3,038 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 peopleā€™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 modelā€™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

    Unleashing the power of artificial intelligence for climate action in industrial markets

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    Artificial Intelligence (AI) is a game-changing capability in industrial markets that can accelerate humanity's race against climate change. Positioned in a resource-hungry and pollution-intensive industry, this study explores AI-powered climate service innovation capabilities and their overall effects. The study develops and validates an AI model, identifying three primary dimensions and nine subdimensions. Based on a dataset in the fast fashion industry, the findings show that the AI-powered climate service innovation capabilities significantly influence both environmental and market performance, in which environmental performance acts as a partial mediator. Specifically, the results identify the key elements of an AI-informed framework for climate action and show how this can be used to develop a range of mitigation, adaptation and resilience initiatives in response to climate change

    Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking

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    Efficiently reviewing scholarly literature and synthesizing prior art are crucial for scientific progress. Yet, the growing scale of publications and the burden of knowledge make synthesis of research threads more challenging than ever. While significant research has been devoted to helping scholars interact with individual papers, building research threads scattered across multiple papers remains a challenge. Most top-down synthesis (and LLMs) make it difficult to personalize and iterate on the output, while bottom-up synthesis is costly in time and effort. Here, we explore a new design space of mixed-initiative workflows. In doing so we develop a novel computational pipeline, Synergi, that ties together user input of relevant seed threads with citation graphs and LLMs, to expand and structure them, respectively. Synergi allows scholars to start with an entire threads-and-subthreads structure generated from papers relevant to their interests, and to iterate and customize on it as they wish. In our evaluation, we find that Synergi helps scholars efficiently make sense of relevant threads, broaden their perspectives, and increases their curiosity. We discuss future design implications for thread-based, mixed-initiative scholarly synthesis support tools.Comment: ACM UIST'2

    Relationship Between Country Culture, Country Demographics, and Restaurant Electronic Word-of-Mouth Valence Ratings

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    Researchers have documented that country culture and country demographics influence electronic word-of-mouth (eWOM) within various industries. Although past research has shown the importance of eWOM to restaurants as a measure of consumer satisfaction, researchers have not established the effect of country culture and country demographics on eWOM within the restaurant industry. Thus, the specific management problem addressed in this quantitative correlational study was the lack of knowledge and understanding regarding the relationship between country culture, country demographics, and restaurant eWOM valence ratings. Grounded in Hofstedeā€™s cultural dimensions theory, the research questions addressed six measures of country culture, 12 measures of country demographics, and their relationship with restaurant eWOM valence ratings. With a purposive sample from the Yelp social media platform, eWOM ratings from 3,659 restaurants in 21 countries were analyzed with correlation analyses and multiple linear regression. Results indicated that a model of five variables and eight two-factor interactions statistically and significantly explained 14.4% of the variance in restaurant eWOM valence ratings. This study may promote positive social change by informing restaurant managers about which aspects of country culture and country demographics relate to restaurant eWOM valence ratings. Restaurant leaders may improve their eWOM response strategies by focusing on the most relevant country culture and country demographic constructs when developing eWOM communication

    A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

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    In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure

    Understanding Agreement and Disagreement in Listenersā€™ Perceived Emotion in Live Music Performance

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    Emotion perception of music is subjective and time dependent. Most computational music emotion recognition (MER) systems overlook time- and listener-dependent factors by averaging emotion judgments across listeners. In this work, we investigate the influence of music, setting (live vs lab vs online), and individual factors on music emotion perception over time. In an initial study, we explore changes in perceived music emotions among audience members during live classical music performances. Fifteen audience members used a mobile application to annotate time-varying emotion judgments based on the valence-arousal model. Inter-rater reliability analyses indicate that consistency in emotion judgments varies significantly across rehearsal segments, with systematic disagreements in certain segments. In a follow-up study, we examine listeners' reasons for their ratings in segments with high and low agreement. We relate these reasons to acoustic features and individual differences. Twenty-one listeners annotated perceived emotions while watching a recorded video of the live performance. They then reflected on their judgments and provided explanations retrospectively. Disagreements were attributed to listeners attending to different musical features or being uncertain about the expressed emotions. Emotion judgments were significantly associated with personality traits, gender, cultural background, and music preference. Thematic analysis of explanations revealed cognitive processes underlying music emotion perception, highlighting attributes less frequently discussed in MER studies, such as instrumentation, arrangement, musical structure, and multimodal factors related to performer expression. Exploratory models incorporating these semantic features and individual factors were developed to predict perceived music emotion over time. Regression analyses confirmed the significance of listener-informed semantic features as independent variables, with individual factors acting as moderators between loudness, pitch range, and arousal. In our final study, we analyzed the effects of individual differences on music emotion perception among 128 participants with diverse backgrounds. Participants annotated perceived emotions for 51 piano performances of different compositions from the Western canon, spanning various era. Linear mixed effects models revealed significant variations in valence and arousal ratings, as well as the frequency of emotion ratings, with regard to several individual factors: music sophistication, music preferences, personality traits, and mood states. Additionally, participants' ratings of arousal, valence, and emotional agreement were significantly associated to the historical time periods of the examined clips. This research highlights the complexity of music emotion perception, revealing it to be a dynamic, individual and context-dependent process. It paves the way for the development of more individually nuanced, time-based models in music psychology, opening up new avenues for personalised music emotion recognition and recommendation, music emotion-driven generation and therapeutic applications

