Article 10. Fairness reviews: identifying essential attributes

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

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

1:14:43

Artificial intelligence is evolving at an unprecedented pace—what does that mean for the future of technology, venture capital, business, and even our understanding of ourselves? Award-winning journalist and writer Anil Ananthaswamy joins us for our latest episode to discuss his latest book Why Machines Learn: The Elegant Math Behind Modern AI . Anil helps us explore the journey and many breakthroughs that have propelled machine learning from simple perceptrons to the sophisticated algorithms shaping today’s AI revolution, powering GPT and other models. The discussion aims to demystify some of the underlying mathematical concepts that power modern machine learning, to help everyone grasp this technology impacting our lives–even if your last math class was in high school. Anil walks us through the power of scaling laws, the shift from training to inference optimization, and the debate among AI’s pioneers about the road to AGI—should we be concerned, or are we still missing key pieces of the puzzle? The conversation also delves into AI’s philosophical implications—could understanding how machines learn help us better understand ourselves? And what challenges remain before AI systems can truly operate with agency? If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for exclusive insights and updates on upcoming TechSurge Live Summits. Links: Read Why Machines Learn, Anil’s latest book on the math behind AI https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749 Learn more about Anil Ananthaswamy’s work and writing https://anilananthaswamy.com/ Watch Anil Ananthaswamy’s TED Talk on AI and intelligence https://www.ted.com/speakers/anil_ananthaswamy Discover the MIT Knight Science Journalism Fellowship that shaped Anil’s AI research https://ksj.mit.edu/ Understand the Perceptron, the foundation of neural networks https://en.wikipedia.org/wiki/Perceptron Read about the Perceptron Convergence Theorem and its significance https://www.nature.com/articles/323533a0…

about a year ago 6:54

MP3•Episode home

Spoken (by a human) version of this article.

When we're checking for fairness in our algorithmic systems (incl. processes, models, rules), we often ask:

What are the personal characteristics or attributes that, if used, could lead to discrimination?

This article provides a basic framework for identifying and categorising these attributes.

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 episodes

Article 10. Fairness reviews: identifying essential attributes

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

published about a year ago

MP3•Episode home

Spoken (by a human) version of this article.

When we're checking for fairness in our algorithmic systems (incl. processes, models, rules), we often ask:

What are the personal characteristics or attributes that, if used, could lead to discrimination?

This article provides a basic framework for identifying and categorising these attributes.

27 episodes

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

Semua episod

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Article 23. Algorithmic System Integrity: Testing 5:47

21 days ago5:47

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).…

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

25 days ago7:26

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).…

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

6 weeks ago41:23

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).…

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

6 weeks ago6:26

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).…

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

7 weeks ago32:03

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).…

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

7 weeks ago44:49

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).…

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

8 weeks ago8:26

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).…

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

8 weeks ago6:26

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).…

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

13 weeks ago4:07

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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).…

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

14 weeks ago9:10

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).…

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

15 weeks ago8:07

8:07

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).…

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

16 weeks ago5:19

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).…

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

17 weeks ago6:59

6:59

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).…

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

18 weeks ago7:18

7:18

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).…

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

19 weeks ago12:00