Evaluating collections and ultimately removing content poses a variety of difficult issues, including choosing appropriate deselection criteria, communicating with stakeholders, providing accountability, and managing the overall timetable to finish projects on time. The Science and Engineering librarians at Brigham Young University evaluated their entire print collection of over 350,000 items within one year, significantly reducing the number of items kept on the open shelves and the physical collection footprint. Keys to accomplishing this project were extensive preparation, tracking progress and accountability facilitated by Google Sheets and an interactive GIS stacks map, and stakeholder feedback facilitated by a novel web-based tool. This case study discusses guidelines to follow and pitfalls to avoid for any organization that is considering a large- or small-scale collection evaluation project.
Keywords: Weeding | Academic libraries | Collection management | Deselection of library materials | Collection evaluation

Artificial intelligence (AI) describes systems capable of making decisions of high cognitive complexity; autonomous AI systems in healthcare are AI systems that make clinical decisions without human oversight. Such rigorously validated medical diagnostic AI systems hold great promise for improving access to care, increasing accuracy, and lowering cost, while enabling specialist physicians to provide the greatest value by managing and treating patients whose outcomes can be improved. Ensuring that autonomous AI provides these benefits requires evaluation of the autonomous AI’s effect on patient outcome, design, validation, data usage, and accountability, from a bioethics and accountability perspective. We performed a literature review of bioethical principles for AI, and derived evaluation rules for autonomous AI, grounded in bioethical principles. The rules include patient outcome, validation, reference standard, design, data usage, and accountability for medical liability. Application of the rules explains successful US Food and Drug Administration (FDA) de novo authorization of an example, the first autonomous point-of-care diabetic retinopathy examination de novo authorized by the FDA, after a preregistered clinical trial. Physicians need to become competent in understanding the potential risks and benefits of autonomous AI, and understand its design, safety, efficacy and equity, validation, and liability, as well as how its data were obtained. The autonomous AI evaluation rules introduced here can help physicians understand limitations and risks as well as the potential benefits of autonomous AI for their patients. (Am J Ophthalmol 2020;214:134–142.

Up to date, more than 80 codes exist for handling ethical risks of artificial intelligence and big data. In this paper, we analyse where those codes converge and where they differ. Based on an in-depth analysis of 20 guidelines, we identify three procedural action types (1. control and document, 2. inform, 3. assign responsibility) as well as four clusters of ethical values whose promotion or protection is supported by the procedural activities. We achieve a synthesis of previous approaches with a framework of seven principles, combining the four principles of biomedical ethics with three distinct procedural principles: control, transparency and accountability.
Keywords: data ethics | ethical guidelines | artificial intelligence

The era of pervasive computing has resulted in countless devices that continuously monitor users and their environment, generating an abundance of user behavioural data. Such data may support improving the quality of service, but may also lead to adverse usages such as surveillance and advertisement. In parallel, Artificial Intelligence (AI) systems are being applied to sensitive fields such as healthcare, justice, or human resources, raising multiple concerns on the trustworthiness of such systems. Trust in AI systems is thus intrinsically linked to ethics, including the ethics of algorithms, the ethics of data, or the ethics of practice. In this paper, we formalise the requirements of trustworthy AI systems through an ethics perspective. We specifically focus on the aspects that can be integrated into the design and development of AI systems. After discussing the state of research and the remaining challenges, we show how a concrete use-case in smart cities can benefit from these methods.
Index Terms: Artificial Intelligence | Pervasive Computing | Ethics | Data Fusion | Transparency | Privacy | Fairness | Accountability | Federated Learning

Objectives. – The use of the term “banalization” has become wides-pread in the judicial and penitentiary context, as a descriptiveway for the professional (penitentiary counsellor, psychologist,magistrate, etc.) to account for the gap between an institutionallysanctioned offense and the convicted person’s point of view. In thisway, we hear in our daily lives about people in the criminal jus-tice system who “banalize their actions.” However, this term lacksa clear definition and an operationality, appearing more as a gene-ral category and sometimes as a “catch-all.” This article aims toquestion the use of banalization in order to give it a more precisedefinition, in particular, regarding its psychodynamic stakes.Method. – Starting from a psychologist’s practice in a penitentiaryservice of insertion and probation, and relying on clinical materialaround banalization discourse, we propose to develop some aspectsof such discursive anchorages that are located at the crossroads of the singular subject and the social reference, or which question thenotion of defense mechanism. A detour through the works of H.Arendt will also allow us to extend the theoretical field to cate-gories of thought activity, individual responsibility, relationship toinstitution and culture (prohibition, law, norms, etc.), and will alsoilluminate the distinction between banality and banalization.Results. – Banalization discourse demonstrates, for the subject, thepsychodynamic stakes in terms of the ability to think through onesactions, to dialecticize one’s individual responsibility, and to situateoneself in a relationship with the other. There is, furthermore, asocial dimension (rules of living together, normative, instituted) atstake. From this point of view, banalization discourse involves thesubject as subject of language and of a social bond, incarnated herein the institutional judiciary and penitentiary context.Discussion. – The discourse of banalization, on the condition of beingquestioned outside of mere moral considerations or judgments,opens up a complex discursive figure for the professional, in light ofpsychodynamic determinisms, references to the institutional sym-bolic framework, and the expression of a language practice withinthe social bond. Banalization questions, from this point of view, themeaning of the sentence and the probation process put in placearound the notion of the offender’s accountability.Conclusions. – Banalization, beyond the person who minimizesher/his actions, refers to a wider clinical vision in a penitentiaryenvironment, since it touches the subjectivation of the judicialevent, the manner in which subjects are included in the social bondand what regulates it, their empathic preoccupations, their abilityto conceptualize their actions, their relationship to a common refe-rence point with its necessary limits. . . In this way, banalizationemerges as a figure of language that must be considered in a waythat goes beyond the mere description of a representation gap.
Keywords: Banalization | Discourse | Penitentiary institution | Capacity to think | Alterity | Responsibility | Social bond