Discretion in Hiring Mitchell

By M. Hoffman, Lisa B. Kahn, Danielle Li. 2015

Subfields: AI Ethics, Fairness & Society, Economics & Operations Research

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Citations in the corpus

Wasserstein Fair Classification (2019, background): “…risk assessment, social services, hiring, ﬁnancial lending, and online advertising (De Fauw et al., 2018; Dieterich et al., 2016; Eubanks, 2018; Hoffman et al., 2018; Malekipir-bazari and Aksakalli, 2015; Perlich et al., 2014), is raising concern that bias in the data and model inaccuracies…”
A Causal Bayesian Networks Viewpoint on Fairness (2018, neutral): “…a wide range of decision-making scenarios that have serious implications for individuals and society, including ﬁnancial lending [10, 44], hiring [8, 27], online advertising [26, 38], pretrial and immigration detention [5, 40], child maltreatment screening [13, 45], health care [18, 31], and…”
Path-Specific Counterfactual Fairness (2018, background): “Machine learning is increasingly being used to take decisions that can severely affect people’s lives, e.g. in policing, education, hiring (Hoffman et al., 2015), lending, and criminal risk assessment (Dieterich et al., 2016).”
An algorithm for removing sensitive information: Application to race-independent recidivism prediction (2017, methodology): “Statistical and machine learning models are increasingly used to inform high-stakes decisions, including hiring [Hoffman et al., 2015], credit scoring [Khandani et al.”
A statistical framework for fair predictive algorithms (2016, background): “Examples include hiring [8], predictive policing [13], pre-trial risk assessment of recidivism[6, 2], and risk of violence while incarcerated [5].”
The Impact of Algorithms on Judicial Discretion : Evidence from Regression Discontinuities (2018, background): “Human experts are unwilling to defer to algorithms in many settings (Dietvorst et al., 2015b,a; Hoffman et al., 2015; Yeomans et al., 2017; Logg, 2017; Stevenson, 2017a); this fact is often problematic for human-computer interaction designers.”
Fairness in Machine Learning (2020, background): “Machine Learning (ML) is increasingly used in a wide range of decision-making scenarios that have serious implications for individuals and society, including financial lending [20, 120], hiring [17, 77], online advertising [72, 150], pretrial and immigration detention [10, 158], child maltreatment s”
Experimental evidence on the productivity effects of generative artificial intelligence (2023, background): “Insofar as automation complements existing workers, it can simultaneously benefit workers, capital owners, and consumers by raising productivity and wages and lowering prices (Kleinberg et al., 2018; Hoffman et al., 2018; Agrawal et al., 2019).”
Teacher applicant hiring and teacher performance: Evidence from DC public schools (2016, background): “Recent work has examined the role of personal referrals (Schmutte 2015, Burks et al. 2015), placement agencies (Stanton and Thomas 2013) and, most similar to our setting, objective testing technologies (Hoffman et al. 2015) in the hiring process.”
Generative AI at Work (2023, unknown): “Our paper is related to a large literature on the impact of technological adoption on worker productivity and the organization of work (e.g. Rosen, 1981; Autor et al., 1998; Athey and Stern, 2002; Bresnahan et al., 2002; Bartel et al., 2007; Acemoglu et al., 2007; Hoffman et al., 2017; Bloom et al.,”
Fair Kernel Learning (2017, background): “New social and economic activities massively exploit big data and machine learning algorithms to do inferences, and they decide on the best curriculum to fill in a position [15], to determine wages and in pre-trial risk assessment [4, 9], and to evaluate risk of violence [8].”