Four Facets of AI Transparency
Larsson, Stefan LU ; Haresamudram, Kashyap LU ; Högberg, Charlotte LU
; Lao, Yucong
; Nyström, Axel
LU
; Söderlund, Kasia
LU
and Heintz, Fredrik
(2023)
p.445-455
- Abstract
- Transparency in artificial intelligence (AI) can mean many things, but at the same time, it is currently a central focus for both scientific and regulatory attention. We seek to critically unpack this conceptual vagueness. This is particularly called for given recent focus on transparency in much of AI policy. To this end, we construct our analysis of AI Transparency into four facets. Firstly, (1) explainability (XAI) has become an expanding field in AI, which we argue needs to be complemented by more explicit focus on the (2) mediation of AI-systems functionality, as a communicated artefact. Furthermore, in the policy discourse on AI, the importance of (3) literacy is underscored. We draw from the rich literacy literature in order to show... (More)
- Transparency in artificial intelligence (AI) can mean many things, but at the same time, it is currently a central focus for both scientific and regulatory attention. We seek to critically unpack this conceptual vagueness. This is particularly called for given recent focus on transparency in much of AI policy. To this end, we construct our analysis of AI Transparency into four facets. Firstly, (1) explainability (XAI) has become an expanding field in AI, which we argue needs to be complemented by more explicit focus on the (2) mediation of AI-systems functionality, as a communicated artefact. Furthermore, in the policy discourse on AI, the importance of (3) literacy is underscored. We draw from the rich literacy literature in order to show both promising and troubling consequences of this. Lastly, we unpack transparency as a form of governance, within a (4) legal framework encompassing a structure of trade-offs. By these four facets we aim to bring more clarity to the multifaceted concept of transparency in AI. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/e18f2439-e53c-44f1-8879-2866b71624fe
- author
- Larsson, Stefan
LU
; Haresamudram, Kashyap
LU
; Högberg, Charlotte
LU
; Lao, Yucong
; Nyström, Axel
LU
; Söderlund, Kasia
LU
and Heintz, Fredrik
- organization
- publishing date
- 2023-11-27
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- AI Transparency, explainability, xAI, AI, machine learning, literacy, AI literacy, explainable AI, law as tradeoff, mediation
- host publication
- Handbook of Critical Studies of Artificial Intelligence
- editor
- Lindgren, Simon
- pages
- 12 pages
- publisher
- Edward Elgar Publishing
- external identifiers
-
- scopus:85180826326
- ISBN
- 9781803928555
- 9781803928562
- DOI
- 10.4337/9781803928562.00047
- project
- Perceptions of AI and robotics in Sweden and Japan
- AI-förordningen - mellan fixering och flexibilitet
- The Automated Administration: Governance of ADM in the public sector
- AI Transparency and Consumer Trust
- AI in the Name of the Common Good - Relations of data, AI and humans in health and public sector
- Mammography Screening with Artificial Intelligence
- AIR Lund - Artificially Intelligent use of Registers
- Automated decision-making – Nordic perspectives
- language
- English
- LU publication?
- yes
- id
- e18f2439-e53c-44f1-8879-2866b71624fe
- date added to LUP
- 2022-09-02 13:25:44
- date last changed
- 2024-06-12 23:48:05
@inbook{e18f2439-e53c-44f1-8879-2866b71624fe,
abstract = {{Transparency in artificial intelligence (AI) can mean many things, but at the same time, it is currently a central focus for both scientific and regulatory attention. We seek to critically unpack this conceptual vagueness. This is particularly called for given recent focus on transparency in much of AI policy. To this end, we construct our analysis of AI Transparency into four facets. Firstly, (1) explainability (XAI) has become an expanding field in AI, which we argue needs to be complemented by more explicit focus on the (2) mediation of AI-systems functionality, as a communicated artefact. Furthermore, in the policy discourse on AI, the importance of (3) literacy is underscored. We draw from the rich literacy literature in order to show both promising and troubling consequences of this. Lastly, we unpack transparency as a form of governance, within a (4) legal framework encompassing a structure of trade-offs. By these four facets we aim to bring more clarity to the multifaceted concept of transparency in AI.}},
author = {{Larsson, Stefan and Haresamudram, Kashyap and Högberg, Charlotte and Lao, Yucong and Nyström, Axel and Söderlund, Kasia and Heintz, Fredrik}},
booktitle = {{Handbook of Critical Studies of Artificial Intelligence}},
editor = {{Lindgren, Simon}},
isbn = {{9781803928555}},
keywords = {{AI Transparency; explainability; xAI; AI; machine learning; literacy; AI literacy; explainable AI; law as tradeoff; mediation}},
language = {{eng}},
month = {{11}},
pages = {{445--455}},
publisher = {{Edward Elgar Publishing}},
title = {{Four Facets of AI Transparency}},
url = {{https://lup.lub.lu.se/search/files/142186928/Larsson_et_al_2022_Four_Facets_of_AI_Transparency.pdf}},
doi = {{10.4337/9781803928562.00047}},
year = {{2023}},
}