To predict and serve?

By Kristian Lum, William M. Isaac. 2016

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In ML Systems & Hardware

Citations in the corpus

Understanding and Evaluating Racial Biases in Image Captioning (2021, background): “This work goes along with others that unveil biases present in existing algorithms [53, 49, 4].”
The Frontiers of Fairness in Machine Learning (2018, background): “here is potential for feedback loops. These are situations where the trained machine learning model informs decisions that then aﬀect the data collected for future iterations of the training process. [LI16] demonstrate how feedback loops might arise in predictive policing if arrest data were used to”
A Causal Bayesian Networks Viewpoint on Fairness (2018, neutral): “This uneven distribution of police attention, as well as other factors such as funding for pretrial services [30, 43], means that diﬀerences in base rates be-tween racial groups are not reﬂective of ground truth rates.”
Causal Reasoning for Algorithmic Fairness (2018, background): “This has spurred the use of machine learning algorithms in predictive policing [25], in loan lending [17], and to predict whether released people from jail will re-oﬀend [9].”
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020, background): “…now available that exposes concerns 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…”
Runaway Feedback Loops in Predictive Policing (2017, methodology): “hoods that police have been sent to by the predictive policing algorithm itself, there is the potential for this sampling bias to be compounded, causing a runaway feedback loop. Indeed, Lum and Isaac [7] have recently shown that this can happen. Lum and Isaac’s work focused on PREDPOL [8], a predict”
Problem Formulation and Fairness (2019, background): “For example, arrests are often used as a proxy for crime in applications of machine learning to policing and criminal justice, even though arrests are a racially biased representation of the true incidence of crime [28].”
Power to the People? Opportunities and Challenges for Participatory AI (2022, background): “…expansion of data enrichment and moderation, problematic applications of machine learning tools in high stakes domains such as criminal justice [10, 57, 73], healthcare [7, 9], and hiring [2, 71] have prompted both researchers, civil society, and regulators to increasingly urge greater use…”
Opportunities in Machine Learning for Healthcare (2018, unknown): “rom existing clinical practice are susceptibletoamplifyingthebiasesendemictomodernhealthcare. Whilenotyetobservedinhealthcare, such feedbackloops have beennoted in the deploymentof predictivepolicing(Lum and Isaac, 2016). Such biases reﬂected in deployed predictions can propagate into future trainin”