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作者：Heinrich, Bernd; Klier, Mathias; Obermeier, Andreas; Schiller, Alexander
作者单位：University of Regensburg; Ulm University
摘要：The importance of data quality assessment, in general, and duplicate detection, in particular, has been recognized in both research and practice. Duplicates are known to cause critical problems in many domains, including customer relationship management, data management and data warehousing, fraud detection, production, and healthcare. Such duplicates are caused by events that are typically associated with data patterns in the records. For example, duplicates in customer databases caused by th...
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作者：Grisold, Thomas; Berente, Nicholas; Seidel, Stefan
作者单位：Vienna University of Economics & Business; University of St Gallen; University of Notre Dame; University of Cologne
摘要：Human-AI ecologies involve human and AI-based agents that coordinate their interactions in part by following social norms. Social norms, therefore, are important for establishing the guardrails that ensure desirable interactions in a way that is consistent with essential values, such as human safety. Managing human-AI ecologies requires specifying norms to enable coordination in known situations but also allowing for the emergence of norms to enable coordination in unspecified, unstructured si...
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作者：Kane, Gerald C.; Alavi, Maryam; Labianca, Giuseppe (Joe); Borgatti, Stephen P.
作者单位：University System of Georgia; University of Georgia; University System of Georgia; Georgia Institute of Technology; University of Massachusetts System; University of Massachusetts Amherst; University of Exeter; University of Kentucky
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作者：Wei, Dongjun; Chau, Michael; Li, Zhepeng (Lionel)
作者单位：University of Hong Kong
摘要：Hate speech is a major problem on social media platforms. Automatic hate speech detection methods relying on machine learning models, which learn from manually labeled datasets, have been proposed in both academia and industry. However, there is increasing evidence that hate speech detection datasets labeled by general annotators (e.g., amateurs orMTurk workers) contain systematic bias, as they cannot effectively consider language use differences among different speakers. When such biased data...
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作者：de Vaujany, Francois-Xavier; Leclercq-Vandelannoitte, Aurelie; Aroles, Jeremy; Introna, Lucas; Davidson, Scott
作者单位：IESEG School of Management; Centre National de la Recherche Scientifique (CNRS); Universite de Lille; CNRS - Institute for Humanities & Social Sciences (INSHS); University of York - UK; Lancaster University; West Virginia University
摘要：Researchers, policymakers, and industry are increasingly aware of the urgent risks and threats arising in the digital age. Their awareness of this urgency has led to a rise of interest in responsibility. While this turn to responsibility is well-intentioned, an underappreciated problem is that the dominant, centuries-old view of responsibility is not up to this task-it is unable to make sense of the increasingly extended scope of responsibility in the digital age because it is mired in outdate...
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作者：Tanriverdi, Huseyin; Akinyemi, John-Patrick Olatunji
作者单位：University of Texas System; University of Texas Austin
摘要：A key assumption in data science is that the fairness of an algorithm depends on its accuracy. Antecedents that create accuracy problems are expected to reduce fairness and cause algorithmic social injustices. We theorize why complexities in ground truths, IT ecosystems, and statistical models of algorithms can also generate algorithmic social injustices, above and beyond the indirect effects of antecedents, through the mediation of accuracy problems. We also theorize technology design and org...
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作者：Gao, Yi; Gu, Meilin; Liu, Dengpan
作者单位：Texas Tech University System; Texas Tech University; Arizona State University; Arizona State University-Tempe; Tsinghua University
摘要：Recently, a controversial new privacy policy on mobile app platforms, which requires app developers to display privacy labels and explicitly request data-tracking permissions from users, has sparked a heated discussion among practitioners in digital advertising. In this paper, we build a game-theoretic model to examine how this new policy impacts the key stakeholders of a mobile app platform (i.e., app developers, the platform, and consumers). The model captures how the new policy prompts deve...
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作者：Larsen, Kai R.; Lukyanenko, Roman; Mueller, Roland M.; Storey, Veda C.; Parsons, Jeffrey; VanderMeer, Debra; Hovorka, Dirk S.
作者单位：University of Colorado System; University of Colorado Boulder; University of Virginia; Berlin School of Economics & Law; University System of Georgia; Georgia State University; Memorial University Newfoundland; State University System of Florida; Florida International University; University of Sydney
摘要：Researchers must ensure that the claims about the knowledge produced by their work are valid. However, validity is neither well-understood nor consistently established in design science, which involves the development and evaluation of artifacts (models, methods, instantiations, and theories) to solve problems. As a result, it is challenging to demonstrate and communicate the validity of knowledge claims about artifacts. This paper defines validity in design science and derives a design scienc...
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作者：Du, Qianzhou; Zhang, Xiaohui; Zhang, Zhongju
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Arizona State University; Arizona State University-Tempe
摘要：We examine the effects of crowd characteristics on crowd value, which is measured by the improvement in the power to predict stock volatility using crowd-generated content. Leveraging a natural platform-wide event that changes the crowd compositions of S&P 500 stock discussions, we found empirical evidence that content from a larger crowd size is associated with a higher crowd value. Moreover, the magnitude of the effect of crowd size on crowd value decreases with increased diversity of the cr...
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作者：Ebrahimi, Reza; Chai, Yidong; Li, Weifeng; Pacheco, Jason; Chen, Hsinchun
作者单位：State University System of Florida; University of South Florida; Hefei University of Technology; City University of Hong Kong; University System of Georgia; University of Georgia; University of Arizona; University of Arizona
摘要：Artificial intelligence (AI) is being widely adopted in modern cyber defense to weave automation and scalability into the operational fabric of cybersecurity firms. Today, AI aids in crucial cyber defense tasks such as malware and intrusion detection to keep information technology (IT) infrastructure secure. Despite their value, cyber defense AI agents can be vulnerable to adversarial attacks. In these attacks, the adversary deliberately manipulates a malicious input by taking a sequence of ac...
