In this paper, we study the effects of using an algorithm-based risk assessment instrument (RAI) to support the prediction of risk of violent recidivism upon release. The instrument we used is a machine learning version of RiskCanvi used by the Justice Department of Catalonia, Spain. It was hypothesized that people can improve their performance on defining the risk of recidivism when assisted with a RAI. Also, that professionals can perform better than non-experts on the domain. Participants had to predict whether (...) a person who has been released from prison will commit a new crime leading to re-incarceration, within the next two years. This user study is done with (1) general participants from diverse backgrounds recruited through a crowdsourcing platform, (2) targeted participants who are students and practitioners of data science, criminology, or social work and professionals who work with RisCanvi. We also run focus groups with participants of the targeted study, including people who use RisCanvi in a professional capacity, to interpret the quantitative results. Among other findings, we observe that algorithmic support systematically leads to more accurate predictions from all participants, but that statistically significant gains are only seen in the performance of targeted participants with respect to that of crowdsourced participants. Among other comments, professional participants indicate that they would not foresee using a fully-automated system in criminal risk assessment, but do consider it valuable for training, standardization, and to fine-tune or double-check their predictions on particularly difficult cases. We found that the revised prediction by using a RAI increases the performance of all groups, while professionals show a better performance in general. And, a RAI can be considered for extending professional capacities and skills along their careers. (shrink)

Background Q-CEP (Qualificação dos Comitês de Ética em Pesquisa que compõem o Sistema CEP/Conep) is a nationwide project resulting from a partnership between the Brazilian National Research Ethics Commission (Conep), the Ministry of Health and Hospital Moinhos de Vento (HMV). It was developed to consolidate policy for ethical review of research with human beings in all members of the CEP/Conep System, Brazil’s national system of institutional review boards. The aim of this study was therefore to report on the experience and (...) results of the Q-CEP project. Methods An observational, retrospective study includes data from the Q-CEP, obtained from visits to all the institutional research ethics committees (RECs) in the country. The actions implemented by Q-CEP were part of a two-step process: (i) training visits to each REC; (ii) development of distance learning modules on strategic topics pertaining to research ethics evaluation. The data presented herein cover step one (training visits), defined by Q-CEP as the diagnostic stage of the project. For a country with social and economics inequalities such as Brazil, this is a particularly important stage; an accurate picture of reality is needed to inform planning of quality improvement strategies. Results In 2019–2021, Q-CEP visited 832 RECs and trained 11,197 people. This sample covered almost all active RECs in the country; only 4 (0.5%) were not evaluated. Of the 94 items evaluated, 62% did not reach the target of at least 80% compliance and around 1/4 (26%) were below 50% compliance. The diagnostic stage of the process revealed inadequacies on the part of the RECs in their ethical reviews. The analysis of informed consent forms showed compliance in only 131 RECs (15.74%). The description of pending issues made by RECs in their reports was compliant in 19.33% (n = 161). Administrative and operational aspects were also considered inadequate by more than half of the RECs. Conclusions Overall, Brazilian RECs showed poor compliance in several aspects of their operation, both in ethics evaluation and in other processes, which justifies additional training. The Q-CEP project is part of a quality improvement policy promoted by the Brazilian Ministry of Health. The data obtained in the diagnostic step of the project have contributed to the qualification and consolidation of one of the world’s largest research ethics evaluation systems. (shrink)

This volume brings together eminent theologians, philosophers and political theorists to discuss such questions as how religious understandings have shaped the moral landscape of contemporary culture; the possible contributions of theology and theologically informed moral argument to contemporary public life; the problem of religious and moral discourse in a pluralistic society; and the proper relationship between religion and culture.

In this paper we investigate risk prediction of criminal re-offense among juvenile defendants using general-purpose machine learning algorithms. We show that in our dataset, containing hundreds of cases, ML models achieve better predictive power than a structured professional risk assessment tool, the Structured Assessment of Violence Risk in Youth, at the expense of not satisfying relevant group fairness metrics that SAVRY does satisfy. We explore in more detail two possible causes of this algorithmic bias that are related to biases in (...) the data with respect to two protected groups, foreigners and women. In particular, we look at the differences in the prevalence of re-offense between protected groups and the influence of protected group or correlated features in the prediction. Our experiments show that both can lead to disparity between groups on the considered group fairness metrics. We observe that methods to mitigate the influence of either cause do not guarantee fair outcomes. An analysis of feature importance using LIME, a machine learning interpretability method, shows that some mitigation methods can shift the set of features that ML techniques rely on away from demographics and criminal history which are highly correlated with sensitive features. (shrink)

Faces provide information about a person's identity, as well as their sex, age, and ethnicity. People also infer social and personality traits from the face — judgments that can have important societal and personal consequences. In recent years, deep convolutional neural networks (DCNNs) have proven adept at representing the identity of a face from images that vary widely in viewpoint, illumination, expression, and appearance. These algorithms are modeled on the primate visual cortex and consist of multiple processing layers of simulated (...) neurons. Here, we examined whether a DCNN trained for face identification also retains a representation of the information in faces that supports social‐trait inferences. Participants rated male and female faces on a diverse set of 18 personality traits. Linear classifiers were trained with cross validation to predict human‐assigned trait ratings from the 512 dimensional representations of faces that emerged at the top‐layer of a DCNN trained for face identification. The network was trained with 494,414 images of 10,575 identities and consisted of seven layers and 19.8 million parameters. The top‐level DCNN features produced by the network predicted the human‐assigned social trait profiles with good accuracy. Human‐assigned ratings for the individual traits were also predicted accurately. We conclude that the face representations that emerge from DCNNs retain facial information that goes beyond the strict limits of their training. (shrink)