Algorithmic fairness in machine learning (ML) seeks to ensure that ML models are not biased against certain groups of people. This is important because ML models are increasingly being used to make decisions that affect people’s lives, such as whether to grant a loan, hire an employee, or admit someone to college. If these models are biased, they can perpetuate discrimination and inequality.
There are a number of different approaches to algorithmic fairness. One approach is to try to make the ML model itself fair. This can be done by using techniques such as disparate impact analysis, which identifies groups of people who are disproportionately affected by the model’s decisions, and fairness constraints, which are mathematical constraints that the model must satisfy in order to be fair.
Another approach to algorithmic fairness is to focus on the data that is used to train the ML model. This can be done by balancing the data to ensure that it is representative of the population as a whole, or by using techniques such as data augmentation to create synthetic data that is more representative of the population.
Algorithmic fairness is a complex and evolving field. There is no single approach that is guaranteed to work in all cases. However, by carefully considering the different approaches to algorithmic fairness, it is possible to develop ML models that are fair and unbiased.
Here are some of the most common notions of algorithmic fairness:
- Statistical parity: This notion of fairness requires that the model’s predictions be independent of sensitive attributes, such as race or gender. For example, if a model is used to predict whether someone will be approved for a loan, the model should not be more likely to approve loans for white people than for black people.
- Equalized odds: This notion of fairness requires that the model’s predictions have the same accuracy for all groups of people. For example, if a model is used to predict whether someone will pass a test, the model should have the same accuracy for men and women.
- Calibration: This notion of fairness requires that the model’s predictions be accurate for all groups of people. For example, if a model is used to predict the risk of someone defaulting on a loan, the model should be equally accurate for people of all races.
It is important to note that there is no single notion of algorithmic fairness that is universally accepted. The best notion of fairness to use will depend on the specific application.