This curated guide is built for decision-makers who need clarity—not just curiosity—when performing effective AI algorithm due diligence. Each section points you to frameworks, tools, and resources that offer practical value.
1. Frameworks for Evaluating Algorithmic Risk
For any Head of Innovation or Chief Risk Officer, having a structured evaluation process is critical. Several organisations now offer open frameworks designed specifically to help standardise due diligence in AI algorithms:
- AI Risk Management Framework (NIST): Helps evaluate fairness, explainability, and robustness of deployed AI systems.
- OECD Principles on AI: For understanding international expectations around accountability and transparency.
- Partnership on AI’s “About ML” resources: Provides practical steps to document machine learning system decisions and assumptions.
These frameworks are useful starting points when reviewing vendor submissions, internal development pipelines, or third-party AI integration.
2. Tools for Auditing AI Algorithms
For Heads of Engineering or Product Managers who want technical depth, hands-on tools can be invaluable for conducting or supporting AI algorithm due diligence internally. Recommended tools include:
- IBM AI Fairness 360 (AIF360): Open-source toolkit to check for bias in datasets and machine learning models.
- Google’s What-If Tool: Integrated into TensorBoard, allowing model inspection and dataset exploration without writing new code.
- Microsoft InterpretML: Helps you understand model behaviour using various explainability techniques.
- Truera: Commercial platform offering monitoring, explainability, and debugging at enterprise scale.
Using these tools early in your procurement or development cycle can surface risks before they become liabilities.
3. Ethical & Legal Guidelines to Reference
For Chief Legal Officers or ESG leads, it’s important to review legal and ethical precedents that could impact AI algorithm due diligence decisions. Useful resources include:
- EU AI Act (Proposed): Likely to become one of the world’s most comprehensive regulatory regimes for AI—great for benchmarking risk levels.
- Data & Society Research Institute: Publishes in-depth studies on algorithmic accountability and its social implications.
- IEEE’s Ethically Aligned Design (EAD): Offers high-level ethical guidance for designing AI systems aligned with human values.
- AI Now Institute: Particularly strong on intersectional impacts—race, gender, and structural inequalities amplified by algorithms.
These references are essential for aligning your diligence practices with evolving legal landscapes and ethical expectations.
4. Internal Readiness Checklists
CIOs and Digital Transformation Heads often overlook their own internal readiness before reviewing external systems. Before you perform AI algorithm due diligence on vendors, ensure your organisation is prepared to ask the right questions. Recommended internal assets include:
- A Data Inventory: Know what data you have, how it’s sourced, and whether it’s suitable for AI.
- A Bias and Fairness Policy: A document that outlines your standards—so vendors know where the bar is.
- A Model Lifecycle Management Plan: Includes retraining schedules, version controls, and incident response protocols.
- A Cross-Functional AI Review Board: Comprising legal, technical, and operational leads—key for scenario planning and rapid decision-making.
Being internally aligned speeds up the process and ensures your organisation applies due diligence in AI algorithms consistently and credibly.
5. Case Study Libraries for Comparative Insight
Finally, for Strategy Heads or Investment Analysts, comparative case studies provide insight into what successful (or failed) diligence looks like. Explore:
- World Economic Forum’s Centre for the Fourth Industrial Revolution: Offers case studies on AI deployments across healthcare, mobility, and finance.
- Algorithmic Justice League: Documents public failures in AI decision systems, often with legal or reputational fallout.
- McKinsey’s “State of AI” Reports: Includes industry-level case examples of companies that pivoted based on model performance insights.
Studying outcomes from similar industries can help you structure your own evaluation, avoiding blind spots in your approach to due diligence in AI algorithms.