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

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    This ļ¬fth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ļ¬elds 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 modiļ¬ed Proportional Conļ¬‚ict 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 classiļ¬ers, 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, identiļ¬cation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiļ¬cation. 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 classiļ¬cation, and hybrid techniques mixing deep learning with belief functions as well

    Combined Digital Nudging to Leverage Public Transportation Use

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    The urgency of global climate change is becoming increasingly evident, but current mobility patterns in developed countries continue to cause severe environmental damage. Therefore, developed countries need to change their mobility patterns fundamentally, such as modal changes to public transportation instead of private car use. Digital nudging in IT-enabled mobility applications is a novel and promising way to influence modal changes to public transportation. In this study, we conduct an online experiment with 183 participants in which they are being nudged toward public transportation trip options. Our results show that combining two different digital nudges significantly affects the choice of public transportation options. By contrast, single nudges do not lead to significant changes in the choice of public transportation trips. With our findings, we contribute to the research stream of digital nudging and the transportation literature and provide insights for practice to address the adverse effects of current mobility patterns

    The politics of content prioritisation online governing prominence and discoverability on digital media platforms

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    This thesis examines the governing systems and industry practices shaping online content prioritisation processes on digital media platforms. Content prioritisation, and the relative prominence and discoverability of content, are investigated through a critical institutional lens as digital decision guidance processes that shape online choice architecture and influence usersā€™ access to content online. This thesis thus shows how prioritisation is never neutral or static and cannot be explained solely by political economic or neoclassical economics approaches. Rather, prioritisation is dynamically shaped by the institutional environment and by the clash between existing media governance systems and those emerging for platform governance. As prioritisation processes influence how audiovisual media services are accessed online, posing questions about the public interest in such forms of intermediation is key. In that context, this research asks how content prioritisation is governed on digital media platforms, and what the elements of a public interest framework for these practices might be. To address these questions, I use a within case study comparative research design focused on the United Kingdom, collecting data by means of semi-structured interviews and document analysis. Through a thematic analysis, I then investigate how institutional arrangements influence both organisational strategies and interests, as well as the relationships among industry and policy actors involved, namely, platform organisations, pay-TV operators, technology manufacturers, content providers including public service media, and regulators. The results provide insights into the ā€˜black boxā€™ of content prioritisation across three interconnected dimensions: technical, market, and regulatory. In each dimension, a battle between industry and policy actors emerges to influence prioritisation online. As the UK Government and regulator intend to develop new prominence rules, the dispute takes on a normative dimension and gives rise to contested visions of what audiovisual services should be prioritised to the final users, and which private- and public-interest-driven criteria are (or should) be used to determine that. Finally, the analysis shows why it is crucial to reflect on how the public interest is interpreted and operationalised as new prominence regulatory regimes emerge with a variety of sometimes contradictory implications for media pluralism, diversity and audience freedom of choice. The thesis therefore indicates the need for new institutional arrangements and a public interest-driven framework for prioritisation on digital media platforms. Such a framework conceives of public interest content standards as an institutional imperative for media and platform organisations and prompts regulators to develop new online content regulation that is appropriate to changing forms of digital intermediation and emerging audiovisual market conditions. While the empirical focus is on the UK, the implications of the research findings are also considered in the light of developments in the European Union and Council of Europe initiatives that bear on the future discoverability of public interest media services and related prominence regimes
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