Trust in Data Science

Passi, Samir; Jackson, Steven

doi:10.1145/3274405

Cited by 134 publications

(44 citation statements)

References 80 publications

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“…For example, IoT sensors calibrated to measure the salinity of water may, over time, begin to provide incorrect values due to biofouling. Data science information products often rely on near real-time data to provide timely alerts, and, as such, problems may arise if these data quality issues are not timely detected and corrected (Gao et al 2015;Passi and Jackson 2018).…”

Section: The Role Of Data Governance With Regards To Data Science As mentioning

confidence: 99%

“…Data scientists gain domain expertise and apply this knowledge in big data analysis to gain the best results (Gao et al 2015). However, the trustworthiness of data science outcomes in practice is often affected by tensions arising through ongoing forms of work (Passi and Jackson 2018). According to Passi and Jackson (2018), data science is a socio-material practice in which human agency and technology are mutually intertwined.…”

Section: The Role Of Data Governance With Regards To Data Science As mentioning

confidence: 99%

“…However, the trustworthiness of data science outcomes in practice is often affected by tensions arising through ongoing forms of work (Passi and Jackson 2018). According to Passi and Jackson (2018), data science is a socio-material practice in which human agency and technology are mutually intertwined. de de Medeiros et al (2020) therefore stress the importance of developing a "data-driven culture."…”

Section: The Role Of Data Governance With Regards To Data Science As mentioning

confidence: 99%

“…The results of the case studies confirm that data science projects in which data owners have a direct influence on the project from start to finish are more likely to generate trusted outcomes. Data governance is important for creating value and moderating risk in data science initiatives (Foster et al 2018), as the trustworthiness of data science outcomes in practice is often affected by tensions arising through ongoing forms of work (Passi and Jackson 2018). This suggests that data governance plays an important role in creating trust in data science outcomes and positively influencing the use and acceptance of data science outcomes in the organization.…”

Section: Proposition 2 Organizations With Established Data Governancmentioning

confidence: 99%

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Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes

Brous¹,

Janssen

2020

Administrative Sciences

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Organizations are increasingly introducing data science initiatives to support decision-making. However, the decision outcomes of data science initiatives are not always used or adopted by decision-makers, often due to uncertainty about the quality of data input. It is, therefore, not surprising that organizations are increasingly turning to data governance as a means to improve the acceptance of data science decision outcomes. In this paper, propositions will be developed to understand the role of data governance in creating trust in data science decision outcomes. Two explanatory case studies in the asset management domain are analyzed to derive boundary conditions. The first case study is a data science project designed to improve the efficiency of road management through predictive maintenance, and the second case study is a data science project designed to detect fraudulent usage of electricity in medium and low voltage electrical grids without infringing privacy regulations. The duality of technology is used as our theoretical lens to understand the interactions between the organization, decision-makers, and technology. The results show that data science decision outcomes are more likely to be accepted if the organization has an established data governance capability. Data governance is also needed to ensure that organizational conditions of data science are met, and that incurred organizational changes are managed efficiently. These results imply that a mature data governance capability is required before sufficient trust can be placed in data science decision outcomes for decision-making.

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Section: The Role Of Data Governance With Regards To Data Science As mentioning

confidence: 99%

Section: The Role Of Data Governance With Regards To Data Science As mentioning

confidence: 99%

Section: The Role Of Data Governance With Regards To Data Science As mentioning

confidence: 99%

Section: Proposition 2 Organizations With Established Data Governancmentioning

confidence: 99%

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Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes

Brous¹,

Janssen

2020

Administrative Sciences

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“…Below we describe the importance of cross-disciplinary infrastructure; describe five ways shared vocabularies support productive crossdisciplinary work; offer a definitional overview of concepts of fairness, examining how the term is defined and conceptualized in different disciplines; provide a Fairness Analytic to provide a set of dimensions to discuss concepts fairness across definitions and disciplines; apply these tools to a case study; and reflect on this work as an incremental step toward building a shared vocabulary that fosters the collaborative work of this emerging community. While the community focused on empirical analysis to design, including: critical analysis of harms such systems can cause (particularly when purportedly recognizing and categorizing gender) and the violence they can perpetuate [18,65,75]; ethnographic investigation of how data science and algorithmic work is taught, learned, and enacted in practice [116,117]; user perceptions of "fairness" [94]; and methods to design algorithms while being cognizant of stakeholder values [154].…”

Section: Introductionmentioning

confidence: 99%

This Thing Called Fairness

Mulligan

Kroll

Kohli

et al. 2019

Proc. ACM Hum.-Comput. Interact.

