When Is Discrimination Wrong

By Deborah Hellman. 2008

204 citations recorded by the source corpus.

Subfields: Law

How researchers see this paper

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

In Law

1. Reuben Binns
2. Joshua A. Kroll
3. Emily M. Bender
4. Michael Veale
5. Nitin Kohli

Citations in the corpus

• Fairness in Machine Learning: Lessons from Political Philosophy (2017, background): “This might be the case for airport security checks, where it is important to a sense of social solidarity that no group is over-examined as a result of a predictive system, even if there really are differ-ences in base rates Hellman (2008).”
• This Thing Called Fairness (2019, background): “Hellman draws distinctions between fairness and justice in understanding the underpinnings of why discrimination is a moral harm [62].”
• A Hierarchy of Limitations in Machine Learning (2020, unknown): “Documented examples of mistakes in scientific results mostly have to do with data handling, prior to going into software that does analysis and model-building (Miller, 2006; Karraker and Latham, 2015), especially when Excel is involved (Panko, 1998; Hutson, 2010; Herndon et al., 2013). But mistakes ”
• Fairness without Harm: Decoupled Classifiers with Preference Guarantees (2019, background): “Parity is an appropriate notion of fairness for applications such as hiring or sentencing, where a model that exhibits disparate treatment or disparate impact may be viewed as a system to perpetrate wrongful discrimination (see Arneson, 2006; Hellman, 2008; Barocas & Selbst, 2016).”
• Bias in word embeddings (2020, background): “Regardless of their polarity, they are always a result of group antagonism [41, 54, 97].”
• On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 (2021, background): “gation, denigration, belittlement, loss of opportunity [3, 4, 56] and others on the part of those discriminated against.”
• Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data (2017, background): “Some forms of discrimination however are considered unacceptable (Hellman, 2008).”