On Evidence Capture for Accountable AI Systems

Wei Pang; Milan Markovic; Iman Naja; Chiu  Pang Fung; Pete Edwards

On Evidence Capture for Accountable AI Systems

Wei Pang, Milan Markovic, Iman Naja, Chiu Pang Fung, Pete Edwards

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Abstract

This research explores evidence capture for accountable AI systems. First, different scopes of AI accountability are set out by extending existing classification. Based on these scopes, two important and fundamental questions in evidence capture are answered: what types of evidence need to be captured and how we can capture them to facilitate better AI accountability. We hope that this research can provide guidance on building better accountable AI systems with effective evidence capture and initiate further research along this line

Original language	English
Title of host publication	CEUR Workshop Proceedings
Editors	Kyle Martin, Nirmalie Wiratunga , Anjana Wijekoon
Pages	33-39
Number of pages	7
Volume	2894
Publication status	Published - 2 Jul 2021
Event	SICSA Workshop on eXplainable Artificial Intelligence (XAI) 2021 - Duration: 1 Jun 2021 → 1 Jun 2021 https://sites.google.com/view/sicsa-xai-workshop/home?authuser=0

Publication series

Name
ISSN (Electronic)	1613-0073

Workshop

Workshop	SICSA Workshop on eXplainable Artificial Intelligence (XAI) 2021
Abbreviated title	XAI
Period	1/06/21 → 1/06/21
Internet address	https://sites.google.com/view/sicsa-xai-workshop/home?authuser=0

Keywords

Accountability
Artificial intelligence
Evidence capture

Access to Document

Pang_etal_On_Evidence_Capture_VoR
Copyright © 2021 for the individual papers by the papers' authors. Copyright © 2021 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).
Final published version, 317 KBLicence: CC BY

http://ceur-ws.org/Vol-2894/Licence: CC BY

Cite this

@inproceedings{5522d5e0d1ab401b8e837b50d460d037,

title = "On Evidence Capture for Accountable AI Systems",

abstract = "This research explores evidence capture for accountable AI systems. First, different scopes of AI accountability are set out by extending existing classification. Based on these scopes, two important and fundamental questions in evidence capture are answered: what types of evidence need to be captured and how we can capture them to facilitate better AI accountability. We hope that this research can provide guidance on building better accountable AI systems with effective evidence capture and initiate further research along this line",

keywords = "Accountability, Artificial intelligence, Evidence capture",

author = "Wei Pang and Milan Markovic and Iman Naja and {Pang Fung}, Chiu and Pete Edwards",

note = "Conference Proceedings ISSN 16130073 CEUR Workshop Proceedings Volume 2894, Pages 33 - 392021 2021 SICSA eXplainable Artifical Intelligence Workshop, SICSA XAI 2021, Aberdeen, 1 June 2021, 169872; SICSA Workshop on eXplainable Artificial Intelligence (XAI) 2021, XAI ; Conference date: 01-06-2021 Through 01-06-2021",

year = "2021",

month = jul,

day = "2",

language = "English",

volume = "2894",

pages = "33--39",

editor = "Martin, {Kyle } and {Wiratunga }, {Nirmalie } and Wijekoon, {Anjana }",

booktitle = "CEUR Workshop Proceedings",

url = "https://sites.google.com/view/sicsa-xai-workshop/home?authuser=0",

}

TY - GEN

T1 - On Evidence Capture for Accountable AI Systems

AU - Pang, Wei

AU - Markovic, Milan

AU - Naja, Iman

AU - Pang Fung, Chiu

AU - Edwards, Pete

N1 - Conference Proceedings ISSN 16130073 CEUR Workshop Proceedings Volume 2894, Pages 33 - 392021 2021 SICSA eXplainable Artifical Intelligence Workshop, SICSA XAI 2021, Aberdeen, 1 June 2021, 169872

PY - 2021/7/2

Y1 - 2021/7/2

N2 - This research explores evidence capture for accountable AI systems. First, different scopes of AI accountability are set out by extending existing classification. Based on these scopes, two important and fundamental questions in evidence capture are answered: what types of evidence need to be captured and how we can capture them to facilitate better AI accountability. We hope that this research can provide guidance on building better accountable AI systems with effective evidence capture and initiate further research along this line

AB - This research explores evidence capture for accountable AI systems. First, different scopes of AI accountability are set out by extending existing classification. Based on these scopes, two important and fundamental questions in evidence capture are answered: what types of evidence need to be captured and how we can capture them to facilitate better AI accountability. We hope that this research can provide guidance on building better accountable AI systems with effective evidence capture and initiate further research along this line

KW - Accountability

KW - Artificial intelligence

KW - Evidence capture

UR - https://sites.google.com/view/sicsa-xai-workshop/home

UR - http://www.scopus.com/inward/record.url?scp=85109636347&partnerID=8YFLogxK

UR - http://ceur-ws.org/Vol-2894/

M3 - Published conference contribution

VL - 2894

SP - 33

EP - 39

BT - CEUR Workshop Proceedings

A2 - Martin, Kyle

A2 - Wiratunga , Nirmalie

A2 - Wijekoon, Anjana

T2 - SICSA Workshop on eXplainable Artificial Intelligence (XAI) 2021

Y2 - 1 June 2021 through 1 June 2021

ER -

On Evidence Capture for Accountable AI Systems

Abstract

Publication series

Workshop

Keywords

Access to Document

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