On Evidence Capture for Accountable AI Systems

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

On Evidence Capture for Accountable AI Systems

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

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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
Pages (from-to)	33-39
Number of pages	7
Journal	CEUR Workshop Proceedings
Volume	2894
Publication status	Published - 2 Jul 2021
Event	SICSA Workshop on eXplainable Artificial Intelligence 2021 - Aberdeen, United Kingdom Duration: 1 Jun 2021 → 1 Jun 2021

Keywords

Accountability
Artificial intelligence
Evidence capture

ASJC Scopus subject areas

General Computer Science

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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 Fung, {Chiu Pang} and Peter Edwards",

note = "Funding Information: ★Copyright {\textcopyright} 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). This research is supported by the RAInS project (https://rainsproject.org/) funded by EPSRC (EP/R033846/1). We thank all other members of the project for their inspiration and suggestions for this research. Publisher Copyright: Copyright {\textcopyright} 2021 for this paper by its authors.; SICSA Workshop on eXplainable Artificial Intelligence 2021, SICSA XAI Workshop 2021 ; Conference date: 01-06-2021 Through 01-06-2021",

year = "2021",

month = jul,

day = "2",

language = "English",

volume = "2894",

pages = "33--39",

journal = "CEUR Workshop Proceedings",

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T1 - On Evidence Capture for Accountable AI Systems

AU - Pang, Wei

AU - Markovic, Milan

AU - Naja, Iman

AU - Fung, Chiu Pang

AU - Edwards, Peter

N1 - Funding Information: ★Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). This research is supported by the RAInS project (https://rainsproject.org/) funded by EPSRC (EP/R033846/1). We thank all other members of the project for their inspiration and suggestions for this research. Publisher Copyright: Copyright © 2021 for this paper by its authors.

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

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M3 - Conference article

SN - 1613-0073

VL - 2894

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EP - 39

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - SICSA Workshop on eXplainable Artificial Intelligence 2021

Y2 - 1 June 2021 through 1 June 2021

ER -

On Evidence Capture for Accountable AI Systems

Abstract

Keywords

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