This speed tutorial simplifies how to run a high-impact due diligence AI review—quickly, rigorously, and cross-functionally. Follow these 10 steps to move beyond guesswork and ensure your AI investments are built on foundations of trust, not assumptions.
Step 1: Define the Scope of Risk
Before running any model or scanning code, clarify your exposure zones. Ask:
- Will this system influence pricing, credit, hiring, or healthcare decisions?
- Could the model generate decisions that affect customer rights or legal exposure?
- Will results trigger automated actions or assist human judgment?
The answers determine how deeply your AI in due diligence workflow must probe. High-impact zones require layered technical, legal, and ethical scrutiny.
Step 2: Break the Model Apart
Collaborate with the Head of Data Science to deconstruct the system.
- What are the raw input sources?
- Are training datasets current, clean, and labelled consistently?
- Have features been engineered in a way that may encode bias?
An effective due diligence AI process treats each layer—data, logic, impact—as auditable.
Step 3: Validate Fairness and Bias Controls
No model is “neutral.” Fairness must be statistically demonstrated.
- Run subgroup tests across gender, ethnicity, income level, and geography.
- Document how sensitive fields (e.g., age, zip code) are handled.
- Include ethics and compliance in the validation loop.
Bias audits are now regulatory expectations, not optional extras.
Step 4: Test Explainability Outputs
Opaque AI creates accountability risks.
- Are explanations generated and archived?
- Can end-users understand the rationale behind decisions?
- Are there thresholds or confidence scores tied to each result?
If model decisions can’t be explained to a regulator or a customer, they shouldn’t be deployed.
Step 5: Evaluate Model Drift and Monitoring
Most models degrade over time.
- Is data drift monitored in real time?
- Are triggers in place for retraining or alerts?
- Does the system flag declining performance?
AI for due diligence must plan beyond the pilot. Continuous assurance is a must.
Step 6: Review Documentation and Access Controls
The system should be readable—not just to engineers, but to future reviewers.
- Is version control applied to data, code, and model weights?
- Are logs preserved for every inference and update?
- Can a new team pick up where the last left off?
Traceability prevents guesswork and audit failures later.
Step 7: Align Stakeholder Ownership
Ownership needs to be proactive, not reactive.
- Who owns AI oversight post-launch?
- Who funds model upgrades or retires deprecated versions?
- Are clear escalation paths defined for errors?
Your due diligence AI process should end with a stakeholder map.
Step 8: Confirm Ethical Alignment
Ethical alignment is business resilience.
- Is the model likely to amplify harm, exclusion, or misinformation?
- Have internal values (e.g., DEI or sustainability) been embedded in design?
- Are human-in-the-loop protocols present for critical decisions?
Legal isn’t the only bar. Public trust is the real test.
Step 9: Stress-Test for Worst-Case Scenarios
Ask: “What’s the worst that could happen?”
- Does the model behave predictably under edge cases?
- Has it been tested against adversarial inputs or corrupted data?
- Is there a kill-switch or override for high-risk failures?
AI for due diligence must anticipate chaos—not just best-case outcomes.
Step 10: Pressure-Test for Scalability
Today’s pilot is tomorrow’s enterprise system.
- Can the model scale across geographies and data volumes?
- Are latency, load handling, and resource usage documented?
- Can new integrations be absorbed without compromising stability?
Your final due diligence AI score should reflect not just current performance—but performance under scale, stress, and scrutiny.
Done right, Ai in due diligence isn’t a burden—it’s a strategic unlock.