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The explosion in the use of software in important sociotechnical systems has renewed focus on the study of the way technical constructs reflect policies, norms, and human values. This effort requires the engagement of scholars and practitioners from many disciplines. And yet, these disciplines often conceptualize the operative values very differently while referring to them using the same vocabulary. The resulting conflation of ideas confuses discussions about values in technology at disciplinary boundaries. In the service of improving this situation, this paper examines the value of shared vocabularies, analytics, and other tools that facilitate conversations about values in light of these disciplinary specific conceptualizations, the role such tools play in furthering research and practice, outlines different conceptions of "fairness"deployed in discussions about computer systems, and provides an analytic tool for interdisciplinary discussions and collaborations around the concept of fairness. We use a case study of risk assessments in criminal justice applications to both motivate our effort-describing how conflation of different concepts under the banner of "fairness" led to unproductive confusion-and illustrate the value of the fairness analytic by demonstrating how the rigorous analysis it enables can assist in identifying key areas of theoretical, political, and practical misunderstanding or disagreement, and where desired support alignment or collaboration in the absence of consensus. the terms we consider here is at the early stages of formation, now is the time to attend to the infrastructure necessary to support its development. BACKGROUNDParticularly with the rise of machine learning as a core technical tool for building computer systems and the concomitant sense that these systems were not adequately reflecting the values and goals of their designers and creators, the question of how to build values into software systems has gained significant traction in recent years. One focal point for the community has been the rise of the FAT scholarly meetings (FAT/ML, or the Workshop on Fairness, Accountability, and Transparency in Machine Learning, held annually since 2014 at a major machine learning conference and FAT * , the (now, ACM) Conference on Fairness, Accountability, and Transparency, which aims to build community outside of research with a machine learning focus. 2 In a similar vein, prior research within Computer Supported Cooperative Work (CSCW) and related fields, such as Human Computer Interaction (HCI) and Science & Technology Studies, has paid attention to the ways in which technical practices and computational artifacts of all kinds embed or promote a range of social values (e.g., [78,110,137,138,151]). Research programs developed to focus on values and technology, such as Value Sensitive Design and "values in design", include forms of both analysis to identify and critique values associated with systems, and methods for incorporating values into the processes of engineering and design [48,4...

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Decoding human behavior with big data? Critical, constructive input from the decision sciences

Katsikopoulos

Canellas

2022

AI Magazine

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Big data analytics employs algorithms to uncover people's preferences and values, and support their decision making. A central assumption of big data analytics is that it can explain and predict human behavior. We investigate this assumption, aiming to enhance the knowledge basis for developing algorithmic standards in big data analytics. First, we argue that big data analytics is by design atheoretical and does not provide process-based explanations of human behavior; thus, it is unfit to support deliberation that is transparent and explainable. Second, we review evidence from interdisciplinary decision science, showing that the accuracy of complex algorithms used in big data analytics for predicting human behavior is not consistently higher than that of simple rules of thumb. Rather, it is lower in situations such as predicting election outcomes, criminal profiling, and granting bail. Big data algorithms can be considered as candidate models for explaining, predicting, and supporting human decision making when they match, in transparency and accuracy, simple, process-based, domain-grounded theories of human behavior. Big data analytics can be inspired by behavioral and cognitive theory.

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Trust in Data Science

Cited by 134 publications

References 80 publications

Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes

Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes

This Thing Called Fairness

Decoding human behavior with big data? Critical, constructive input from the decision sciences

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