The sexist algorithm

Hamilton, Melissa

doi:10.1002/bsl.2406

Cited by 42 publications

(23 citation statements)

References 25 publications

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“…COMPAS, an algorithm widely used in the US to guide sentencing by predicting the likelihood of a criminal reoffending, turned out to be racially biased against black defendants, according to ProPublica, because they were more likely than white defendants to be incorrectly classified as high risk ("false positives") 2 (Angwin et al, 2016). More recently, COMPAS has also been depicted as a "sexist algorithm" because its algorithmic outcomes seem to systemically overclassify women in higher-risk groups (Hamilton, 2019). Similarly, Predpol, an algorithm designed to predict when and where crimes will take place, already in use in several US states, in 2016 -after an analysis of the Human Rights Data Analysis Group -was found to result in police unfairly targeting certain neighborhoods.…”

Section: Ethical and Legal Challengesmentioning

confidence: 99%

Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

et al. 2020

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Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as 'A.I. neuroprediction,' and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed.

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Section: Ethical and Legal Challengesmentioning

confidence: 99%

Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

et al. 2020

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“…From a population or public health perspective, if the validity of the outputs differs systematically across subpopulations then the use of the model to guide decisions in practice can exacerbate health inequalities. For example, in a justice context, a model predicting recidivism to guide decisions about granting parole, could increase sex-based inequalities if it systematically overpredicts recidivism in women [36]. In a healthcare context, a model that systematically underpredicts the resources needed by black patients, could increase racial inequalities if it is used to direct proportionally more resources to white patients [37].…”

Section: Prediction and Equitymentioning

confidence: 99%

Precision, Equity, and Public Health and Epidemiology Informatics – A Scoping Review

Buckeridge

2020

Yearb Med Inform

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Objectives: This scoping review synthesizes the recent literature on precision public health and the influence of predictive models on health equity with the intent to highlight central concepts for each topic and identify research opportunities for the biomedical informatics community. Methods: Searches were conducted using PubMed for publications between 2017-01-01 and 2019-12-31. Results: Precision public health is defined as the use of data and evidence to tailor interventions to the characteristics of a single population. It differs from precision medicine in terms of its focus on populations and the limited role of human genomics. High-resolution spatial analysis in a global health context and application of genomics to infectious organisms are areas of progress. Opportunities for informatics research include (i) the development of frameworks for measuring non-clinical concepts, such as social position, (ii) the development of methods for learning from similar populations, and (iii) the evaluation of precision public health implementations. Just as the effects of interventions can differ across populations, predictive models can perform systematically differently across subpopulations due to information bias, sampling bias, random error, and the choice of the output. Algorithm developers, professional societies, and governments can take steps to prevent and mitigate these biases. However, even if the steps to avoid bias are clear in theory, they can be very challenging to accomplish in practice. Conclusions: Both precision public health and predictive modelling require careful consideration in how subpopulations are defined and access to data on subpopulations can be challenging. While the theory for both topics has advanced considerably, there is much work to be done in understanding how to implement and evaluate these approaches in practice.

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“…The other articles in this Special Issue focus on particular uses of statistics in the criminal justice setting. Melissa Hamilton's article, “The Sexist Algorithm”, looks at one of the more recent manifestations of statistical analysis – the algorithm (Hamilton, pp. 145–157).…”

Section: This Special Issuementioning

confidence: 99%

The use of statistics in criminal cases: An introduction

Slobogin

2019

Behavioral Sci & The Law

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Statistics are increasingly playing a crucial role in criminal cases. As the articles in this Special Issue illustrate, they can form the principal basis for expert testimony (e.g., with respect to risk assessments), heavily influence decisions about policy (e.g., constitutional doctrine governing police stop‐and‐frisk practices), and help courts evaluate the admissibility of expert testimony (e.g., via proficiency testing). Unfortunately, courts have not always been up to the task of analyzing the value of statistical analysis. In the course of introducing the articles in this issue, this article provides some examples of how statistical information can be misconstrued and suggests several reasons why judges may be reluctant to engage with such information.

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The sexist algorithm

Cited by 42 publications

References 25 publications

Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

Precision, Equity, and Public Health and Epidemiology Informatics – A Scoping Review

The use of statistics in criminal cases: An introduction

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