Stage Case-Based Reasoning on Concepts for Sensible Explainable AI- Saclay-H/F

Description du poste

Domaine

Sciences pour l'ingénieur

Contrat

Stage

Intitulé de l'offre

Stage Case-Based Reasoning on Concepts for Sensible Explainable AI- Saclay-H/F

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.

Durée du contrat (en mois)

4 à 6 mois

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.

#Cea List

Moyens / Méthodes / Logiciels

CaBRNet

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;

Localisation du poste

Site

Saclay

Localisation du poste

France, Ile-de-France, Essonne (91)

Ville

Saclay

Critères candidat

Langues

• Français (Courant)
• Anglais (Courant)

Diplôme préparé

Bac+5 - Master of Science

Formation recommandée

Machine Learning

Possibilité de poursuite en thèse

Oui

Demandeur

Disponibilité du poste

01/03/2027

Informations générales

Entité de rattachement

Le CEA est un acteur majeur de la recherche, au service des citoyens, de l'économie et de l'Etat.

Il apporte des solutions concrètes à leurs besoins dans quatre domaines principaux : transition énergétique, transition numérique, technologies pour la médecine du futur, défense et sécurité sur un socle de recherche fondamentale. Le CEA s'engage depuis plus de 75 ans au service de la souveraineté scientifique, technologique et industrielle de la France et de l'Europe pour un présent et un avenir mieux maîtrisés et plus sûrs.

Implanté au cœur des territoires équipés de très grandes infrastructures de recherche, le CEA dispose d'un large éventail de partenaires académiques et industriels en France, en Europe et à l'international.

Les 20 000 collaboratrices et collaborateurs du CEA partagent trois valeurs fondamentales :

• La conscience des responsabilités
• La coopération
• La curiosité

Référence

2026-42007

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).