Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor

By Virginia E. Eubanks. 2018

800 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.

Citations in the corpus

Wasserstein Fair Classification (2019, neutral): “…care, criminal 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…”
A Causal Bayesian Networks Viewpoint on Fairness (2018, neutral): “…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 social services [1, 22].”
Bringing the People Back In: Contesting Benchmark Machine Learning Datasets (2020, neutral): “…systems abound in ways that they have failed people of color (Noble, 2018; Benjamin, 2019), women (Bolukbasi et al., 2016), LGBTQ+ communities (Scheuerman et al., 2019), people with disabilities (Hutchinson et al., 2020; Trewin, 2018), and the working class and those in poverty (Eubanks, 2018).”
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020, background): “…of bias and injustice in algorithmic systems (Angwin et al. 2016a; Benjamin 2019; Buolamwini and Gebru 2018; Lum and Isaac 2016; Noble 2018; Eubanks 2018), exploitative or extractive data practices (Gray and Suri 2019), and applications of AI that dispossess the identity and resources of…”
Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from? (2019, neutral): “tic issues can occur behind closed doors. This is even the case with relatively simple forms of analytics and automation — such as if-then statements, linear regressions, or rule-based expert systems [11, 62]. In contrast, we are concerned with what is and is not taken for granted when developing a ”
On the genealogy of machine learning datasets: A critical history of ImageNet (2021, background): “…of examples of how these systems are failing people of color (Noble, 2018; Benjamin, 2019), women (Bolukbasi et al., 2016), LGBT+ communities (Scheuerman et al., 2019), people with disabilities (Hutchinson et al., 2020; Trewin, 2018), and the working class and those in poverty (Eubanks, 2018).”
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (2022, unknown): “In the government or social services domain, screening tools to identify children at-risk for maltreatment can amplify already-existing biases against poor parents [84, 241].”
A Hierarchy of Limitations in Machine Learning (2020, background): “Eubanks (2018) describes three cases of computer systems for optimizing and automating decisions, detections, and resource allocations.”
The explanation game: a formal framework for interpretable machine learning (2020, unknown): “…et al. 2015); that facial recognition software is often trained on predominantly white subjects, making them inaccurate classifiers for black and brown faces (Buolamwini and Gebru 2018); and that predatory lenders use financial data to disproportionately target poor communities (Eubanks 2018).”
Governing artificial intelligence: ethical, legal and technical opportunities and challenges (2018, unknown): “…example around liability regarding automated vehicles, the limits of current legal frameworks in dealing with ‘big data’s disparate impact’ [2] or preventing algorithmic harms [3], social justice issues related to automating law enforcement or social welfare [4], or online media consumption [5].”
Critical Perspectives on Governance Mechanisms for AI/ML Systems (2020, background): “in response to this upsurge in AI boosterism (Benjamin, 2019; Eubanks, 2018) but have at times struggled to gain traction with policymakers (Crawford et al., 2019).”
Addressing Privacy Threats from Machine Learning (2021, background): “The impact of data-driven surveillance technologies is not borne equally by all; rather, the brunt of these technologies is disproportionately borne by already marginalized people (Marwick and boyd, 2018; Eubanks, 2018; Roberts, 2019).”
How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in India (2022, background): “and fairer decisions than their human counterparts [45, 52], they could also produce harmful outcomes, worsening inequality in society [20, 23, 50, 54, 92, 94, 98].”