Equality of opportunity
- See also: Machine learning terms
Equality of Opportunity in Machine Learning
Equality of opportunity in machine learning refers to the design, implementation, and assessment of algorithms and models that ensure fairness and unbiased outcomes for different subgroups within a given population. This is particularly important when these models are used to make decisions that may have significant impacts on people's lives, such as job applications, loan approvals, or medical diagnoses.
The concern about fairness and equality in machine learning arises from the historical context in which biased data and algorithms have produced unintended consequences. Due to the presence of biases in training data, machine learning models can inadvertently perpetuate or exacerbate existing inequalities. This has led to a growing awareness of the need for fairness in machine learning and the development of techniques to address these challenges.
Several metrics have been proposed to measure fairness in machine learning algorithms, including demographic parity, equalized odds, and equal opportunity. The primary focus of this article is on the concept of equal opportunity, which aims to ensure that an algorithm's true positive rate (TPR) is equal across all protected subgroups within the population. In other words, the proportion of correct positive predictions should be the same for all subgroups, regardless of their background or characteristics.
To evaluate equality of opportunity, researchers typically analyze the confusion matrix of a classifier, which includes the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) for each subgroup. By comparing the TPR across subgroups, one can assess the degree to which the algorithm meets the criterion of equal opportunity.
Techniques for Achieving Equality of Opportunity
Multiple methods have been proposed to achieve equality of opportunity in machine learning models. Some of these techniques include:
- Pre-processing techniques: These methods involve modifying the training data to remove biases before the learning process. Examples include re-sampling or re-weighting instances from underrepresented subgroups to balance the training data.
- In-processing techniques: These approaches adjust the learning algorithm itself, incorporating fairness constraints during model training. Some examples include adversarial training and incorporating fairness regularization terms in the optimization process.
- Post-processing techniques: After a model has been trained, post-processing techniques adjust the predictions or decision thresholds to meet fairness criteria. One common method is to apply different decision thresholds for each subgroup, ensuring that the TPR is equal across all subgroups.
Explain Like I'm 5 (ELI5)
Equality of opportunity in machine learning means making sure that computer programs treat everyone fairly, no matter their background or characteristics. When a computer program is used to help make important decisions, like whether someone gets a job or a loan, it should give everyone an equal chance.
Sometimes, computer programs learn from information that isn't fair to everyone. This can make the computer program not fair, too. To fix this, people who create computer programs can use special techniques to help make the programs treat everyone fairly. They can make sure the information the program learns from is fair, change how the program learns, or adjust the decisions it makes to be more fair.