removing or correcting noisy or inaccurate data, regularizing or penalizing the model, providing explanations or feedback, and implementing safeguards or policies.
-
John Lunsford, PhD
Leading 0-1 work for personal safety and AI. Cornell | MIT | Oxford
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.
Testing your AI/ML models for fairness and bias is not a one-time task, but a continuous process that requires constant evaluation and improvement. You should test your models at different stages of the model development cycle, such as before, during, and after training, as well as in production. You should also test your models from different perspectives and dimensions, such as technical, ethical, legal, and social. You should use multiple methods and metrics to measure fairness and bias, and be aware of their limitations and trade-offs. You should also involve diverse and relevant stakeholders in the testing process, such as data scientists, domain experts, users, customers, regulators, or affected communities.
-
John Lunsford, PhD
Leading 0-1 work for personal safety and AI. Cornell | MIT | Oxford
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.
-
Rahul Sankrutyan Bhaviri
Spotter! Ex MindTickle|Vera|Rivigo|Amadeus
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.
Rate this article
More relevant reading
-
Artificial IntelligenceHow can you ensure your AI systems produce real-world outcomes?
-
Artificial IntelligenceHow do you address AI bias?
-
Search EnginesHow can you design algorithms that promote fairness in artificial intelligence?
-
Computer ScienceHow can you communicate the limitations and uncertainties of your AI systems to decision-makers?