Article 2. Choice vs obligation: motivation shapes the effectiveness of your review

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

https://riskinsights.com.au/blog/need-vs-want-an-audit

30:00

Living together in a group is a strategy many animals use to survive and thrive. And a big part of what makes that living situation successful is listening. In this episode, we explore the collaborative world of the naked mole-rat. Threshold is nonprofit, listener-supported, and independently produced. You can support Threshold by donating today . To stay connected, sign up for our newsletter . Operation frog sound! Send us your frog sounds for an upcoming episode. We want you to go out, listen for frogs and toads, and record them. Just find someone croaking, and hit record on your phone. It doesn’t matter if there’s background noise. It doesn’t even matter if you’re not sure whether or not you’re hearing an amphibian—if you think you are, we would love to get a recording from you. Please also say your name and where you are in the world, and then email the recording to us at outreach@thresholdpodcast.org…

for ca. et år siden 6:53

MP3•Episode hjem

Spoken (by a human) version of this article.
The motivation(s) for commissioning a review can determine how effective it will be.

Consider a personal health check-up:

Sometimes we undergo medical check-ups because we don’t have a choice. We need to - for example for workplace requirements or for insurance.
At other times, we choose to undergo such check-ups. We want to maintain optimal health and catch any potential issues early.

Often, our approach differs depending on whether we are forced to, or choose to.

The motivations – need vs want – can define how we prioritise them, what our interactions with the medical professional are and how we view the results.

Our engagement and satisfaction levels are generally higher when we choose (than when we are forced).

The same holds for reviews and audits.

About this podcast
A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI.
Hosted by Yusuf Moolla.
Produced by Risk Insights (riskinsights.com.au).

27 episoder

#MBA #Business #Risk Insights: Yusuf Moolla #Audit #Algorithms #Bias #Accuracy #Fair #Algorithm Governance #Algorithm Integrity #Ai Governance #Ai Assurance #Ai Audit

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

Article 2. Choice vs obligation: motivation shapes the effectiveness of your review

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

published for ca. et år siden

Del

MP3•Episode hjem

Spoken (by a human) version of this article.
The motivation(s) for commissioning a review can determine how effective it will be.

Consider a personal health check-up:

Sometimes we undergo medical check-ups because we don’t have a choice. We need to - for example for workplace requirements or for insurance.
At other times, we choose to undergo such check-ups. We want to maintain optimal health and catch any potential issues early.

Often, our approach differs depending on whether we are forced to, or choose to.

The motivations – need vs want – can define how we prioritise them, what our interactions with the medical professional are and how we view the results.

Our engagement and satisfaction levels are generally higher when we choose (than when we are forced).

The same holds for reviews and audits.

https://riskinsights.com.au/blog/need-vs-want-an-audit

27 episoder

#MBA #Business #Risk Insights: Yusuf Moolla #Audit #Algorithms #Bias #Accuracy #Fair #Algorithm Governance #Algorithm Integrity #Ai Governance #Ai Assurance #Ai Audit

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Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

1
Article 23. Algorithmic System Integrity: Testing 5:47

for 7 weeks siden5:47

5:47

Spoken by a human version of this article. TL;DR (TL;DL?) Testing is a core basic step for algorithmic integrity. Testing involves various stages, from developer self-checks to UAT. Where these happen will depend on whether the system is built in-house or bought. Testing needs to cover several integrity aspects, including accuracy, fairness, security, privacy, and performance. Continuous testing is needed for AI systems, differing from traditional testing due to the way these newer systems change (without code changes). About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla . Produced by Risk Insights (riskinsights.com.au).…

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

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Article 22. Algorithm Integrity: Third party assurance 7:26

for 8 weeks siden7:26

7:26

Spoken by a human version of this article. One question that comes up often is “How do we obtain assurance about third party products or services?” Depending on the nature of the relationship, and what you need assurance for, this can vary widely. This article attempts to lay out the options, considerations, and key steps to take. TL;DR (TL;DL?) Third-party assurance for algorithm integrity varies based on the nature of the relationship and specific needs, with several options. Key factors to consider include the importance and risk level of the service/product, regulatory expectations, complexity, transparency, and frequency of updates. Standardised assurance frameworks for algorithm integrity are still emerging; adopt a risk-based approach, and consider sector-specific standards like CPS230(Australia). About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla . Produced by Risk Insights (riskinsights.com.au).…

