Phd Position F/m Workflow Provenance and Its

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat **:CDD

**Niveau de diplôme exigé **:Bac + 5 ou équivalent

**Autre diplôme apprécié **:Master's degree

**Fonction **:Doctorant

**A propos du centre ou de la direction fonctionnelle**:
The Inria Centre at Rennes University is one of Inria's eight centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.

**Contexte et atouts du poste**:
**Supervisory **Team**:

- Silvina Caino-Lores, PhD (Inria, France)
- Alexandru Costan, PhD, HDR (INSA Rennes, France)
- Rafael Ferreira da Silva, PhD (Oak Ridge National Laboratory, USA)
- Ana Trisovic, PhD (Massachusetts Institute of Technology, USA)

**Location and Mobility**:
The thesis will be hosted by the KerData team at the Inria research center of Rennes. Rennes is the capital city of Britanny, in the western part of France. It is easy to reach thanks to the high-speed train line to Paris. Rennes is a dynamic, lively city and a major center for higher education and research: 25% of its population are students.

This thesis will include collaborations with international partners from the USA, thus research visits to and from the collaborator's teams are expected.
- KerData is a human-sized team currently comprising 5 permanent researchers, 2 contract researchers, 1 engineer and 5 PhD students. You will work in a caring environment, offering a good work-life balance.

**Mission confiée**:
**Context and Overview**:
Artificial Intelligence (AI) is driving scientific discovery and economic growth in all kinds of applica
- tion domains while impacting from routine daily tasks to societal-level challenges. However, research
communities, industry players and social actors are expressing increasing concern about the potential
ethical and practical implications of the pervasive presence of AI. Of particular concern are the explain
- ability of AI, or making AI’s decision-making process understandable, and transparency of AI, ensuring
clarity in AI’s design, data and operation. Therefore, working towards advancing explainability and
address these challenges, the FAIR principles (i.e., findability, accessibility, interoperability, and reuse
of digital assets) have emerged as a valuable framework [WDA+16]. However, FAIRness in AI goes
beyond the mere organization and sharing of data and code, encompassing the entire workflow that
Recent works suggest that workflow provenance (i.e., the documentation and tracking of all pro
- cesses within AI development) might hold the key to supporting FAIR and Responsible AI [SAL+22,
KNHJ+23]. Workflow provenance refers to capturing detailed information about all activities, pro
- cesses, and transformations applied to data and code during AI development and operations. It
includes information about data sources, data preprocessing, model selection, hyperparameter tuning,
and evaluation metrics, among others. Capturing this provenance could provide a holistic view of
the AI workflow, making it transparent and reproducible. However, a challenging aspect of working
with AI workflows is that today there are no comprehensive formalisms able to capture the com
- plexity and relationships in workflow and model provenance data [BBFM23]. Furthermore, multiple
technical challenges arise when attempting to capture, store and manage the full provenance of AI
workflows [MCSAGBS21, SS23], and it is still not well understood what information is valuable and
how it can be leveraged in the support of AI transparency and explainability [JRO+20]

**Research Objectives**:
This project aims to advance the research on AI’s transparency and explainability, addressing the
to formalize, capture, store, and manage metadata in AI-powered workflows, and will explore
the relationship between model provenance, metadata, and model behavior, aiming to decipher how
architectural and algorithmic characteristics impact in the model’s outcome. The project is structured
into three primary objectives:

- Aim A, that focuses on the definition of ontologies and taxonomies for AI workflow provenance
data from multiple angles: system (e.g., hardware, computing infrastructure, storage),
platform (e.g., workflow manager, machine learning framework), model (e.g., hyperparameters,
outcome of Aim A is a formal and theoretical framework able to systematically capture the complexity
of the provenance metadata landscape, and facilitate a reduction of scope for the different
- Aim B, that establishes the technical foundation to capture, store, manage and query provenance
metadata at runtime during the execution of AI workflows. This includes defining data structures,
algorithms, system architect