Stage Case-Based Reasoning on Concepts for Sensible Explainable AI- Saclay-H/F
CEA · Saclay (91) · Stage
Publiée le 12 octobre 2026
Sujet de stage
Case-based reasoning is a popular framework for explainable AI. It exposes the
network decision process by relating inference-time inputs with
prototypes.
Recent work proposed to rely on the formalism of Abductive Explanations as to
guarantee that an prototype-based explanation is both minimal and sufficient.
Assigning meaning to prototypes is however a time-consuming task, as the various models tend to learn prototypes that do not carry sufficiently high-level meaning.
A parallel line of work is that of Concept Learning. By injecting concepts
meaningful to human operators, Concept Bottleneck Models
provide a principled way to map feature maps to annotated concepts, providing an
easy to understand - and contest - decision procedure.
Recent work display a renewed interest in those approaches, by exposing counterfactuals generation capabilities.
Description de l'offre
As an intern at the CEA, you will have the opportunity to work in a world-renowned research environment. Our teams consist of passionate and dedicated experts, providing an environment conducive to learning and collaboration. You will have access to state-of-the-art equipment and top-tier research resources to carry out your assignments. The work performed may potentially lead to a scientific publication.
Context :
The main objective of the internship is to investigate, design and implement an
abductive explanations and
concept-based decision procedure.
What do we expect from you ?
The broad internship goals are:
- familiarization with the state-of-the-art;
- sketching a decision procedure on paper;
- implement a proof-of-concept of concept bottleneck models in the CaBRNet
library;
- evaluate the approach on existing metrics and various human-in-the-loop
evaluation methods.
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Profil du candidat
Profile :
Minimal qualifications
- master student or equivalent (2nd/3rd engineering school year) in computer
science and/or machine learning;
- some knowledge of the Python programming language;
- ability to work in a team;
Preferred
- knowledge of the PyTorch framework;
- used to work with version control systems (git) and software forges (GitLab);
- background on explainable AI;
Description de l'unité
The French Atomic Energy Commission (CEA) is a key
player in research, development, and innovation. Drawing on the widely
acknowledged expertise gained by its 16,000 staff spanned over 9 research
centers with a budget of 4.1 billion Euros, CEA actively participates in more
than 400 European collaborative projects with a large number of academic
(notably as a member of Paris-Saclay University)
and industrial partners. Within the CEA Technological Research Division, the
CEA-List institute addresses the challenges coming from smart digital systems.
Among other activities, CEA List's Software Safety and Security Laboratory (LSL)
research teams design and implement automated analysis in order to make software
systems more trustworthy, to exhaustively detect their vulnerabilities, to
guarantee conformity to their specifications, and to accelerate their
certification. The lab recently extended its activities on the topic of AI
trustworthiness and gave birth to a new research group: AISER (Artificial
Intelligence Safety, Explainability and Robustness).
Compétences
- systèmes numériques
- apprentissage automatique
- systèmes de commande
- informatique
- énergie nucléaire
- détecter les goulets d'étranglement
- outils de gestion de configuration logicielle
- Python (programmation informatique)
- Français
- technologie de l’automatisation
Voir l'offre d'origine et postuler · Source : site carrières de CEA