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

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Guest 3. Shea Brown, Founder and CEO of BABL AI 41:23

for 10 weeks siden41:23

41:23

Navigating AI Audits with Dr. Shea Brown Dr. Shea Brown is Founder and CEO of BABL AI BABL specializes in auditing and certifying AI systems, consulting on responsible AI practices, and offering online education. Shea shares his journey from astrophysics to AI auditing, the core services provided by BABL AI including compliance audits, technical testing, and risk assessments, and the importance of governance in AI. He also addresses the challenges posed by generative AI, the need for continuous upskilling in AI literacy, and the role of organizations like the IAAA and For Humanity in building consensus and standards in AI auditing. Finally, Shea provides insights on third-party risks, in-house AI developments, and key skills needed for effective AI governance. Chapter Markers 00:00 Introduction to Dr. Shea Brown and BABL AI 00:36 The Journey from Astrophysics to AI Auditing 02:22 Core Services and Compliance Audits at BABL 03:57 Educational Initiatives and AI Literacy 05:48 Collaborations and Professional Organizations 08:57 Approach to AI Audits and Readiness 17:29 Challenges with Generative AI in Audits 29:21 Trends in AI Deployment and Risk Assessment 34:53 Skills and Training for AI Governance 40:15 Conclusion and Contact Information About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla . Produced by Risk Insights (riskinsights.com.au).…

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

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Article 21. AI Risk Training: Role-based tailoring 6:26

for 10 weeks siden6:26

6:26

Spoken by a human version of this article. AI literacy is growing in importance (e.g., EU AI Act, IAIS). AI literacy needs vary across roles. Even "AI professionals" need AI Risk training. Links EU AI Act : The European Union Artificial Intelligence Act - specific expectation about “AI literacy”. IAIS: The International Association of Insurance Supervisors is developing a guidance paper on the supervision of AI . About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla . Produced by Risk Insights (riskinsights.com.au).…

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

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Guest 2. Patrick Sullivan: VP of Strategy and Innovation at A-LIGN 32:03

for 12 weeks siden32:03

32:03

Navigating AI Governance and Compliance Patrick Sullivan is Vice President of Strategy and Innovation at A-LIGN and an expert in cybersecurity and AI compliance with over 25 years of experience. Patrick shares his career journey, discusses his passion for educating executives and directors on effective governance, and explains the critical role of management systems like ISO 42001 in AI compliance. We discuss the complexities of AI governance, risk assessment, and the importance of clear organizational context. Patrick also highlights the challenges and benefits of AI assurance and offers insights into the changing landscape of AI standards and regulations. Chapter Markers 00:00 Introduction 00:23 Patrick's Career Journey 02:31 Focus on AI Governance 04:19 Importance of Education and Internal Training 08:08 Involvement in Industry Associations 14:13 AI Standards and Governance 20:06 Challenges with preparing for AI Certification 28:04 Future of AI Assurance About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla . Produced by Risk Insights (riskinsights.com.au).…

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

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Guest 1. Ryan Carrier: Executive Director of ForHumanity 44:49

for 12 weeks siden44:49

44:49

Mitigating AI Risks Ryan Carrier is founder and executive director of ForHumanity , a non-profit focused on mitigating the risks associated with AI, autonomous, and algorithmic systems. With 25 years of experience in financial services, Ryan discusses ForHumanity's mission to analyze and mitigate the downside risks of AI to benefit society. The conversation includes insights on the foundation of ForHumanity, the role of independent AI audits, educational programs offered by the ForHumanity AI Education and Training Center, AI governance, and the development of audit certification schemes. Ryan also highlights the importance of AI literacy, stakeholder management, and the future of AI governance and compliance. Chapter Markers 00:00 Introduction to Ryan Carrier and ForHumanity 00:57 Ryan's Background and Journey to AI 02:10 Founding ForHumanity: Mission and Early Challenges 05:15 Developing Independent Audits for AI 08:02 ForHumanity's Role and Activities 17:26 Education Programs and Certifications 29:21 AI Literacy and Future of Independent Audits 42:06 Getting Involved with ForHumanity About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla . Produced by Risk Insights (riskinsights.com.au).…

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

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Article 20. Algorithm Reviews: Public vs Private Reports 8:26

for 12 weeks siden8:26

8:26

Spoken (by a human) version of this article. Public AI audit reports aren't universally required; they mainly apply to high-risk applications and/or specific jurisdictions. The push for transparency primarily concerns independent audits, not internal reviews. Prepare by implementing ethical AI practices and conducting regular reviews. Note: High-risk AI systems in banking and insurance are subject to specific requirements Links AI and algorithm audit guidelines vary widely and are not universally applicable. We discussed this in a previous article , outlining how the appropriateness of audit guidance depends on your circumstances. Audit vs Review: we explored this topic in depth in a previous article . About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla . Produced by Risk Insights (riskinsights.com.au).…

