Evidence map›Paper›PMID 35465233›Full record

ReviewPatterns (New York, N.Y.)2022

How transparency modulates trust in artificial intelligence.

John Zerilli, Umang Bhatt, Adrian Weller

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

John ZerilliInstitute for Ethics in AI and Faculty of Law, University of Oxford, St Cross Building, St Cross Road, Oxford OX1 3U, UK.
Umang BhattLeverhulme Centre for the Future of Intelligence and Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK.
Adrian WellerLeverhulme Centre for the Future of Intelligence and Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study of human-machine systems is central to a variety of behavioral and engineering disciplines, including management science, human factors, robotics, and human-computer interaction. Recent advances in artificial intelligence (AI) and machine learning have brought the study of human-AI teams into sharper focus. An important set of questions for those designing human-AI interfaces concerns trust, transparency, and error tolerance. Here, we review the emerging literature on this important topic, identify open questions, and discuss some of the pitfalls of human-AI team research. We present opposition (extreme algorithm aversion or distrust) and loafing (extreme automation complacency or bias) as lying at opposite ends of a spectrum, with algorithmic vigilance representing an ideal mid-point. We suggest that, while transparency may be crucial for facilitating appropriate levels of trust in AI and thus for counteracting aversive behaviors and promoting vigilance, transparency should not be conceived solely in terms of the explainability of an algorithm. Dynamic task allocation, as well as the communication of confidence and performance metrics-among other strategies-may ultimately prove more useful to users than explanations from algorithms and significantly more effective in promoting vigilance. We further suggest that, while both aversive and appreciative attitudes are detrimental to optimal human-AI team performance, strategies to curb aversion are likely to be more important in the longer term than those attempting to mitigate appreciation. Our wider aim is to channel disparate efforts in human-AI team research into a common framework and to draw attention to the ecological validity of results in this field.

Indexed as

artificial intelligenceexplainable AIhuman-AI teamshuman-computer interactionhuman factorsmachine learningtransparencytrust

Identifiers

PMID35465233
PMCPMC9023880

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.