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Machine learning classifiers have become a household tool for banks, companies, and government institutes for automated decision-making. In order to help explain why a person was classified a certain way, a solution was proposed that could generate these counterfactual explanations. Several generators have been introduced and tested but include several side effects. One of these side effects is making it easier to be classified incorrectly after sufficient recourse has been applied. Dynamics, a.k.a. shifts in both the domain and model, cause these side effects. We aimed to quantify these dynamics induced by two generators, Wachter et al. and REVISE, and compare them against each other. We performed three experiments with both generators and looked at the effect a different dataset, model, or hyper-parameter may have had on the dynamics. We found that REVISE induces a slight model shift while the domain shifts increase with each round of recourse., CSE3000 Research Project, Computer Science and Engineering

Machine learning classifiers have become a household tool for banks, companies, and government institutes for automated decision-making. In order to help explain why a person was classified a certain way, a solution was proposed that could generate these counterfactual explanations. Several generators have been introduced and tested but include several side effects. One of these side effects is making it easier to be classified incorrectly after sufficient recourse has been applied. Dynamics, a.k.a. shifts in both the domain and model, cause these side effects. We aimed to quantify these dynamics induced by two generators, Wachter et al. and REVISE, and compare them against each other. We performed three experiments with both generators and looked at the effect a different dataset, model, or hyper-parameter may have had on the dynamics. We found that REVISE induces a slight model shift while the domain shifts increase with each round of recourse., CSE3000 Research Project, Computer Science and Engineering

Music is often both personally and affectively meaningful to human listeners. However, little work has been done to create music recommender systems that take this into account. In this demo proposal, we present Spotivibes: a first prototype for a new color-based tagging and music recommender system. This innovative tagging system is designed to take the users' personal experience of music into account and allows them to tag their favorite songs in a non-intrusive way, which can be generalized to their entire library. The goal of Spotivibes is twofold: to help users better tag their playlists to get better playlists and to provide research data on implicit grouping mechanisms in personal music collections. The system was tested with a user study on 34 Spotify users., Multimedia Computing

Music is often both personally and affectively meaningful to human listeners. However, little work has been done to create music recommender systems that take this into account. In this demo proposal, we present Spotivibes: a first prototype for a new color-based tagging and music recommender system. This innovative tagging system is designed to take the users' personal experience of music into account and allows them to tag their favorite songs in a non-intrusive way, which can be generalized to their entire library. The goal of Spotivibes is twofold: to help users better tag their playlists to get better playlists and to provide research data on implicit grouping mechanisms in personal music collections. The system was tested with a user study on 34 Spotify users., Multimedia Computing