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

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Article 19. Algorithmic System Reviews: Substantive vs. Controls Testing 6:26

for 13 weeks siden6:26

6:26

Spoken by a human version of this article. Knowing the basics of substantive testing vs. controls testing can help you determine if the review will meet your needs. Substantive testing directly identifies errors or unfairness, while controls testing evaluates governance effectiveness. The results/conclusions are different. Understanding these differences can also help you anticipate the extent of your team's involvement during the review process. Links This article details a (largely) substantive testing method for accuracy reviews. About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla . Produced by Risk Insights (riskinsights.com.au).…

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

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Article 18. Algorithm Integrity: Training and Awareness 4:07

for 17 weeks siden4:07

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Spoken by a human version of this article. Ongoing education helps everyone understand their role in responsibly developing and using algorithmic systems. Regulators and standard-setting bodies emphasise the need for AI literacy across all organisational levels. Links ForHumanity - join the growing community here . ForHumanity - free courses here . IAIS: The International Association of Insurance Supervisors is developing a guidance paper on the supervision of AI . DNB: De Nederlandsche Bank - 6 general principles for the use of AI in the financial sector . ASIC: The Australian Securities & Investments Commission - report . NIST: The National Institute of Standards and Technology - AI Risk Management Framework . EU AI Act : The European Union Artificial Intelligence Act - specific expectation about “AI literacy”. About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla . Produced by Risk Insights (riskinsights.com.au).…

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

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Article 17. Algorithm Integrity: Audit vs Review 9:10

for 19 weeks siden9:10

9:10

Spoken by a human version of this article. The terminology – “audit” vs “review” - is important, but clarity about deliverables is more important when commissioning algorithm integrity assessments. Audits are formal, with an opinion or conclusion that can often be shared externally. Reviews come in various forms and typically produce recommendations, for internal use. Regardless of the terminology you use, when commissioning an assessment, clearly define and document the expected deliverable, including the report content and intended distribution, to ensure expectations are met. About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla . Produced by Risk Insights (riskinsights.com.au).…

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

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Article 16. Algorithmic System Accuracy Reviews – Choosing the Right Approach 8:07

for 20 weeks siden8:07

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Spoken (by a human) version of this article. Outcome-focused accuracy reviews directly verify results, offering more robust assurance than process-focused methods. This approach can catch translation errors, unintended consequences, and edge cases that process reviews might miss. While more time-consuming and complex, outcome-focused reviews provide deeper insights into system reliability and accuracy. This article explains why verifying outcomes is preferred over tracing through processes, and how it works. About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla . Produced by Risk Insights (riskinsights.com.au).…

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

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Article 15. Algorithm Integrity Documentation - Getting Started 5:19

for 21 weeks siden5:19

5:19

Spoken (by a human) version of this article. Documentation makes it easier to consistently maintain algorithm integrity. This is well known. But there are lots of types of documents to prepare, and often the first hurdle is just thinking about where to start. So this simple guide is meant to help do exactly that – get going. About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla . Produced by Risk Insights (riskinsights.com.au).…

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

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Article 14. External data - use with care 6:59

for 22 weeks siden6:59

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Spoken (by a human) version of this article. Banks and insurers are increasingly using external data; using them beyond their intended purpose can be risky (e.g. discriminatory). Emerging regulations and regulatory guidance emphasise the need for active oversight by boards, senior management to ensure responsible use of external data. Keeping the customer top of mind, asking the right questions, and focusing on the intended purpose of the data, can help reduce the risk. Law and guideline mentioned in the article: Colorado's External Consumer Data and Information Sources (ECDIS) law New York's proposed circular letter . About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla . Produced by Risk Insights (riskinsights.com.au).…

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

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Article 13. Bridging the purpose-risk gap: Customer-first algorithmic risk assessments 7:18

for 23 weeks siden7:18

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Spoken (by a human) version of this article . Banks and insurers sometimes lose sight of their customer-centric purpose when assessing AI/algorithm risks, focusing instead on regular business risks and regulatory concerns. Regulators are noticing this disconnect. This article aims to outline why the disconnect happens and how we can fix it. Report mentioned in the article: ASIC, REP 798 Beware the gap: Governance arrangements in the face of AI innovation. About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla . Produced by Risk Insights (riskinsights.com.au).…

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

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Article 12. Risk-Focused Principles for Change Control in Algorithmic Systems 12:00

for 24 weeks siden12:00