When it comes to technical due diligence for AI, the process diverges significantly from traditional due diligence practices. Let’s compare and contrast these two methodologies to understand their unique focuses, strengths, and limitations.
1. Scope and Focus
Traditional Technical Due Diligence:
Traditional due diligence typically evaluates the overall technology stack, infrastructure, and software capabilities of an organisation. The focus lies in assessing scalability, security, and maintainability across standard systems. It’s broad and often agnostic to specific applications, offering a holistic view of technological health.
AI-Specific Technical Due Diligence:
In contrast, due diligence for AI that is technical zeroes in on machine learning models, data pipelines, and algorithmic performance. The evaluation delves into model accuracy, training processes, and data integrity. Since AI systems are often more dynamic than static software solutions, this type of due diligence requires an in-depth understanding of how models evolve and adapt over time.
2. Data Assessment
Traditional Technical Due Diligence:
For traditional systems, data assessments focus on database structures, integrations, and storage mechanisms. The emphasis is on ensuring secure and efficient data handling practices.
AI-Specific Technical Due Diligence:
Data is the backbone of AI, making its assessment much more intricate. Technical due diligence for AI scrutinises the quality, diversity, and volume of data used for training models. It also evaluates data governance policies, ensuring compliance with privacy laws like GDPR or CCPA. Bias detection is another critical factor, as flawed datasets can lead to discriminatory or inaccurate AI outcomes.
3. Risk Identification
Traditional Technical Due Diligence:
Risk analysis in traditional due diligence primarily revolves around infrastructure vulnerabilities, software bugs, or gaps in cybersecurity protocols. These risks, while critical, are generally static and easier to identify.
AI-Specific Technical Due Diligence:
AI introduces unique risks, such as model drift and ethical concerns. Model drift occurs when the system’s performance degrades due to changes in input data over time. Ethical considerations include ensuring fairness, transparency, and accountability in AI decision-making. Evaluating these risks requires specialised expertise that goes beyond traditional frameworks.
4. Metrics for Success
Traditional Technical Due Diligence:
Success is measured using benchmarks like system uptime, throughput, and cost efficiency. These metrics provide a clear understanding of how well the technology supports the organisation’s objectives.
AI-Specific Technical Due Diligence:
In AI, success metrics are tied to performance indicators such as model accuracy, precision, recall, and F1 score. Additionally, explainability and interpretability are increasingly important as stakeholders demand transparency in AI-driven decisions. These metrics ensure that AI systems are not only effective but also trustworthy.
5. Tools and Methodologies
Traditional Technical Due Diligence:
Tools used in traditional assessments include software testing suites, code analysis tools, and infrastructure monitoring platforms. These tools are designed for general-purpose applications.
AI-Specific Technical Due Diligence:
For AI, specialised tools like model validation frameworks, data lineage tracking systems, and bias detection software come into play. Technical due diligence for AI leverages these advanced tools to ensure the robustness and fairness of AI models. The methodology often includes testing models under various scenarios to simulate real-world applications.
6. Team Composition
Traditional Technical Due Diligence:
Teams conducting traditional due diligence typically consist of IT specialists, software engineers, and system architects.
AI-Specific Technical Due Diligence:
AI-specific teams require data scientists, machine learning engineers, and AI ethicists. Their expertise is critical in assessing nuanced aspects like model explainability, bias mitigation, and compliance with AI regulations.
Key Takeaway
While both approaches share the goal of mitigating risks and maximising value, the complexity of technical due diligence for AI requires a specialised approach. Organisations venturing into AI projects must ensure their due diligence efforts address the unique challenges and opportunities of AI systems.
