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Carina Prunkl

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2 papers

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IJCAI Conference 2025 Conference Paper

Fairness-Aware Interactive Target Variable Definition

Dalia Gala
Milo Phillips-Brown
Naman Goel
Carina Prunkl
Laura Alvarez Jubete
medb corcoran
Ray Eitel-Porter

Machine learning requires defining one's target variable for predictions or decisions, a process that can have profound implications on fairness, since biases are often encoded in target variable definition itself, before any data collection or training. The downstream impacts of target variable definitions must be taken into account in order to responsibly develop, deploy, and use the algorithmic systems. We propose FairTargetSim (FTS), an interactive and simulations-based approach for this. We demonstrate FTS using the example of algorithmic hiring, grounded in real-world data and user-defined target variables. FTS is open-source; it can be used by algorithm developers, non-technical stakeholders, researchers, and educators in a number of ways. FTS is available at: http: //tinyurl. com/ftsinterface. The video accompanying this paper is here: http: //tinyurl. com/ijcaifts.

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AAAI Conference 2023 Conference Paper

LUCID: Exposing Algorithmic Bias through Inverse Design

Carmen Mazijn
Carina Prunkl
Andres Algaba
Jan Danckaert
Vincent Ginis

AI systems can create, propagate, support, and automate bias in decision-making processes. To mitigate biased decisions, we both need to understand the origin of the bias and define what it means for an algorithm to make fair decisions. Most group fairness notions assess a model's equality of outcome by computing statistical metrics on the outputs. We argue that these output metrics encounter intrinsic obstacles and present a complementary approach that aligns with the increasing focus on equality of treatment. By Locating Unfairness through Canonical Inverse Design (LUCID), we generate a canonical set that shows the desired inputs for a model given a preferred output. The canonical set reveals the model's internal logic and exposes potential unethical biases by repeatedly interrogating the decision-making process. We evaluate LUCID on the UCI Adult and COMPAS data sets and find that some biases detected by a canonical set differ from those of output metrics. The results show that by shifting the focus towards equality of treatment and looking into the algorithm's internal workings, the canonical sets are a valuable addition to the toolbox of algorithmic fairness evaluation.

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