AI & Data
Best Practice: Assess model performance across demographic groups for fairness
Sep 12, 2024
AI models can sometimes exhibit unintended bias, producing results that disproportionately favour or disadvantage certain demographic groups. To build fair and equitable systems, it’s critical to assess model performance across various subgroups and address any discrepancies. This practice helps prevent discrimination and ensures that AI models are unbiased and just.
Why Fairness Assessments Matter
- Preventing discrimination: AI models trained on unbalanced or biased datasets can unintentionally favor certain groups over others, leading to discriminatory outcomes. Fairness assessments help identify and rectify these biases.
- Ensuring ethical AI: Fairness is a core principle of ethical AI. Evaluating performance across demographic groups demonstrates a commitment to building AI systems that are inclusive and just.
- Building trust with users: When users see that AI systems are fair and unbiased, it fosters trust. In industries like finance, healthcare, or hiring, fairness is particularly crucial to maintaining credibility and compliance with regulations.
- Mitigating legal and reputational risks: Biased models can result in legal challenges or reputational damage. Conducting fairness assessments minimises the risk of adverse consequences.
Implementing This Best Practice
- Use fairness evaluation tools: Tools like Fairlearn or AI Fairness 360 can automatically assess model performance across different demographic groups. These tools provide fairness metrics that help identify biases and disparities.
- Analyse key subgroups: Identify key demographic groups relevant to your use case, such as gender, age, ethnicity, or socioeconomic status. Evaluate the model’s performance for each subgroup and check for significant differences in accuracy or outcomes.
- Adjust training data and algorithms: If discrepancies in model performance are found, consider rebalancing the training data or adjusting the model algorithms. This may involve increasing the representation of underrepresented groups in the dataset or using bias mitigation techniques during training.
- Document fairness assessments: Maintain comprehensive records of your fairness assessments, detailing the steps taken to identify and mitigate bias. This documentation is critical for transparency and accountability.
Conclusion
Assessing AI model performance across demographic groups is essential to ensuring fairness and preventing bias. By using tools like Fairlearn or AI Fairness 360 and adjusting training data or algorithms, organisations can build more inclusive and equitable AI systems. Regular fairness assessments contribute to ethical AI practices, protecting against discrimination and enhancing public trust.
