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

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TL;DR (TL;DL?)

• Algorithmic systems create challenges in balancing explainability with privacy and confidentiality.
• Key challenges include protecting sensitive information, preserving proprietary algorithms, and securing fraud detection systems.
• Focusing on what audiences need, with a few specific considerations, can help address these.

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TL;DR (TL;DL?)

• Explainability is necessary to build trust in AI systems.
• There is no universally accepted definition of explainability.
• So we focus on key considerations that don't require us to select any particular definition.

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About this podcast

A podcast for Financial Services leaders, wher...

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TL;DR (TL;DL?)

• Algorithmic processes are often complicated by intricate data flows and transformations.
• Data flow diagrams and documentation can help make processes simpler.

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About this podcast

A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algo...

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TL;DR (TL;DL?)

• Complexity must be actively managed rather than passively accepted.
• Data relevance directly impacts both accuracy and explainability.
• Technical “visibility” techniques can be useful.

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About this podcast

A podcast for Financial Services leaders, where we discuss fairness and accuracy i...

Spoken by a human version of this article.

TL;DR (TL;DL?)

• Why Explainability Matters: It builds trust, is needed to meet compliance obligations, and can help identify errors faster.
• Key Challenges: Complex algorithms, intricate workflows, privacy concerns, and making explanations understandable for all stakeholders.
• What’s Next: Future articles will explore practical solutions to these challenges.

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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 f...

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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 relation...

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.

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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 superv...

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...

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 Fo...

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• 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...

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• 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 involve...

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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 Supervis...

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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...

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• 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 e...

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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.

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About this podcast
...

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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 ...

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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 arr...