On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness

Kästner, Lena; Langer, Markus; Lazar, Veronika; Schomäcker, Astrid; Speith, Timo; Sterz, Sarah

doi:10.1109/rew53955.2021.00031

Cited by 27 publications

(19 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The problems that XAI should tackle often require interdisciplinary research [17,41,44]. Preventing discrimination [24,41,74], increasing trustworthiness [37,41,46], allocating responsibility [13,41,59], and generally, promoting human well-being [27,46] is, in principle, possible with explainability -as long as researchers from different disciplines can come together to work on it. Accordingly, confusion in the field may postpone the potentially vast social benefits XAI promises to bring about.…”

Section: Discussionmentioning

confidence: 99%

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

Speith

2022

2022 ACM Conference on Fairness, Accountability, and Transparency

Self Cite

135

View full text Add to dashboard Cite

The recent surge in publications related to explainable artificial intelligence (XAI) has led to an almost insurmountable wall if one wants to get started or stay up to date with XAI. For this reason, articles and reviews that present taxonomies of XAI methods seem to be a welcomed way to get an overview of the field. Building on this idea, there is currently a trend of producing such taxonomies, leading to several competing approaches to construct them. In this paper, we will review recent approaches to constructing taxonomies of XAI methods and discuss general challenges concerning them as well as their individual advantages and limitations. Our review is intended to help scholars be aware of challenges current taxonomies face. As we will argue, when charting the field of XAI, it may not be sufficient to rely on one of the approaches we found. To amend this problem, we will propose and discuss three possible solutions: a new taxonomy that incorporates the reviewed ones, a database of XAI methods, and a decision tree to help choose fitting methods. CCS CONCEPTS• General and reference → Surveys and overviews; • Computing methodologies → Artificial intelligence.

show abstract

Section: Discussionmentioning

confidence: 99%

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

Speith

2022

2022 ACM Conference on Fairness, Accountability, and Transparency

Self Cite

135

View full text Add to dashboard Cite

show abstract

“…According to Kästner et al [64] is trust "an attitude a stakeholder holds towards a system". In contrast, the authors describe trustworthiness as "a property of a system: intuitively, a system is trustworthy for a stakeholder when it is warranted for the stakeholder to put trust in the system".…”

Section: Trust and Trustworthinessmentioning

confidence: 99%

Privacy explanations – A means to end-user trust

Brunotte

Specht

Chazette

et al. 2023

Journal of Systems and Software

View full text Add to dashboard Cite

“…Results of studies aiming to test the hypothesis have shown to be inconsistent: users at times over-trust models or model explanations [5,40,44,66], while at other times they inappropriately distrust the AI [76,86]. Additionally, some studies report mixed results, where explanations might increase understanding, but not trust [12,21,39]. These inconsistencies may hamper progress in the field and hinder the successful deployment and use of AI-based systems in practice.…”

Section: Introductionmentioning

confidence: 99%

“…Recent works have related these inconsistencies to current empirical practices aiming at defining and evaluating trust in AI [10,12,13,36,39,80]. First, numerous terms have been introduced to indicate trust and trust-related concepts in empirical studies, including "calibrated trust" [46], "appropriate trust" [86], "reliance" [33], and "actual trustworthiness" [72].…”

Section: Introductionmentioning

confidence: 99%

The Value of Measuring Trust in AI - A Socio-Technical System Perspective

Benk¹,

Tolmeijer²,

Wangenheim³

et al. 2022

Preprint

View full text Add to dashboard Cite

Building trust in AI-based systems is deemed critical for their adoption and appropriate use. Recent research has thus attempted to evaluate how various attributes of these systems affect user trust. However, limitations regarding the definition and measurement of trust in AI have hampered progress in the field, leading to results that are inconsistent or difficult to compare. In this work, we provide an overview of the main limitations in defining and measuring trust in AI. We focus on the attempt of giving trust in AI a numerical value and its utility in informing the design of real-world human-AI interactions. Taking a socio-technical system perspective on AI, we explore two distinct approaches to tackle these challenges. We provide actionable recommendations on how these approaches can be implemented in practice and inform the design of human-AI interactions. We thereby aim to provide a starting point for researchers and designers to re-evaluate the current focus on trust in AI, improving the alignment between what empirical research paradigms may offer and the expectations of real-world human-AI interactions.

show abstract

On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness

Cited by 27 publications

References 73 publications

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

Privacy explanations – A means to end-user trust

The Value of Measuring Trust in AI - A Socio-Technical System Perspective

Contact Info

Product

Resources

About