Equal Justice: A statement from Civis Analytics
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Uncategorized | 2.25.20

Reducing bias and ensuring fairness in data science

Henry Hinnefeld
Senior Data Scientist

Here at Civis, we build a lot of models. Most of the time we’re modeling people and their behavior because that’s what we’re particularly good at, but we’re hardly the only ones doing this — as we enter the age of “big data” more and more industries are applying machine learning techniques to drive person-level decision-making. This comes with exciting opportunities, but it also introduces an ethical dilemma: when machine learning models make decisions that affect people’s lives, how can you be sure those decisions are fair?

Defining “Fairness”

Group vs. Individual Fairness

Balanced vs. Imbalanced Ground Truth

Sample Bias vs. Label Bias in your Data

Sample bias occurs when the data-generating process samples from different groups in different ways. For example, an analysis of New York City’s stop-and-frisk policy found that Black and Hispanic people were stopped by police at rates disproportionate to their share of the population (while controlling for geographic variation and estimated levels of crime participation). A dataset describing these stops would contain sample bias because the process by which data points are sampled is different for people of different races. Sample bias compromises the utility of accuracy as well as ratio-based comparisons, both of which are frequently used in definitions of algorithmic fairness.

Recommendations for Data Scientists

Think about the ground truth you are trying to model

Think about the process that generated your data

Keep a human in the loop, if your model affects people’s lives.

Machine learning is a powerful tool, and like any powerful tool it has the potential to be misused. The best defense against misuse is to keep a human in the loop, and it is incumbent on those of us who do this kind of thing for a living to accept that responsibility.