ISO/IEC TR 24028:2020
(Main)Information technology — Artificial intelligence — Overview of trustworthiness in artificial intelligence
Information technology — Artificial intelligence — Overview of trustworthiness in artificial intelligence
This document surveys topics related to trustworthiness in AI systems, including the following: — approaches to establish trust in AI systems through transparency, explainability, controllability, etc.; — engineering pitfalls and typical associated threats and risks to AI systems, along with possible mitigation techniques and methods; and — approaches to assess and achieve availability, resiliency, reliability, accuracy, safety, security and privacy of AI systems. The specification of levels of trustworthiness for AI systems is out of the scope of this document.
Technologies de l'information — Intelligence artificielle — Examen d'ensemble de la fiabilité en matière d'intelligence artificielle
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TECHNICAL ISO/IEC TR
REPORT 24028
First edition
Information technology —
Artificial intelligence — Overview
of trustworthiness in artificial
intelligence
Technologies de l'information — Intelligence artificielle — Examen
d'ensemble de la fiabilité en matière d'intelligence artificielle
PROOF/ÉPREUVE
Reference number
©
ISO/IEC 2020
© ISO/IEC 2020
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting
on the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address
below or ISO’s member body in the country of the requester.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: +41 22 749 01 11
Fax: +41 22 749 09 47
Email: copyright@iso.org
Website: www.iso.org
Published in Switzerland
ii PROOF/ÉPREUVE © ISO/IEC 2020 – All rights reserved
Contents Page
Foreword .v
Introduction .vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Overview . 7
5 Existing frameworks applicable to trustworthiness . 7
5.1 Background . 7
5.2 Recognition of layers of trust. 8
5.3 Application of software and data quality standards . 8
5.4 Application of risk management.10
5.5 Hardware-assisted approaches .10
6 Stakeholders .11
6.1 General concepts .11
6.2 Types.12
6.3 Assets .12
6.4 Values .13
7 Recognition of high-level concerns .13
7.1 Responsibility, accountability and governance .13
7.2 Safety .14
8 Vulnerabilities, threats and challenges .14
8.1 General .14
8.2 AI specific security threats .15
8.2.1 General.15
8.2.2 Data poisoning .15
8.2.3 Adversarial attacks.15
8.2.4 Model stealing .16
8.2.5 Hardware-focused threats to confidentiality and integrity .16
8.3 AI specific privacy threats .16
8.3.1 General.16
8.3.2 Data acquisition .16
8.3.3 Data pre-processing and modelling .17
8.3.4 Model query .17
8.4 Bias .17
8.5 Unpredictability .17
8.6 Opaqueness .18
8.7 Challenges related to the specification of AI systems .18
8.8 Challenges related to the implementation of AI systems .19
8.8.1 Data acquisition and preparation.19
8.8.2 Modelling .19
8.8.3 Model updates .21
8.8.4 Software defects .21
8.9 Challenges related to the use of AI systems .21
8.9.1 Human-computer interaction (HCI) factors .21
8.9.2 Misapplication of AI systems that demonstrate realistic human behaviour.22
8.10 System hardware faults .22
9 Mitigation measures .23
9.1 General .23
9.2 Transparency .23
9.3 Explainability .24
9.3.1 General.24
© ISO/IEC 2020 – All rights reserved PROOF/ÉPREUVE iii
9.3.2 Aims of explanation .24
9.3.3 Ex-ante vs ex-post explanation .24
9.3.4 Approaches to explainability .25
9.3.5 Modes of ex-post explanation .25
9.3.6 Levels of explainability .26
9.3.7 Evaluation of the explanations .27
9.4 Controllability .27
9.4.1 General.27
9.4.2 Human-in-the-loop control points .28
9.5 Strategies for reducing bias .28
9.6 Privacy .28
9.7 Reliability, resilience and robustness .28
9.8 Mitigating system hardware faults .29
9.9 Functional safety .29
9.10 Testing and evaluation .30
9.10.1 General.30
9.10.2 Software validation and verification methods .30
9.10.3 Robustness considerations .32
9.10.4 Privacy-related considerations .33
9.10.5 System predictability considerations.33
9.11 Use and applicability .34
9.11.1 Compliance .34
9.11.2 Managing expecta
...
TECHNICAL ISO/IEC TR
REPORT 24028
First edition
2020-05
Information technology —
Artificial intelligence — Overview
of trustworthiness in artificial
intelligence
Technologies de l'information — Intelligence artificielle — Examen
d'ensemble de la fiabilité en matière d'intelligence artificielle
Reference number
©
ISO/IEC 2020
© ISO/IEC 2020
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting
on the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address
below or ISO’s member body in the country of the requester.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: +41 22 749 01 11
Fax: +41 22 749 09 47
Email: copyright@iso.org
Website: www.iso.org
Published in Switzerland
ii © ISO/IEC 2020 – All rights reserved
Contents Page
Foreword .v
Introduction .vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Overview . 7
5 Existing frameworks applicable to trustworthiness . 7
5.1 Background . 7
5.2 Recognition of layers of trust. 8
5.3 Application of software and data quality standards . 8
5.4 Application of risk management.10
5.5 Hardware-assisted approaches .10
6 Stakeholders .11
6.1 General concepts .11
6.2 Types.12
6.3 Assets .12
6.4 Values .13
7 Recognition of high-level concerns .13
7.1 Responsibility, accountability and governance .13
7.2 Safety .14
8 Vulnerabilities, threats and challenges .14
8.1 General .14
8.2 AI specific security threats .15
8.2.1 General.15
8.2.2 Data poisoning .15
8.2.3 Adversarial attacks.15
8.2.4 Model stealing .16
8.2.5 Hardware-focused threats to confidentiality and integrity .16
8.3 AI specific privacy threats .16
8.3.1 General.16
8.3.2 Data acquisition .16
8.3.3 Data pre-processing and modelling .17
8.3.4 Model query .17
8.4 Bias .17
8.5 Unpredictability .17
8.6 Opaqueness .18
8.7 Challenges related to the specification of AI systems .18
8.8 Challenges related to the implementation of AI systems .19
8.8.1 Data acquisition and preparation.19
8.8.2 Modelling .19
8.8.3 Model updates .21
8.8.4 Software defects .21
8.9 Challenges related to the use of AI systems .21
8.9.1 Human-computer interaction (HCI) factors .21
8.9.2 Misapplication of AI systems that demonstrate realistic human behaviour.22
8.10 System hardware faults .22
9 Mitigation measures .23
9.1 General .23
9.2 Transparency .23
9.3 Explainability .24
9.3.1 General.24
© ISO/IEC 2020 – All rights reserved iii
9.3.2 Aims of explanation .24
9.3.3 Ex-ante vs ex-post explanation .24
9.3.4 Approaches to explainability .25
9.3.5 Modes of ex-post explanation .25
9.3.6 Levels of explainability .26
9.3.7 Evaluation of the explanations .27
9.4 Controllability .27
9.4.1 General.27
9.4.2 Human-in-the-loop control points .28
9.5 Strategies for reducing bias .28
9.6 Privacy .28
9.7 Reliability, resilience and robustness .28
9.8 Mitigating system hardware faults .29
9.9 Functional safety .29
9.10 Testing and evaluation .30
9.10.1 General.30
9.10.2 Software validation and verification methods .30
9.10.3 Robustness considerations .32
9.10.4 Privacy-related considerations .33
9.10.5 System predictability considerations.33
9.11 Use and applicability .34
9.11.1 Compliance .34
9.11.2 Managing expectations .
...
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