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As demand grows for job-ready data science professionals, there is increasing recognition that traditional training often falls short in cultivating the higher-order reasoning and real-world problem-solving skills essential to the field. A foundational step toward addressing this gap is the identification and organization of knowledge components (KCs) that underlie data science problem solving (DSPS). KCs represent conditional knowledge—knowing about appropriate actions given particular contexts or conditions—and correspond to the critical decisions data scientists must make throughout the problem-solving process. While existing taxonomies in data science education support curriculum development, they often lack the granularity and focus needed to support the assessment and development of DSPS skills. In this paper, we present a novel framework that combines the strengths of large language models (LLMs) and human expertise to identify, define, and organize KCs specific to DSPS. We treat LLMs as ``knowledge engineering assistants" capable of generating candidate KCs by drawing on their extensive training data, which includes a vast amount of domain knowledge and diverse sets of real-world DSPS cases. Our process involves prompting multiple LLMs to generate decision points, synthesizing and refining KC definitions across models, and using sentence-embedding models to infer the underlying structure of the resulting taxonomy. Human experts then review and iteratively refine the taxonomy to ensure validity. This human-AI collaborative workflow offers a scalable and efficient proof-of-concept for LLM-assisted knowledge engineering. The resulting KC taxonomy lays the groundwork for developing fine-grained assessment tools and adaptive learning systems that support deliberate practice in DSPS. Furthermore, the framework illustrates the potential of LLMs not just as content generators but as partners in structuring domain knowledge to inform instructional design. Future work will involve extending the framework by generating a directed graph of KCs based on their input-output dependencies and validating the taxonomy through expert consensus and learner studies. This approach contributes to both the practical advancement of DSPS coaching in data science education and the broader methodological toolkit for AI-supported knowledge engineering. 

Currently, there is a surge of interest in fair Artificial Intelligence (AI) and Machine Learning (ML) research which aims to mitigate discriminatory bias in AI algorithms, e.g., along lines of gender, age, and race. While most research in this domain focuses on developing fair AI algorithms, in this work, we examine the challenges which arise when humans and fair AI interact. Our results show that due to an apparent conflict between human preferences and fairness, a fair AI algorithm on its own may be insufficient to achieve its intended results in the real world. Using college major recommendation as a case study, we build a fair AI recommender by employing gender debiasing machine learning techniques. Our offline evaluation showed that the debiased recommender makes fairer career recommendations without sacrificing its accuracy in prediction. Nevertheless, an online user study of more than 200 college students revealed that participants on average prefer the original biased system over the debiased system. Specifically, we found that perceived gender disparity is a determining factor for the acceptance of a recommendation. In other words, we cannot fully address the gender bias issue in AI recommendations without addressing the gender bias in humans. We conducted a follow-up survey to gain additional insights into the effectiveness of various design options that can help participants to overcome their own biases. Our results suggest that making fair AI explainable is crucial for increasing its adoption in the real world. 

We propose definitions of fairness in machine learning and artificial intelligence systems that are informed by the framework of intersectionality, a critical lens from the legal, social science, and humanities literature which analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including gender, race, sexual orientation, class, and disability. We show that our criteria behave sensibly for any subset of the set of protected attributes, and we prove economic, privacy, and generalization guarantees. Our theoretical results show that our criteria meaningfully operationalize AI fairness in terms of real-world harms, making the measurements interpretable in a manner analogous to differential privacy. We provide a simple learning algorithm using deterministic gradient methods, which respects our intersectional fairness criteria. The measurement of fairness becomes statistically challenging in the minibatch setting due to data sparsity, which increases rapidly in the number of protected attributes and in the values per protected attribute. To address this, we further develop a practical learning algorithm using stochastic gradient methods which incorporates stochastic estimation of the intersectional fairness criteria on minibatches to scale up to big data. Case studies on census data, the COMPAS criminal recidivism dataset, the HHP hospitalization data, and a loan application dataset from HMDA demonstrate the utility of our methods. 

There is growing awareness that AI and machine learning systems can in some cases learn to behave in unfair and discriminatory ways with harmful consequences. However, despite an enormous amount of research, techniques for ensuring AI fairness have yet to see widespread deployment in real systems. One of the main barriers is the conventional wisdom that fairness brings a cost in predictive performance metrics such as accuracy which could affect an organization's bottom-line. In this paper we take a closer look at this concern. Clearly fairness/performance trade-offs exist, but are they inevitable? In contrast to the conventional wisdom, we find that it is frequently possible, indeed straightforward, to improve on a trained model's fairness without sacrificing predictive performance. We systematically study the behavior of fair learning algorithms on a range of benchmark datasets, showing that it is possible to improve fairness to some degree with no loss (or even an improvement) in predictive performance via a sensible hyper-parameter selection strategy. Our results reveal a pathway toward increasing the deployment of fair AI methods, with potentially substantial positive real-world impacts.