Teaching machine learning (ML) workflows to non-programmers remains a challenge in introductory AI courses. Traditionally, educators have turned to no-code tools such as KNIME to lower barriers. With the rise of generative AI (GenAI), students can now construct ML pipelines through natural language prompts, potentially offering a new “no-code” pathway. In a polytechnic-wide elective in Singapore, students were given the choice of using either KNIME or a GenAI chatbot for practical exercises and their semester project. Survey responses, informal interviews, and classroom observations revealed that both tools supported conceptual learning, but students’ experiences diverged: KNIME provided predictability and structured guidance, while GenAI offered speed and flexibility yet posed setup challenges and required coding familiarity. Students valued having a choice, though this complicated teaching logistics. Our experience suggests that GenAI can complement—but not yet replace—traditional no-code platforms, and that the design of introductory activities is critical for adoption. We share lessons learned for educators considering GenAI as an alternative in workflow-based ML education.

Privacy and transparency are two key foundations of trustworthy machine learning. Model explanations offer insights into a model's decisions on input data, whereas privacy is primarily concerned with protecting information about the training data. We analyze connections between model explanations and the leakage of sensitive information about the model's training set. We investigate the privacy risks of feature-based model explanations using membership inference attacks: quantifying how much model predictions plus their explanations leak information about the presence of a datapoint in the training set of a model. We extensively evaluate membership inference attacks based on feature-based model explanations, over a variety of datasets. We show that backpropagation-based explanations can leak a significant amount of information about individual training datapoints. This is because they reveal statistical information about the decision boundaries of the model about an input, which can reveal its membership. We also empirically investigate the trade-off between privacy and explanation quality, by studying the perturbation-based model explanations.

We study the following problem: given a labeled dataset and a specific datapoint ∼x, how did the i-th feature influence the classification for ∼x? We identify a family of numerical influence measures — functions that, given a datapoint ∼x, assign a numeric value φi(∼x) to every feature i, corresponding to how altering i’s value would influence the outcome for ∼x. This family, which we term monotone influence measures (MIM), is uniquely derived from a set of desirable properties, or axioms. The MIM family constitutes a provably sound methodology for measuring feature influence in classification domains; the values generated by MIM are based on the dataset alone, and do not make any queries to the classifier. While this requirement naturally limits the scope of our framework, we demonstrate its effectiveness on data.

Pirate syndicates capturing tankers to siphon oil, causing an estimated cost of $5 billion a year, has become a serious security issue for maritime traffic. In response to the threat, coast guards and navies deploy patrol boats to protect international oil trade. However, given the vast area of the sea and the highly time and space dependent behaviors of both players, it remains a significant challenge to find efficient ways to deploy patrol resources. In this paper, we address the research challenges and provide four key contributions. First, we construct a Stackelberg model of the oil-siphoning problem based on incident reports of actual attacks; Second, we propose a compact formulation and a constraint generation algorithm, which tackle the exponentially growth of the defender’s and attacker’s strategy spaces, respectively, to compute efficient strategies of security agencies; Third, to further improve the scalability, we propose an abstraction method, which exploits the intrinsic similarity of defender’s strategy space, to solve extremely large-scale games; Finally, we evaluate our approaches through extensive simulations and a detailed case study with real ship traffic data. The results demonstrate that our approach achieves a dramatic improvement of scalability with modest influence on the solution quality and can scale up to realistic-sized problems.

Recent years have seen the widespread implementation of data-driven algorithms making decisions in increasingly highstakes domains, such as finance, healthcare, transportation and public safety. Using novel ML techniques, these algorithms are able to process massive amounts of data and make highly accurate predictions; however, their inherent complexity makes it increasingly difficult for humans to understand why certain decisions were made. Indeed, these algorithms are black-box decision makers: their underlying decision processes are either hidden from human scrutiny by proprietary law, or (as is often the case) their inner workings are so complicated that even their own designers will be hard-pressed to explain the underlying reasoning behind their decision making processes. By obfuscating their function, data-driven classifiers run the risk of exposing human stakeholders to risks. These may include incorrect decisions (e.g. a loan application that was wrongly rejected due to system error), information leaks (e.g. an algorithm inadvertently uses information it should not have used), or discrimination (e.g. biased decisions against certain ethnic or gender groups). Government bodies and regulatory authorities have recently begun calling for algorithmic transparency: providing human-interpretable explanations of the underlying reasoning behind large-scale decision making algorithms. My thesis research will be concerned with an axiomatic analysis of automatically generated explanations of such classifiers. Especially, I'm interested in how to decide which explanation of a decision to trust given that there are many, potentially conflicting, possible explanations for any given decision.