As AI becomes a ubiquitous part of everyday life, people increasingly understand how it works. Whereas traditional computer programs operate on clear logic—”If A happens, then do B”—AI models, especially neural networks, make decisions that are difficult to trace back to any single rule or line of code. As a result, traditional program analysis techniques such as code review are ineffective in addressing neural networks’ vulnerabilities.
Yang Zhou, a winner of the SMU Research Staff Excellence Award for 2024, is part of SMU Professor of Computer Science Sun Jun’s MOE Tier 3 project titled “The Science of Certified AI Systems” that will look into the issue. As listed in the proposal, the first aim of the project is:
“First, we will develop a scientific foundation for analyzing AI systems, with a focus on three fundamental concepts: abstraction, causality and interpretability. Note that these concepts are fundamental for program analysis and yet must be re-invented formally due to the difference between programs and neural networks. This scientific foundation will be the basis of developing systematic analysis tools.”
Abstraction, causality and interpretability are core concepts in AI and computer science. Abstraction refers to the invisible process of how a program or model produces an output, such as a “calculate_area” function in a computer program that considers pi and radius that the user never sees.
In AI, a model would learn to identify what is a “circle” through repeated training and learn to measure its area, but nobody can point to a single line of code to identify it as where/when it learnt to do so.
Causality is simpler to understand. In programming it’s an if-then situation, e.g., if water level > 2m, sound alarm. It’s less clear cut in AI, where a car loan application could be rejected based on patterns and correlations. For example, someone over 50 years old might have the loan application approved but another 50-year-old might be rejected.
The screening model might have spotted other factors such as a history of hospitalization at an eye hospital or being issued a speeding ticket recently. As such, AI systems learn correlations but not necessarily causes.
Interpretability, simply put, is: Do you understand how the software came to the final output or decision? AI output can sometimes be opaque and needs special tools for decisions to make sense.
Once that is done, the following will be developed:
- A set of effective tools for analyzing and repairing neural networks, including testing engines, debuggers and verifiers.
- Certification standards which provide actionable guidelines for achieving a different level of quality control.
- Propose principled processes for developing AI systems, with best practices and guidelines from researchers as well as practitioners.
“This project is a huge one, and the research group under each Co-PI works on a subset of the problems above,” Yang explains. “I work with [UOB Chair Professor of Computer Science] David Lo, and our responsibility is to understand the concerns and challenges developers face when developing AI-enabled systems in practice, as well as to extract the best practices and guidelines from AI researchers and practitioners.”
The impact
Examples of AI-enabled systems include autonomous driving, image recognition, and smart traffic lights. “My research in this project focuses on an important phase of AI: How AI is integrated into software in practice and what the challenges, solutions, concerns, and practices are in this important phase,” Yang tells the Office of Research Governance & Administration (ORGA).
“For example, we suggest that it is important to write well-structured documentation for an open-source model to be more easily adapted in other software.”
The real-world impact of Yang’s work is substantial. Clear and comprehensive documentation could help smoothen deployment by listing hardware requirements and alternatives in cases of software failing to work on certain devices.
Proper documentation also facilitates faster adoption by showing developers how to plug AI models into systems, be they for autonomous driving, supply chain optimization, or smart assistants such as Amazon’s Alexa and Google Assistant.
Yang’s work on the project ties in with some of his other collaborations, one of which involves interviewing AI practitioners from the industry to understand the challenges and solutions to ensure the quality of AI systems, and validating findings by conducting surveys to collect the opinions and practices of AI developers.
More research, more impact
Yang also recently published a paper titled “Unveiling Memorization in Code Models” that looked at AI models trained to understand and generate computer code. As written in the paper, these models “automate a series of critical tasks such as defect prediction, code review, code generation and software questions analysis.”
While code models make it easier to write and maintain code, they do so by being trained on a lot of data, so much so they memorize frequently occurring code.
“Generally, language models are trained on a large corpus of code, aiming to learn ‘given a piece of code, what are the next tokens/code snippets,'” explains Yang. “There exist many code clones (identical code) in the training data, and the code models will learn such information very well, just like memorizing some training data.
“Code models may memorize the information belonging to one developer and expose the information to another, which may cause some concerns,” he adds.
Among these include security breaches (models leak passwords and database credentials), intellectual property theft (proprietary algorithms and licensed code get exposed), vulnerability propagation (insecure code patterns spread to new applications), and privacy violations (personal information and sensitive business data exposure).
How does Yang’s work address this issue? “We prompt the model to generate a large number of code snippets and identify those that can also be found in the training data via a technique called ‘code clone detection,” says Yang.
“In the paper, we aim to expose the problem of memorization and not to address it. We have recently published another paper on mitigating privacy information leakage in code models.”
The impact of this particular piece of research lies in better preserving the privacy of developers in the era of large language models. He explains, “Specifically, we design a new ‘machine unlearning’ method to guide the model to ‘forget’ the privacy information while preserving its general knowledge. When the new model is deployed, it can still generate the correct code upon user request, but will use a placeholder when privacy information is likely to be involved.”
Singapore Management University
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Amplifying AI’s impact by making it understandable (2025, August 29)
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