Equalized odds ensures that a model’s true positive, false positive, and false negative rates are equal across protected groups, like race or gender. For example, in a recidivism prediction model, equalized odds would require that the rates of correctly identifying reoffenders (true positives), incorrectly flagging non-reoffenders (false positives), and failing to identify actual reoffenders (false negatives) are the same for all racial groups.  

This metric helps prevent systematic bias by ensuring that prediction accuracy and error rates are consistent across groups. However, achieving equalized odds is challenging due to trade-offs between fairness and accuracy, especially when groups have different base rates for the outcome. Additionally, it may conflict with other fairness goals and requires complex adjustments to the model, making it difficult to implement in practice.

Individual fairness focuses on ensuring that individuals who are similar in relevant ways receive similar outcomes or treatment from a model. The core idea is that if two people are alike in terms of the characteristics that are important for a decision (e.g., qualifications for a job or creditworthiness), they should receive the same or very similar predictions or results from the model.

To apply individual fairness, it’s crucial to define what “similar” means in the specific context of the model. This involves identifying the features or attributes that are most relevant to the decision-making process. For example, in a loan approval model, relevant similarities might include income, credit score, and employment history. The challenge lies in determining which features are truly important for fairness and ensuring that they are measured accurately and without bias.

Once relevant similarities are defined, the model must be designed to treat individuals who are similar in those respects in a consistent manner. This often requires sophisticated algorithms to ensure that slight differences in unimportant characteristics (like race or gender) don’t lead to vastly different outcomes.

However, implementing individual fairness is difficult because it relies on correctly identifying and quantifying the important features for each specific case. Furthermore, it assumes that the model has access to all the necessary information to make these comparisons fairly. In complex, real-world situations, this can be hard to achieve, making individual fairness a theoretically sound but practically challenging metric to implement.

Counterfactual fairness is a fairness criterion that requires a model’s predictions to remain unchanged if a protected attribute, such as race or gender, is hypothetically altered while keeping all other relevant attributes the same. The idea behind counterfactual fairness is that if a person’s race or gender were different, but everything else about them—such as their qualifications, experience, or income—remained the same, the model should still make the same prediction for them. This ensures that the decision isn’t influenced by the protected attribute, but by legitimate factors related to the outcome.

Achieving counterfactual fairness involves creating a causal model that can simulate how changing the protected attribute would affect the prediction while holding other factors constant. This requires a deep understanding of the relationships between variables in the data and how they are influenced by protected attributes. For instance, in a hiring model, altering gender while keeping experience and education the same means ensuring that gender didn’t unfairly influence the model’s decision.

However, implementing counterfactual fairness is computationally demanding because it requires building these causal models and running simulations to test whether predictions remain consistent when a protected attribute is altered. Additionally, accurately modeling the causal relationships between variables and determining which attributes are truly independent of the protected characteristic can be complex. This makes counterfactual fairness a powerful but resource-intensive approach that may be difficult to implement in practice, especially in large or highly complex datasets.

In-processing methods work during the model training phase, incorporating fairness constraints to adjust the model as it learns. These techniques aim to mitigate bias by ensuring the model is learning fair representations and making decisions based on unbiased patterns.

• Adversarial Debiasing: This technique involves training the model with two objectives simultaneously. The first objective is to perform the primary task (such as classifying candidates in hiring). The second is an adversarial task, where another model is trained to predict sensitive attributes (e.g., race, gender). The goal is to make the adversarial model perform poorly at predicting these attributes. By doing so, the main model is encouraged to make decisions that are less influenced by these protected attributes, leading to fairer outcomes.

Post-processing techniques are applied after a model has been trained. These methods adjust the outputs or decisions of the model to ensure fairness, without changing the model itself. This is often useful when retraining the model isn’t feasible or when the bias is detected after deployment.

• Calibration: Calibration adjusts the model’s prediction probabilities to account for bias. For instance, if a model is systematically overestimating the likelihood of success for one group and underestimating it for another, calibration can adjust these probabilities to make them more accurate and fair across groups. This method helps align the predictions with real-world expectations, improving fairness in decisions like hiring or lending.
• Relabeling: Relabeling involves adjusting the labels assigned by the model based on fairness considerations. If a model’s predictions are skewed for certain groups, this method can relabel outcomes, ensuring a fairer distribution of positive and negative decisions across demographic categories. It’s a straightforward technique often used when there are clear discrepancies in the model’s performance across groups.