How can you ensure your algorithms are fair across different groups using probability?

1 What is algorithmic bias?

Algorithmic bias is the systematic and unfair deviation of an algorithm's output from the expected or desired outcome for some groups of people. For example, an algorithm that predicts the risk of recidivism for criminal defendants might assign higher scores to people of color than to white people, even if they have similar backgrounds and offenses. This can lead to discrimination, injustice, and harm for the affected groups.

The sources of algorithmic bias can range from data quality, data processing, algorithm design, and implementation. Data used to train or evaluate an algorithm might be incomplete, inaccurate, or unrepresentative of the target population or context. The data might be transformed, filtered, or aggregated in ways that introduce or amplify biases, such as using inappropriate proxies, labels, or features. The algorithm might use assumptions, objectives, or methods that favor or penalize certain groups. Lastly, the algorithm might be deployed or used in settings that do not account for the potential impacts or limitations of the algorithm.

2 How can you measure algorithmic bias using probability?

In order to measure algorithmic bias, you must first define what fairness means for your algorithm and its application. There is no universal definition of fairness, as different notions of fairness can be applicable to different scenarios and stakeholders. A common way to approach fairness is to use probabilistic measures that compare the outcomes or experiences of different groups under the algorithm. Examples of such measures include statistical parity, which requires that the algorithm's output is independent of the group membership of the input, conditional parity, which requires that the algorithm's output is independent of the group membership of the input given some relevant factors or conditions, equalized odds, which requires that the algorithm's output is equally accurate for different groups regardless of the actual outcome, and equalized opportunity, which requires that the algorithm's output is equally accurate for different groups given the actual outcome. To calculate these measures, data containing group information, the algorithm's output, and ground truth (if applicable) must be accessed. Statistical tests or confidence intervals can then be used to determine whether observed differences between groups are significant or not.

3 How can you mitigate algorithmic bias using probability?

To mitigate algorithmic bias, you need to identify and address the sources and impacts of the bias throughout the algorithm's lifecycle. Probabilistic methods can assist in this process, such as sampling techniques like stratified sampling or oversampling to ensure data is representative and balanced for the groups of interest. Data augmentation, such as synthetic data or data perturbation, can also be used to increase the diversity and robustness of your data. Exploratory data analysis, such as descriptive statistics or visualization, can be used to detect and understand patterns and distributions of data across groups. Inferential data analysis techniques, such as hypothesis testing or causal inference, can determine and quantify relationships and effects of your data on the algorithm's output. Algorithmic techniques like regularization, optimization, or learning can modify or adapt your algorithm to reduce or eliminate bias. Fairness-aware techniques like fairness constraints, objectives, or corrections can explicitly incorporate fairness criteria into your algorithm. Validation techniques like cross-validation or bootstrapping can estimate and compare performance and fairness of your algorithm on different datasets or scenarios. Evaluation techniques like error analysis or sensitivity analysis can identify and understand errors or biases of your algorithm. Monitoring techniques like logging, auditing, or reporting can track and measure behavior and outcomes of your algorithm in real-world settings. Feedback techniques like user surveys, reviews, or complaints can collect and analyze opinions and experiences of users or stakeholders of your algorithm.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?