Disentangling Societal Inequality from Model Biases: Gender Inequality in Divorce Court Proceedings

Divorce is the legal dissolution of a marriage by a court. Since this is usually an unpleasant outcome of a marital union, each party may have reasons to call the decision to quit which is generally documented in detail in the court proceedings. Via a substantial corpus of 17,306 court proceedings, this paper investigates gender inequality through the lens of divorce court proceedings. While emerging data sources (e.g., public court records) on sensitive societal issues hold promise in aiding social science research, biases present in cutting-edge natural language processing (NLP) methods may interfere with or affect such studies. We thus require a thorough analysis of potential gaps and limitations present in extant NLP resources. In this paper, on the methodological side, we demonstrate that existing NLP resources required several non-trivial modifications to quantify societal inequalities. On the substantive side, we find that while a large number of court cases perhaps suggest changing norms in India where women are increasingly challenging patriarchy, AI-powered analyses of these court proceedings indicate striking gender inequality with women often subjected to domestic violence.Comment: This paper is accepted at IJCAI 2023 (AI for good track

Sealant Effectiveness for Children Receiving a Combination of Preventive Methods in a Fluoridated Community: Two-Year Results

A series of preventive methods in combination have been used to reduce dental caries in children including dental health education, prophylaxes, pit and fissure sealants, topical application of fluoride and restorative care. Prophylaxes, sealant, and fluoride procedures are repeated every six months. Two-year results show reductions of occlusal caries increments of 74.3% for first graders and 77.1% for sixth graders. Sealant loss, as defined in this study, varied from 33% to 90% with the highest loss occurring in the newly erupted permanent molars during the first six months of the project. These high sealant loss rates are thought to be related to the age of the population which was designed to include children at the ages of peak eruption of permanent molar teeth (ages six and twelve). These teeth were often only minimally erupted and maintaining the dry field required for sealant retention was extremely difficult. However, in spite of these high rates of sealant loss, caries reduction on occlusal surfaces was highly significant in comparison to that of children who did not receive sealants.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68154/2/10.1177_00220345770560121801.pd

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly

Goal-Derived Categories: The Role of Personal and Situational Goals in Category Representations

Prior research often emphasized a stimulus-based or bottom-up view of product category representations. In contrast, we emphasize a more purposeful, top-down perspective and examine categories that consumers might construct in the service of salient (i.e., highly accessible) goals. Specifically, we investigate how the point of view imposed by salient consumer goals might affect category representations assessed by participants ’ similarity judgments of food products. A key factor in our study is that we examine both individual and situational sources of variability in goal salience. In addition, we also vary the surface-level, visual resemblance of the stimulus pairs of foods used in the study. The results suggest that personal goals (e.g., health) and situational goals (e.g., convenience) act in conjunction and exert a systematic impact on category representations. Both types of goals, when salient, enhanced the perceived similarity of goal-appropriate products and reduced the similarity of product pairs when only one product was ideal for the particular goal. The similarity-enhancing effect was most pronounced when the surface resemblance between the products was low, and the similarity-diminishing effect was more apparent when surface resemblance was high. Implications are discussed for current theoretical assumptions regarding categorization in consumer research

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and implausible predictions to directed attacks by means of malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from so-called safety concerns, properties that preclude their deployment as no argument or experimental setup can help to assess the remaining risk. In recent years, an abundance of state-of-the-art techniques aiming to address these safety concerns has emerged. This chapter provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern machine learning methods. We hope that this contribution fuels discussions on desiderata for machine learning systems and strategies on how to help to advance existing approaches accordingly