Exposure to research is an important component of undergraduate university education, cultivating critical thinking, problem-solving, and preparation for advanced study. However, providing individual research experiences for large cohorts of undergraduate students poses significant logistical challenges. This paper demonstrates how an undergraduate research experience can be achieved at scale for a large computer science program. Our approach integrates individual research projects into the undergraduate computer science curriculum for up to almost 400 students within a single 10-week course. We describe three key features of our approach: (1) a matching algorithm that assigns students to research projects based on their preferences, (2) peer-group collaboration, and (3) a distributed supervision and assessment model to guide students through key research activities that include reformulating research questions, designing experiments/user studies, and presenting research. Results and feedback indicate that both students and supervisors are satisfied, demonstrating the feasibility and effectiveness of this scalable approach for integrating research experiences into large undergraduate computer science programs.

Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely focused on single individuals in a static environment: given some estimated model, the goal is to find valid counterfactuals for an individual instance that fulfill various desiderata. The ability of such counterfactuals to handle dynamics like data and model drift remains a largely unexplored research challenge. There has also been surprisingly little work on the related question of how the actual implementation of recourse by one individual may affect other individuals. Through this work, we aim to close that gap. We first show that many of the existing methodologies can be collectively described by a generalized framework. We then argue that the existing framework does not account for a hidden external cost of recourse, that only reveals itself when studying the endogenous dynamics of recourse at the group level. Through simulation experiments involving various state-of-the-art counterfactual generators and several benchmark datasets, we generate large numbers of counterfactuals and study the resulting domain and model shifts. We find that the induced shifts are substantial enough to likely impede the applicability of Algorithmic Recourse in some situations. Fortunately, we find various strategies to mitigate these concerns. Our simulation framework for studying recourse dynamics is fast and open-sourced.

Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely been limited to the static setting and focused on single individuals: given some estimated model, the goal is to find valid counterfactuals for an individual instance that fulfill various desiderata. The ability of such counterfactuals to handle dynamics like data and model drift remains a largely unexplored research challenge at this point. There has also been surprisingly little work on the related question of how the actual implementation of recourse by one individual may affect other individuals. Through this work we aim to close that gap by systematizing and extending existing knowledge. We first show that many of the existing methodologies can be collectively described by a generalized framework. We then argue that the existing framework fails to account for a hidden external cost of recourse, that only reveals itself when studying the endogenous dynamics of recourse at the group level. Through simulation experiments involving various state-of-the-art counterfactual generators and several benchmark datasets, we generate large numbers of counterfactuals and study the resulting domain and model shifts. We find that the induced shifts are substantial enough to likely impede the applicability of Algorithmic Recourse in situations that involve large groups of individuals. Fortunately, we find various potential mitigation strategies that can be used in combination with existing approaches. Our simulation framework for studying recourse dynamics is fast and open-sourced.