3,572 research outputs found

    On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse

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    This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse

    In the name of status:Adolescent harmful social behavior as strategic self-regulation

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    Adolescent harmful social behavior is behavior that benefits the person that exhibits it but could harm (the interest of) another. The traditional perspective on adolescent harmful social behavior is that it is what happens when something goes wrong in the developmental process, classifying such behaviors as a self-regulation failure. Yet, theories drawing from evolution theory underscore the adaptiveness of harmful social behavior and argue that such behavior is enacted as a means to gain important resources for survival and reproduction; gaining a position of power This dissertation aims to examine whether adolescent harmful social behavior can indeed be strategic self-regulation, and formulated two questions: Can adolescent harmful social behavior be seen as strategic attempts to obtain social status? And how can we incorporate this status-pursuit perspective more into current interventions that aim to reduce harmful social behavior? To answer these questions, I conducted a meta-review, a meta-analysis, two experimental studies, and an individual participant data meta-analysis (IPDMA). Meta-review findings of this dissertation underscore that when engaging in particular behavior leads to the acquisition of important peer-status-related goals, harmful social behavior may also develop from adequate self-regulation. Empirical findings indicate that the prospect of status affordances can motivate adolescents to engage in harmful social behavior and that descriptive and injunctive peer norms can convey such status prospects effectively. IPDMA findings illustrate that we can reach more adolescent cooperation and collectivism than we are currently promoting via interventions. In this dissertation, I argue we can do this in two ways. One, teach adolescents how they can achieve status by behaving prosocially. And two, change peer norms that reward harmful social behavior with popularity

    INTEGRATED COMPUTER-AIDED DESIGN, EXPERIMENTATION, AND OPTIMIZATION APPROACH FOR PEROVSKITES AND PETROLEUM PACKAGING PROCESSES

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    According to the World Economic Forum report, the U.S. currently has an energy efficiency of just 30%, thus illustrating the potential scope and need for efficiency enhancement and waste minimization. In the U.S. energy sector, petroleum and solar energy are the two key pillars that have the potential to create research opportunities for transition to a cleaner, greener, and sustainable future. In this research endeavor, the focus is on two pivotal areas: (i) Computer-aided perovskite solar cell synthesis; and (ii) Optimization of flow processes through multiproduct petroleum pipelines. In the area of perovskite synthesis, the emphasis is on the enhancement of structural stability, lower costs, and sustainability. Utilizing modeling and optimization methods for computer-aided molecular design (CAMD), efficient, sustainable, less toxic, and economically viable alternatives to conventional lead-based perovskites are obtained. In the second area of optimization of flow processes through multiproduct petroleum pipelines, an actual industrial-scale operation for packaging multiple lube-oil blends is studied. Through an integrated approach of experimental characterization, process design, procedural improvements, testing protocols, control mechanisms, mathematical modeling, and optimization, the limitations of traditional packaging operations are identified, and innovative operational paradigms and strategies are developed by incorporating methods from process systems engineering and data-driven approaches

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    NEMISA Digital Skills Conference (Colloquium) 2023

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    The purpose of the colloquium and events centred around the central role that data plays today as a desirable commodity that must become an important part of massifying digital skilling efforts. Governments amass even more critical data that, if leveraged, could change the way public services are delivered, and even change the social and economic fortunes of any country. Therefore, smart governments and organisations increasingly require data skills to gain insights and foresight, to secure themselves, and for improved decision making and efficiency. However, data skills are scarce, and even more challenging is the inconsistency of the associated training programs with most curated for the Science, Technology, Engineering, and Mathematics (STEM) disciplines. Nonetheless, the interdisciplinary yet agnostic nature of data means that there is opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog

    AI: Limits and Prospects of Artificial Intelligence

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    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

    Sounding the dead in Cambodia: cultivating ethics, generating wellbeing, and living with history through music and sound

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    This dissertation rethinks the ethics of history and trauma in post-genocide Cambodia by examining how Cambodians use a broad repertoire of sounded practices to form relations of mutual care with ancestors, dead teachers, deities, and other predecessors. At its root, the dissertation is the study of an ethical-religious-aesthetic system by which Cambodians recall predecessors’ legacies, care for the dead, and engage ancestors and deities as supportive co-presences. Traditional and popular musics, Buddhist chants and incantations, whispers, and the non-acoustic practice of “speaking in the heart” (niyāy knung citt) are among the primary sounded practices that Cambodians use to engage the dead. Parts One and Two detail those sounded practices and their social implications. I discuss how previous approaches have misinterpreted the nature and capacities of Cambodian music and other ritualized sounds through historicist, colonialist, and secular epistemologies, which cast those sounds as “culture” or “performance” and ignore their capacities as modes of ethics and exchange with the dead. Instead, by rethinking those sounded practices as Cambodian-Buddhist ethics and exchange, I examine how Cambodians fulfill an obligation to care for the ancestors who have supported themselves. I suggest fulfilling that obligation generates personal wellbeing and provides a new model for what living with history can sound like and feel like. Taken together, in Parts One and Two, I detail the non-linear temporalities, types of personhood, ethics, exchange with the dead, and the intergenerational mode of living with history that Cambodians bring into being through music and sound. Part Three zooms further out to discuss how sounded relations with the dead have consequences for national and international politics, which leads to larger critiques of the Cambodian government’s politicization of Khmer Rouge remembrance and international humanitarian efforts that attempt to help Cambodians heal from trauma. Since at least the mid-1990s, a plurality of international activists, scholars, volunteers, and development workers have concluded that Cambodians perpetuate a silence about the Khmer Rouge era that furthers their traumatization. Most observers suggest that Cambodians need to provide public testimony about that violent past in order to heal. This dissertation contests those conclusions, following work in anthropology and trauma studies that problematizes the universalization of the Western psychotherapeutic notion of biomedical trauma and its treatments. I suggest that those calls for a testimonial voice presuppose historicist modes of remembrance and knowledge production that naturalize liberal Western models of personhood, citizenship, justice, wellness, and political agency. To move away from those models, I argue that Cambodian sounded and ritual practices generate what I term “modes of being historical” and “ways of living with history” that are intimate, familial, intergenerational, engage national pasts, and can be a mode of political action. Those “modes of being historical” include but are not limited to telling stories of others’ struggles and deaths. I illustrate how Cambodians have long used a multitude of sounded practices to engage the past, grapple with life’s difficulties, and care for themselves and their ancestors. This dissertation posits that sound studies and ethnomusicology can further the emerging scholarly shifts toward the culturally specific ways people cope with difficult pasts. I propose a new approach to post-violence ethics and history by arguing for the decolonizing possibilities of emphasizing the modes of being historical, ethical relations of mutual care, and ontological entanglements with the dead that Cambodians generate through music and sound

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
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