Trust and Distributed Epistemic Labor

Miller, Boaz; Freiman, Ori

doi:10.4324/9781315542294-26

Cited by 38 publications

(8 citation statements)

References 28 publications

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“…In this view, trust is a relationship between a trustee (a person or institution that is relied upon to provide accurate and true knowledge) and a trustor (the person who relies on the trustee) (Irzik & Kurtulmus, 2019; McCraw, 2015; Rolin, 2020). In the practice of data literacies within everyday information ecologies, trust/mistrust relationships mediate what we do with data, because all agents, both experts and laypeople, rely on others as purveyors of data and data‐interpretations in their own meaning‐making and knowledge construction (Miller & Freiman, 2020). The role of trustee extends beyond human agents, such as our reliance on our watches to enable us to coordinate activity by accurately telling us the time (Goldberg, 2020).…”

Section: Theoretical Framingmentioning

confidence: 99%

Data practices duringCOVID: Everyday sensemaking in a high‐stakes information ecology

Radinsky

Tabak

2022

Brit J Educational Tech

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How do people reason with data to make sense of the world? What implications might everyday practices hold for data literacy education? We leverage the unique context of the COVID‐19 pandemic to shed light on these questions. COVID‐19 has engendered a complex, multimodal ecology of information resources, with which people engage in high‐stakes sensemaking and decision‐making. We take a relational approach to data literacy, examining how people navigate and interpret data through interactions with tools and other people. Using think‐aloud protocols, a diverse group of people described their COVID‐19 information‐seeking practices while working with COVID‐19 information resources they use routinely. Although participants differed in their disciplinary background and proficiency with data, they each consulted data frequently and used it to make sense of life in the pandemic. Three modes of interacting with data were examined: scanning, looking closer and puzzling through. In each of these modes, we examined the balance of agency between people and their tools; how participants experienced and managed emotions as part of exploring data; and how issues of trust mediated their sensemaking. Our findings provide implications for cultivating more agentic publics, using a relational lens to inform data literacy education. Practitioner notes What is already known about this topic Many people, even those with higher education, struggle with interpreting quantitative data representations. Social and emotional factors influence cognition and learning. People are often overwhelmed by the abundance of available information online. There is a need for data literacy approaches that are humanistic and relational. What this paper adds Everyday data practices can be variable and adaptable, and include engaging with data at different levels: scanning, looking closer, and puzzling through. Each of these modes involves different data practices. People, independently of their quantitative interpretation skills and disciplinary backgrounds, may engage differently with data (eg, avoiding versus delving deeper) based on their emotional responses, level of trust or interpersonal relationships that are evoked by the data. These everyday data practices have implications for people's sense of their own agency with data and involve emotional and trust‐based relationships that shape their interpretations of data. These relational aspects of data literacy suggest productive directions for data literacy education. Implications for practice and/or policy Data literacy can be taught as a process that is inherently relational, for example, by discussing the ways in which learners are personally connected to different data, what emotions these connections evoke, and how that affects the ways in which they attend to, trust and interpret the data. Data literacy education can cultivate a wider range of data practices at a variety of depths of interaction, rather than prioritizing only in‐depth inquiry. It may be helpful to include complex exp...

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Section: Theoretical Framingmentioning

confidence: 99%

Data practices duringCOVID: Everyday sensemaking in a high‐stakes information ecology

Radinsky

Tabak

2022

Brit J Educational Tech

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“…Testimonial accounts of knowledge demand the act of testimony will entail trust between the hearer and the speaker (e.g., Gelfert 2014: §8, 2018: §5). However, according to a commonly accepted approach in the epistemology of trust, only humans can be objects of trust relations, rendering technologies as lacking, in principle, the property of trustworthiness (Miller and Freiman 2020;Freiman 2021). This, yet again anthropocentric view, usually shifts discussions of trust from artifacts to the humans behind the technologies (Pitt 2010: 445).…”

Section: Anthropocentric Assumptions In Testimonial Theories Of Knowl...mentioning

confidence: 99%

Analysis of Beliefs Acquired from a Conversational AI: Instruments-based Beliefs, Testimony-based Beliefs, and Technology-based Beliefs

Freiman

2023

Episteme

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Speaking with conversational AIs, technologies whose interfaces enable human-like interaction based on natural language, has become a common phenomenon. During these interactions, people form their beliefs due to the say-so of conversational AIs. In this paper, I consider, and then reject, the concepts of testimony-based beliefs and instrument-based beliefs as suitable for analysis of beliefs acquired from these technologies. I argue that the concept of instrument-based beliefs acknowledges the non-human agency of the source of the belief. However, the analysis focuses on perceiving signs and indicators rather than content expressed in natural language. At the same time, the concept of testimony-based beliefs does refer to natural language propositions, but there is an underlying assumption that the agency of the testifier is human. To fill the lacuna of analyzing belief acquisition from conversational AIs, I suggest a third concept: technology-based beliefs. It acknowledges the non-human agency-status of the originator of the belief. Concurrently, the focus of analysis is on the propositional content that forms the belief. Filling the lacuna enables analysis that considers epistemic, ethical, and social issues of conversational AIs without excluding propositional content or compromising accepted assumptions about the agency of technologies.

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“…Within social epistemology, the standard view on trust is that trust relations are based on a human quality such as goodwill [24, 52, 56, 59-61, 63, 76, 78]. Therefore, trust relations are only possible between individual persons, however, on a generous interpretation, they also involve groups [23,56]. This view rests upon a commonly acknowledged distinction between a genuine trust and mere reliance [35,83].…”

Section: Anthropocentric View Of Trustmentioning

confidence: 99%

Making sense of the conceptual nonsense ‘trustworthy AI’

Freiman

2022

AI Ethics

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Following the publication of numerous ethical principles and guidelines, the concept of 'Trustworthy AI' has become widely used. However, several AI ethicists argue against using this concept, often backing their arguments with decades of conceptual analyses made by scholars who studied the concept of trust. In this paper, I describe the historical-philosophical roots of their objection and the premise that trust entails a human quality that technologies lack. Then, I review existing criticisms about 'Trustworthy AI' and the consequence of ignoring these criticisms: if the concept of 'Trustworthy AI' is kept being used, we risk attributing responsibilities to agents who cannot be held responsible, and consequently, deteriorate social structures which regard accountability and liability. Nevertheless, despite suggestions to shift the paradigm from 'Trustworthy AI' to 'Reliable AI', I argue that, realistically, this concept will be kept being used. I end by arguing that, ultimately, AI ethics is also about power, social justice, and scholarly activism. Therefore, I propose that community driven and social justice-oriented ethicists of AI and trust scholars further focus on (a) democratic aspects of trust formation; and (b) draw attention to critical social aspects highlighted by phenomena of distrust. This way, it will be possible to further reveal shifts in power relations, challenge unfair status quos, and suggest meaningful ways to keep the interests of citizens.

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Trust and Distributed Epistemic Labor

Cited by 38 publications

References 28 publications

Data practices duringCOVID: Everyday sensemaking in a high‐stakes information ecology

Data practices duringCOVID: Everyday sensemaking in a high‐stakes information ecology

Analysis of Beliefs Acquired from a Conversational AI: Instruments-based Beliefs, Testimony-based Beliefs, and Technology-based Beliefs

Making sense of the conceptual nonsense ‘trustworthy AI’

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