Individual Fairness

Individual Fairness is a fairness principle that posits that individuals who are similar with respect to the task at hand should receive similar predictions or outcomes from the AI system. Unlike group fairness metrics, which focus on ensuring equitable outcomes across predefined demographic groups, individual fairness emphasizes treating like cases alike at the level of individual data points. The core idea is to ensure consistency and impartiality in the AI's decision-making process for similar individuals, regardless of their group affiliations.

Achieving individual fairness requires defining a meaningful similarity metric that captures the relevant characteristics for the specific prediction task. This metric should quantify how alike two individuals are based on the features that are pertinent to the outcome being predicted. Once a similarity metric is established, individual fairness is achieved to the extent that individuals who are deemed similar by this metric receive similar predictions from the AI model.

Mathematically, if $d(x_i,x_j)$ represents the distance or dissimilarity between two individuals $x_i$ and $x_j$ according to our chosen similarity metric, and $f(x)$ is the prediction function of the AI model, then individual fairness can be informally stated as: if $d(x_i,x_j)$ is small (i.e., $x_i$ and $x_j$ are similar), then the distance between their predictions $|f(x_i)-f(x_j)|$ should also be small. This implies that small differences in relevant features should not lead to large differences in predicted outcomes for similar individuals.

Consider a credit scoring system. According to individual fairness, two loan applicants with very similar financial histories, credit scores, and other relevant factors should receive similar credit risk assessments and loan approval decisions, regardless of their race or gender. If the system assigns significantly different risk scores or approval outcomes to such similar individuals, it would violate the principle of individual fairness.

One of the main challenges in implementing individual fairness lies in defining an appropriate and comprehensive similarity metric. This often requires deep domain expertise and a careful consideration of which features are truly relevant and should contribute to the notion of similarity for the task at hand. A poorly defined similarity metric can inadvertently lead to unfair outcomes or be gamed by manipulating irrelevant features.

Another challenge is ensuring that the AI model respects the defined similarity metric. Standard machine learning algorithms do not inherently optimize for individual fairness. Specific techniques, such as regularizers that penalize dissimilar predictions for similar individuals or learning fair representations that embed similar individuals close to each other in a latent space, are often needed to encourage models to adhere to individual fairness principles.

Individual fairness offers a powerful framework for promoting equity at the individual level and can complement group fairness considerations. While group fairness aims to ensure equitable outcomes across predefined groups, individual fairness strives for consistency and impartiality in treatment for similar individuals, irrespective of their group affiliations. The IBM AI Fairness 360 toolkit includes resources and algorithms that can help in defining similarity metrics and developing models that promote individual fairness.

"Predictive Parity asks: when our AI offers a positive prediction, is that prediction equally reliable, equally true, for everyone?" ✅🎯 - AI Alchemy Hub