FairTargetSim: An Interactive Simulator for Understanding and Explaining the Fairness Effects of Target Variable Definition

Machine learning requires defining one's target variable for predictions or decisions, a process that can have profound implications on fairness: biases are often encoded in target variable definition itself, before any data collection or training. We present an interactive simulator, FairTargetSim (FTS), that illustrates how target variable definition impacts fairness. FTS is a valuable tool for algorithm developers, researchers, and non-technical stakeholders. FTS uses a case study of algorithmic hiring, using real-world data and user-defined target variables. FTS is open-source and available at: http://tinyurl.com/ftsinterface. The video accompanying this paper is here: http://tinyurl.com/ijcaifts.

Algorithmic neutrality

Bias infects the algorithms that wield increasing control over our lives. Predictive policing systems overestimate crime in communities of color; hiring algorithms dock qualified female candidates; and facial recognition software struggles to recognize dark-skinned faces. Algorithmic bias has received significant attention. Algorithmic neutrality, in contrast, has been largely neglected. Algorithmic neutrality is my topic. I take up three questions. What is algorithmic neutrality? Is algorithmic neutrality possible? When we have an eye to algorithmic neutrality, what can we learn about algorithmic bias? To answer these questions in concrete terms, I work with a case study: search engines. Drawing on work about neutrality in science, I say that a search engine is neutral only if certain values, like political ideologies or the financial interests of the search engine operator, play no role in how the search engine ranks pages. Search neutrality, I argue, is impossible. Its impossibility seems to threaten the significance of search bias: if no search engine is neutral, then every search engine is biased. To defuse this threat, I distinguish two forms of bias, failing-on-its-own-terms bias and other-values bias. This distinction allows us to make sense of search bias, and capture its normative complexion, despite the impossibility of neutrality.