How to Navigate AI Quality Assurance from Research to Production?

Insufficient data poses a significant challenge during bad debt prediction, requiring a reevaluation of deployment strategies for models to adapt to diverse and sometimes sparse datasets encountered in financial markets.

This happened to me, while I was building a multi-modal network for predicting bad debts for one company I worked for. At one point, we faced a challenge: the company had very little data, which meant that even though the model achieved better results on historical test data than the previous one, we couldn’t immediately deploy it into production.

Instead, we initially launched it in shadow mode, which allowed it to make predictions during each credit decision, but the final decision was still made by a human. After each session, we provided feedback back to the model. This iterative approach allowed us to test the model’s performance in production conditions and identify any issues before allowing it to make any autonomous decisions. It also provided insights into what safeguards need to be implemented and in which situations the model should never make decisions on its own.

Rigorous QA processes are essential, including regular model updates, ongoing performance monitoring, and a robust framework to ensure fairness and compliance with privacy regulations.

Fraud detection models are essential for protecting businesses and consumers, but their deployment poses challenges due to fraudsters' adaptability. Ensuring the model distinguishes between genuine and fraudulent transactions is critical, with ethical considerations necessitating thorough quality assurance to prevent biases impacting specific demographics.

A compelling example from a major bank unfolds a cautionary tale in AI-driven fraud detection. The model, initially celebrated for its triumphs, ultimately revealed a critical oversight during a closer inspection. The model, designed to predict anomalies in transaction sequences, flagged suspicious transactions for manual review. However, a closer inspection of the model revealed a shocking oversight. The model's high accuracy on historical data was attributed to the fact that the "next transaction" it was predicting was already present in its input data. Essentially, the model was being trained with the answers already in hand.

This story emphasizes the critical importance of data integrity and meticulous model evaluation, calling for a thorough approach to testing and validating AI models, especially in sensitive areas such as fraud detection. As industries undergo the AI revolution, this narrative serves as a reminder that success depends not only on building models but also on building them correctly, prompting a thorough review of data and processes to ensure the integrity and reliability of AI systems.

Despite rigorous pre-deployment testing, stock price prediction models face challenges in real-world scenarios. QA in stock price prediction includes evaluating model performance across diverse market conditions, stress-testing against historical data, and regular updates with the latest market information.

While working at a hedge fund, I was responsible for building a neural network model capable of making autonomous trading decisions. Stock trading poses unique challenges for AI models as the market’s inherent randomness and numerous anomalies make testing solutions before deploying them into production exceptionally difficult for conventional QA methods. After approximately a year of research, we developed a neural network that consistently outperformed the market on historical data, but due to resource constraints during the research phase, we couldn't conduct rigorous QA. 

Consequently, we decided to launch the built model into production in paper trading mode, making it the ultimate confirmation of the model's quality. The model performed exceptionally well for about six months, providing strong evidence that it was well-trained and possessed a suitable level of generalization. However, at a certain point, the model began allocating funds to companies whose stock prices were plummeting due to being on the verge of bankruptcy. It turned out that the cause of this situation was a classic survivorship bias. Despite the research team's awareness of survivorship bias and the implementation of several safeguards during training to prevent its occurrence, errors in data transformation for creating model targets occurred in an unexpected place.

Proper QA implementation by a separate team, independent of the research team, would have detected this issue before its launch to production. This experience highlighted the importance of thorough quality assurance processes, especially in complex and dynamic environments like financial markets.

In the financial sector, models making credit decisions must be explainable. Despite the sophistication of AI, the inability to provide clear explanations can lead to regulatory complications. QA processes should include regular audits to ensure that the deployed models align with the latest regulatory requirements. Additionally, risk management models must be tested for their resilience to extreme scenarios and unexpected events.

Collaboration with legal experts and compliance officers ensures that the models adhere to industry-specific regulations. QA efforts should encompass thorough documentation of model decisions and adherence to ethical guidelines to mitigate potential legal and reputational risks.