How Transparency Modulates Trust in Artificial Intelligence

, &
Patterns 3 (4):1-10 (2022)
  Copy   BIBTEX

Abstract

We review the literature on how perceiving an AI making mistakes violates trust and how such violations might be repaired. In doing so, we discuss the role played by various forms of algorithmic transparency in the process of trust repair, including explanations of algorithms, uncertainty estimates, and performance metrics.

Categories

Keywords

Reprint years

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 91,897

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

My notes

Similar books and articles

The appropriate role of dispute resolution in building trust online.Colin Rule & Larry Friedberg - 2005 - Artificial Intelligence and Law 13 (2):193-205.
Trust does not need to be human: it is possible to trust medical AI.Andrea Ferrario, Michele Loi & Eleonora Viganò - 2021 - Journal of Medical Ethics 47 (6):437-438.
Developing artificial agents worthy of trust: “Would you buy a used car from this artificial agent?”. [REVIEW]F. S. Grodzinsky, K. W. Miller & M. J. Wolf - 2011 - Ethics and Information Technology 13 (1):17-27.
Intentional machines: A defence of trust in medical artificial intelligence.Georg Starke, Rik van den Brule, Bernice Simone Elger & Pim Haselager - 2021 - Bioethics 36 (2):154-161.
Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns.Aurelia Tamò-Larrieux, Christoph Lutz, Eduard Fosch Villaronga & Heike Felzmann - 2019 - Big Data and Society 6 (1).
Trust and Trust-Engineering in Artificial Intelligence Research: Theory and Praxis.Melvin Chen - 2021 - Philosophy and Technology 34 (4):1429-1447.
Trust and multi-agent systems: applying the diffuse, default model of trust to experiments involving artificial agents. [REVIEW]Jeff Buechner & Herman T. Tavani - 2011 - Ethics and Information Technology 13 (1):39-51.
A Leap of Faith: Is There a Formula for “Trustworthy” AI?Matthias Braun, Hannah Bleher & Patrik Hummel - 2021 - Hastings Center Report 51 (3):17-22.
Trust and transparency in an age of surveillance.Lora Anne Viola & Paweł Laidler (eds.) - 2021 - New York, NY: Routledge.
In AI We Trust: Ethics, Artificial Intelligence, and Reliability.Mark Ryan - 2020 - Science and Engineering Ethics 26 (5):2749-2767.
Trust in Medical Artificial Intelligence: A Discretionary Account.Philip J. Nickel - 2022 - Ethics and Information Technology 24 (1):1-10.
Transparency and the Black Box Problem: Why We Do Not Trust AI.Warren J. von Eschenbach - 2021 - Philosophy and Technology 34 (4):1607-1622.
Study on effect of shared investing strategy on trust in AI.N. YokoiRyosuke & N. Kazuya - 2019 - Japanese Journal of Experimental 59 (1):46-50.
A probabilistic theory of trust concerning artificial intelligence: can intelligent robots trust humans?Saleh Afroogh - 2022 - AI and Ethics 2 (3).
Examining the Intelligence in Artificial Intelligence.David Cycleback - 2020 - Center for Artifact Studies.

Analytics

Added to PP
2023-06-23

Downloads
6 (#1,461,295)

6 months
6 (#520,798)

Historical graph of downloads

Sorry, there are not enough data points to plot this chart.
How can I increase my downloads?

Author Profiles

Adrian Weller
John Zerilli
University of Edinburgh

Citations of this work

Add more citations

References found in this work

No references found.

Add more references