Artificial Intelligence (AI) Technical Due Diligence

What is AI Technical Due Diligence?

AI Tech Due Diligence plays a crucial role in developing AI-powered solutions. It entails thoroughly examining the system's architecture, algorithms, models, and technologies used. The primary objective of the technical analysis is to uncover the strengths and weaknesses of the AI solution while also identifying any potential threats and risks that may arise during its implementation.

The key elements of technology due diligence

The AI Technical Due Diligence provides a comprehensive assessment to determine if the AI solution is fully operational and prepared for implementation.

Data quality evaluation

Analyzing the quality and quantity of data used for model training is a vital component of AI Technical Due Diligence, as the accuracy and reliability of the training data directly impact the AI model performance. It involves evaluating data sources, accuracy, representativeness, and the adequacy of annotation and labeling.

Machine learning algorithms evaluation

Evaluating ML algorithms entails a thorough assessment of their performance, robustness, and suitability. This process considers performance metrics, model accuracy, scalability, and efficiency. It helps make informed decisions regarding algorithm selection and optimize the AI models' overall performance.

Performance and effectiveness evaluation

This step aims to demonstrate the proficiency of the AI system in performing its tasks and achieving desired outcomes. It involves defining precise metrics, evaluating the system's performance in real-world scenarios, and conducting error analysis to comprehend the types of misclassifications made by the system.

System and architecture tests

System and architecture tests are an integral part of the AI Tech Due Diligence process, focusing on evaluating the scalability and resilience of AI systems and assessing their ability to withstand external attacks. These tests cover various aspects of AI solutions, including scalability, stability, security, and performance.