Bonjour à tous,

Vous êtes tous et toutes cordialement invité.es à assister à la présentation de projet du prédoc III de Emiliano Penaloza, le 2 septembre à 13h00.

Titre : Enhancing Scrutability in Modern Machine Learning

Date: mardi 2 septembre à 13h

Location: Auditorium 2, Mila, 6650 (2e étage)

Jury

Résumé

Modern machine learning systems deliver strong predictive performance but often at the cost of interpretability and user autonomy. Post-hoc explanation methods, while common, suffer from inconsistency and offer limited support for user intervention. This motivates scrutable modeling, which embeds interpretability directly into model design. This presentation highlights two recent approaches that enhance scrutability in distinct domains. First, we discuss Concept Bottleneck Models (CBMs), which constrain predictions to pass through human-interpretable concepts (e.g., “red wings,” “fever”) before making final decisions. We focus on how incorporating preference-based optimization helps address concept mislabeling, improves model performance, and strengthens the effect of user edits. Second, we discuss Textual Representations for Recommender Systems (TEARS), a framework that replaces opaque latent embeddings with editable textual profiles to increase transparency and steerability. Together, these approaches illustrate how scrutability can be systematically embedded into machine learning systems to improve transparency, reliability, and user control.