Modeling Risk and Achieving Algorithmic Fairness Using Potential Outcomes

By Alan Mishler. 2019

6 citations recorded by the source corpus.

Predictive models and algorithms are increasingly used to support human decision makers, raising concerns about how to ensure that these algorithms are fair. Additionally, these tools are generally designed to predict observable outcomes, but this is problematic when the treatment or exposure is confounded with the outcome. I argue that in most cases, what is actually of interest are potential outcomes. I contrast modeling approaches built around observable vs. potential outcomes, and I recharacterize error rate-based algorithmic fairness metrics in terms of potential outcomes. I also aim to formally model the consequences of using confounded observable predictions to drive interventions.

Subfields: Artificial Intelligence, Economics and Econometrics, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Alexandra Chouldechova
2. Amanda Coston
3. Ashesh Rambachan
4. Emilio Ferrara

In Economics and Econometrics

1. Alexandra Chouldechova
2. Amanda Coston
3. Ashesh Rambachan

In Statistics and Probability

1. Alexandra Chouldechova
2. Amanda Coston
3. Ashesh Rambachan

Citations in the corpus

• Characterizing Fairness Over the Set of Good Models Under Selective Labels (2021, background): “This assumption is based on findings that a counterfactual audit of COMPAS yields equivalent conclusions (Mishler, 2019).”