Building Trustworthy AI Systems: Transparency, Explainability, and Control with ISO/IEC TR 24028

سرفصل های دوره

As the global regulatory and legal environments work to catch up to the rapidly changing and evolving world of AI, many mandates have started to require proof of trustworthiness related to developing and deploying AI systems. In this course, instructor Lyron Andrews outlines how you can use the guidance from the ISO/IEC TR 24028 standard to leverage AI tools according to principles of trustworthiness, which include explainability, transparency, and controllability.

1 Introduction

01 Building trustworthy AI systems

2 Existing Frameworks Applicable to Trustworthiness (Clauses 5-5.5)

02 Recognition of layers of trust (Clause 5.1)

03 Software and data quality standards (Clause 5.2)

04 Application of risk management (Clause 5.4)

3 Stakeholders and Recognition of High-Level Concerns (Clauses 6-7.2)

05 Stakeholders, types, assets, and values (Clauses 6-6.4)

06 Responsibility, accountability, and governance (Clause 7.1)

07 Safety (Clause 7.2)

4 Vulnerabilities, Threats, and Challenges (Clauses 8-8.10)

08 AI-specific security threats (Clauses 8-8.2)

09 AI-specific privacy threats (Clause 8.3)

10 Bias, unpredictability, and opaqueness (Clauses 8.4-8.6)

11 Systems specification and implementation challenges (Clauses 8.7-8.8.4)

12 Challenges related to use (Clauses 8.9-8.10)

5 Mitigation Measures

13 Transparency (Clauses 9-9.2)

14 Explainability (Clauses 9.3-9.3.7)

15 Controllability, bias, and privacy (Clauses 9.4-9.6)

16 Reliability, resilience, and robustness (Clauses 9.7-9.9)

17 Testing (Clauses 9.10-9.10.2.7)

18 Evaluation (Clauses 9.10.3-9.10.5)

19 Use and applicability (Clauses 9.11-9.11.4)

6 Conclusion

20 Continue building trustworthy AI systems

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تولید کننده: LinkedIn Learning (Lynda)