Models and Patterns of Trust

Abstract

As in all collaborative work, trust is a vital ingredient of successful computer supported cooperative work, yet there is little in the way of design principles to help practitioners develop systems that foster trust. To address this gap, we present a set of design patterns, based on our experience designing systems with the explicit intention of increasing trust between stakeholders. We contextualize these patterns by describing our own learning process, from the development, testing and refinement of a trust model, to our realization that the insights we gained along the way were most usefully expressed through design patterns. In addition to a set of patterns for trust, this paper seeks to demonstrate of the value of patterns as a means of communicating the nuances revealed through ethnographic investigation.

Maintainer Perspectives on Data-driven Transport Asset Management and the Future Role of the Internet-of-Things

“…The main and, we believe, substantial contribution of this paper is that it provides a detailed understanding of maintainers' everyday work and, in particular their perspective of data work and attempts to present these empirical findings in relation to some important concepts in the field of IoT and CSCW. In particular, this work builds upon and strengthens some of our early empirical work first reported in [4,5,30]. Whilst we would not claim to make any especially new conceptual contributions, we do suggest that this work contributes to an understanding of classic CSCW notions, such as 'awareness' and 'articulation work' in a relatively new domain of drainage maintenance, illustrating the replicability and generalizability of these concepts, and therefore their wider relevance to design work.…”

“…Through our own history of engaging with transport maintenance organisations, exploring issues of data sharing and the trustworthy design of information systems [4,5,30] has provided an invaluable outpost over the past decade to observe the slow, but emerging evolution of transport [33] would describe as data-aware or data-guided organisations. Indeed, this recent shift has given rise to a growing interest in the potential opportunities of IoT analytics, to support more informed drainage planning and decision-making.…”

Trust in Data Science

“…While quantification provides calculative relief in the face of uncertainty, questions of trust are "ultimately questions of interpretation," and within meaningmaking processes "limitations of traditional algorithm design and evaluation methods" are clearly visible [6:10]. Researchers thus focus not only on fostering trust in computational systems [58], but also on understanding users' perception of quantified metrics [48].…”

“…Numbers not only signify model performance or validity, but also embody specific technical ideals and business values. Understanding the pragmatic ways of working with the plastic and plural nature of quantified trust and credibility metrics can further nuance existing CSCW and HCI research on the design of trustworthy systems [37,58,82] and reliable performance metrics [1,48,79]-managing numbers is as important as engineering them.…”

“…This is even harder for lay users who lack specialized data science knowledge but are sometimes the users of these systems or those most impacted by them. CSCW and HCI researchers have begun to take on these challenges through efforts to make results more understandable [81,82], to effectively manage data science systems [55,65], to understand user perception of performance metrics [48], and to ascertain ways to foster trust through system design [57,58].…”