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

By R. Marshall. 2018

150 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

Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “Fairness in ML has been explored in popular books [101, 37] and a White House report [99], surveyed in technical review papers [13, 41, 19], tutorials [9, 94, 92, 23], and an in-progress textbook [10], and inspired a number of software packages [129, 39, 45].”
Wasserstein Fair Classification (2019, background): “The increasing use of machine learning in decisionmaking scenarios that have serious implications for individuals and society, such as health care, criminal risk assessment, social services, hiring, financial lending, and online advertising (De Fauw et al., 2018; Dieterich et al., 2016; Eubanks, 201”
Fairness and Abstraction in Sociotechnical Systems (2019, positive): “Concerns about relevant social groups can be obviated by taking seriously the needs of people typically underrepresented in these processes, rather than making assumptions about their welfare [23], and by understanding the power dynamics that prevent these voices from having influence in society to ”
Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from? (2019, neutral): “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].”
Fairness in Machine Learning (2020, unknown): “…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 screening [30, 171], health care [40, 105], social services [4, 48], and education [140, 143, 144].”
The ethics of algorithms: key problems and solutions (2020, unknown): “…al. 2019a, b), financial institutions (Lee and Floridi 2020; Aggarwal 2020) courts (Green and Chen 2019; Yu and Du 2019), local governmental bodies (Eubanks 2017; Lewis 2019), and national governments (Labati et al. 2016; Hauer 2019; Taddeo and Floridi 2018a; Taddeo et al. 2019; Roberts et al.…”
Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning (2019, background): “racism corrupt what could be considered public information resources [66], [67].”
Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context (2020, unknown): “such as fairness, equity, and ethics [55], has been a recurring topic in recent fair-ML research [21, 29, 41, 87, 109].”
Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making (2018, unknown): “As Eubanks has pointed out, unless automated decision-making tools are built to dismantle structural inequities, their use will only intensify them [24].”
Race After Technology: Abolitionist Tools for the New Jim Code (2020, background): “The book makes a timely contribution to a growing corpus of work on critical data studies, and it might be interesting to read it in conjunction with other contemporary books (e.g. D'Ignazio and Klein 2020; Eubanks 2018).”
Neurosymbolic AI: the 3rd wave (2020, unknown): “Section 6 contains more information about the neurosymbolic AI systems in each of the above categories as part of a note which seeks to clarify the terminology being used in AI, ML and DL.”
Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models (2023, unknown): “…of AI models can lead to the creation of AI systems that are more culturally sensitive, contextually relevant, and fair to all users [147, 148, 149, 150, 151, 152, 153] Bringing together experts from diﬀerent ﬁelds, such as computer science, social sciences, humanities, and ethics, can help to…”
Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure (2020, background): “Despite rapid growth, the disciplines of data-driven decision making—including ML—have come under sustained criticism in recent years due to their tendency to perpetuate and amplify social inequality [13, 44].”
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, unknown): “Governmental systems initially designed to help now profile and discriminate the poor [63].”