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

Descripción del puesto:

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