Counterfactual Fairness in Machine Learning
Fairness in machine learning has been predominantly studied through global metrics like Demographic Parity and Equalized Odds. These approaches aim to ensure equity between groups defined by one or more categorical sensitive attributes, such as race or gender. While such metrics have become standard in fairness research, they inherently focus on group-level fairness and may […]
Predictive Rate Parity in Machine Learning
Fairness in healthcare machine learning is highly context-dependent. Different use cases, populations, and risks require tailored approaches to fairness, making it essential to align fairness metrics with the specific goals and impacts of each healthcare application. The majority of fairness mitigations in ML include: group fairness (including equalized odds, demographic parity, and predictive rate parity), […]
Conditional Demographic Parity in Machine Learning
The growing need for fairness in machine learning models stems from the increasing reliance on AI for decisions that impact people’s lives, from hiring and lending to healthcare and criminal justice. With these decisions often having profound consequences, ensuring that AI models do not unfairly discriminate against certain groups is essential. However, traditional fairness metrics […]
Equalized Odds in Machine Learning
One concept that has gained significant attention in recent years is “Equalized Odds.” As AI and ML models continue to influence crucial decisions, it is essential that they operate fairly, without inadvertently discriminating against certain groups. Equalized Odds offers a structured way to ensure that predictive models do not favour one demographic over another, but […]
Fairness Metrics Assessments in Machine Learning
While AI systems promise efficiency and accuracy, they also carry the risk of perpetuating and amplifying biases. Consider a retail company using an ML model to predict employee attrition. Initial results showed that women were disproportionately flagged as high-risk for leaving, influenced by historical data linking female employees to higher turnover rates. By applying fairness […]
Human-centred Evaluation for Fairness in Machine Learning
As leaders and managers, you are in a unique position to drive the change needed to ensure fairness in machine learning. By adopting human-centred evaluation practices, engaging diverse stakeholders, and ensuring transparency, you can create AI systems that are not only effective but also ethical and just. In this post, I’ll share insights into the […]
Cross-Validation for Fairness in Machine Learning
Fairness in cross-validation isn’t just a technical detail—it’s a critical component of responsible AI development, ensuring that your models serve all users equitably. As leaders and stakeholders, it’s crucial to ensure that the systems we deploy are not only effective but also equitable. This is where cross-validation for fairness comes into play—a vital but often […]
Disaggregated Performance in Machine Learning
As organisations continue to adopt and implement AI technologies, one of the key challenges they face is ensuring that their systems perform well across all user groups. In an increasingly diverse world, achieving fair and reliable outcomes requires more than just a broad, overarching performance metric. It requires disaggregated performance — the practice of evaluating […]
Disaggregated Evaluation in Machine Learning
As AI systems are increasingly used to make decisions in areas such as hiring, healthcare, finance, and law enforcement, there are substantial risks if these systems inadvertently perpetuate or exacerbate existing biases. The social and ethical implications of these biases are profound—discriminatory algorithms could lead to unfair outcomes, erode trust, and damage the reputation of […]