There isn't a definitive number of mathematical models for fairness in AI as this is a rapidly evolving field. However, there are several well-known models that are commonly used. These include:

1. Statistical Parity: This model requires that the decision outcomes are independent of the protected attributes. For example, the proportion of men and women receiving a loan should be the same.

2. Equalized Odds: This model requires that the true positive rate and false positive rate are the same for different groups. For example, the proportion of men and women who are correctly identified as being creditworthy should be the same.

3. Predictive Rate Parity: This model requires that the positive predictive value and negative predictive value are the same for different groups. For example, the proportion of men and women who are predicted to be creditworthy and actually are should be the same.

4. Individual Fairness: This model requires that similar individuals should be treated similarly. For example, two individuals with similar credit histories should have the same chance of getting a loan, regardless of their gender.

5. Counterfactual Fairness: This model requires that the decision for an individual would be the same in a counterfactual world where the individual belonged to a different group. For example, if a woman were a man, the decision about her creditworthiness should be the same.

6. Fairness Through Awareness: This model requires that the decision-maker is aware of the protected attributes and makes a decision that is fair given this information.

These are just a few examples and there are many other models and variations of these models. The choice of model depends on the specific context and what is considered fair in that context.

Question

There isn't a definitive number of mathematical models for fairness in AI as this is a rapidly evolving field. However, there are several well-known models that are commonly used. These include:

1. Statistical Parity: This model requires that the decision outcomes are independent of the protected attributes. For example, the proportion of men and women receiving a loan should be the same.

2. Equalized Odds: This model requires that the true positive rate and false positive rate are the same for different groups. For example, the proportion of men and women who are correctly identified as being creditworthy should be the same.

3. Predictive Rate Parity: This model requires that the positive predictive value and negative predictive value are the same for different groups. For example, the proportion of men and women who are predicted to be creditworthy and actually are should be the same.

4. Individual Fairness: This model requires that similar individuals should be treated similarly. For example, two individuals with similar credit histories should have the same chance of getting a loan, regardless of their gender.

5. Counterfactual Fairness: This model requires that the decision for an individual would be the same in a counterfactual world where the individual belonged to a different group. For example, if a woman were a man, the decision about her creditworthiness should be the same.

6. Fairness Through Awareness: This model requires that the decision-maker is aware of the protected attributes and makes a decision that is fair given this information.

These are just a few examples and there are many other models and variations of these models. The choice of model depends on the specific context and what is considered fair in that context.

Knowee AI · Accepted Answer