Hiring by Algorithm: Predicting and Preventing Disparate Impact
By Ifeoma Ajunwa, Sorelle A. Friedler, Carlos Eduardo Scheidegger, Suresh Venkatasubramanian. 2016
67 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Berk Ustun
- Indrė Žliobaitė
- Elayne Ruane
- Bart Custers
- Alexander Spangher
- Abeba Birhane
- Anthony Ventresque
- Yang Liu
Citations in the corpus
- Gender and Dialect Bias in YouTube’s Automatic Captions (2017, background): “Given the evidence that speech analysis tools preform more poorly on some speakers who are members of protected classes, this could legally be discrimination (Ajunwa et al., 2016).”
- Actionable Recourse in Linear Classification (2018, background): “A model should provide recourse to its decision subjects in applications such as lending [40], hiring [2, 9], insurance [38], or the allocation of public services [11, 39].”
- Conversational AI: Social and Ethical Considerations (2019, background): “…as bias is found in: detecting skin tones in pedestrians [52], predictive policing systems and justice [38], the display of STEM career ads [27], recidivism algorithms [2], politics of search engines [21], medical applications [13], automatic speech recognition [44], and in hiring algorithms [1].”
- Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models (2016, background): “For example, automated matching of candidate CVs and job descriptions may propagate ethnicity related biases (Ajunwa et al. 2016).”
- Algorithms, Platforms, and Ethnic Bias: An Integrative Essay Forthcoming in Phylon: The Clark Atlanta University Review of Race and Culture (2018, unknown): “To illustrate, high school attendance, street address, or even first names can be a proxy for race and provide results that are functionally equivalent to those that use race as a variable (Ajunwa et al., 2016).”
- Equal Protection Under the Algorithm : A Legal-Inspired Framework for Identifying Discrimination in Machine Learning (2018, background): “Work in this area generally recognizes that even if protected features are not explicitly included, the feature set may contain correlated features, leading to problematic outcomes (Barocas et al., 2017; Ajunwa et al., 2016).”