The trustworthiness of data science systems in applied and real-world settings emerges from the resolution of specific tensions through situated, pragmatic, and ongoing forms of work. Drawing on research in CSCW, critical data studies, and history and sociology of science, and six months of immersive ethnographic fieldwork with a corporate data science team, we describe four common tensions in applied data science work: (un)equivocal numbers, (counter)intuitive knowledge, (in)credible data, and (in)scrutable models. We show how organizational actors establish and re-negotiate trust under messy and uncertain analytic conditions through practices of skepticism, assessment, and credibility. Highlighting the collaborative and heterogeneous nature of real-world data science, we show how the management of trust in applied corporate data science settings depends not only on pre-processing and quantification, but also on negotiation and translation. We conclude by discussing the implications of our findings for data science research and practice, both within and beyond CSCW.
A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governance
Deparment of Computer Science and Artificial Intelligence and Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada（计算机科学与人工智能系和安达卢西亚数据科学与计算智能研究所（DaSCI），格拉纳达大学）；TECNALIA (BRTA)（TECNALIA（BRTA））；University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU））；School of Engineering, Cornell University（工程学院，康奈尔大学）；ADIA Lab（ADIA实验室）；Computational Research Department, Lawrence Berkeley National Laboratory（计算研究部，劳伦斯伯克利国家实验室）；Intelligent Systems for Robotics and Automation Laboratory, Macau University of Science and Technology（机器人与自动化智能系统实验室，澳门科学技术大学）
Artificial Intelligence began as a field probing some of the most fundamental questions of science - the nature of intelligence and the design of intelligent artifacts. But it has grown into a discipline that is deeply entwined with commerce and society. Today's AI technology, such as expert systems and intelligent assistants, pose some difficult questions of risk, trust and accountability. In this paper, we present these concerns, examining them in the context of historical developments that have shaped the nature and direction of AI research. We also suggest the exploration and further devel…
Efforts to promote fairness, accountability, and transparency are assumed to be critical in fostering Trust in AI (TAI), but extant literature is frustratingly vague regarding this 'trust'. The lack of exposition on trust itself suggests that trust is commonly understood, uncomplicated, or even uninteresting. But is it? Our analysis of TAI publications reveals numerous orientations which differ in terms of who is doing the trusting (agent), in what (object), on the basis of what (basis), in order to what (objective), and why (impact). We develop an ontology that encapsulates these key axes of …
Permisionless decentralized ledgers ("blockchains") such as the one underlying the cryptocurrency Bitcoin allow anonymous participants to maintain the ledger, while avoiding control or "censorship" by any single entity. In contrast, permissioned decentralized ledgers exploit real-world trust and accountability, allowing only explicitly authorized parties to maintain the ledger. Permissioned ledgers support more flexible governance and a wider choice of consensus mechanisms. Both kinds of decentralized ledgers may be susceptible to manipulation by participants who favor some transactions over o…
What is Safety? Corporate Discourse, Power, and the Politics of Generative AI Safety
College of Information Sciences and Technology, The Pennsylvania State University（信息科学与技术学院，宾夕法尼亚州立大学）；Max Planck Institute for Security and Privacy（安全与隐私研究所）
Cloud computing represents a significant economic opportunity for Europe. However, this growth is threatened by adoption barriers largely related to trust. This position paper examines trust and confidence issues in cloud computing and advances a case for addressing them through the implementation of a novel trustmark scheme for cloud service providers. The proposed trustmark would be both active and dynamic featuring multi-modal information about the performance of the underlying cloud service. The trustmarks would be informed by live performance data from the cloud service provider, or ideal…
The data generated by the devices and existing infrastructure in the Internet of Things (IoT) should be shared among applications. However, data sharing in the IoT can only reach its full potential when multiple participants contribute their data, for example when people are able to use their smartphone sensors for this purpose. We believe that each step, from sensing the data to the actionable knowledge, requires trust-enabled mechanisms to facilitate data exchange, such as data perception trust, trustworthy data mining, and reasoning with trust related policies. The absence of trust could af…
Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination
University of Southampton（南安普顿大学）；The Open University（开放大学）；Vienna University of Economics and Business（维也纳经济与商业大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Leibniz University of Hannover（莱布尼茨汉诺威大学）；TIB-Leibniz Information Centre of Science and Technology（莱布尼茨科学技术信息中心）
Cryptography has been widely accepted for security and partly for privacy control as discovered from past works. However, many of these works did not provide a way to manage cryptographic keys effectively especially in EHR applications, as this is the Achilles heel of cryptographic techniques currently proposed. The issue of accountability for legitimate users also has not been so popular and only a few considered it in EHR. Unless a different approach is used, the reliant on cryptography and password or escrow based system for key management will impede trust of the system and hence its accep…
This paper explores the importance of accountability to data protection, and how it can be built into the Internet of Things (IoT). The need to build accountability into the IoT is motivated by the opaque nature of distributed data flows, inadequate consent mechanisms, and lack of interfaces enabling end-user control over the behaviours of internet-enabled devices. The lack of accountability precludes meaningful engagement by end-users with their personal data and poses a key challenge to creating user trust in the IoT and the reciprocal development of the digital economy. The EU General Data …
Enhancing transparency in AI-powered customer engagement
Pegasystems（Pegasystems公司）；Oracle（甲骨文公司）；EMC（易安信公司）；University of California at Berkeley（加州大学伯克利分校）；University of Massachusetts at Amherst（马萨诸塞大学阿默斯特分校）
Introduction: Increasingly, the label data trust is being applied to repeatable mechanisms or approaches to sharing data in a timely, fair, safe and equitable way. However, there is a gap in terms of practical guidance about how to establish and operate a data trust. Aim and Approach: In December 2019, the Canadian Institute for Health Information and the Vector Institute for Artificial Intelligence convened a working meeting of 19 people representing 15 Canadian organizations/initiatives involved in data sharing, most of which focus on public sector health data. The objective was to identify …
