Scientific Fields: Artificial Intelligence, Data Science, Machine Learning, Statistics.
Keywords: XIA or Explainable AI Methods, Equality Algorithms, Statistical Education, Deep Neural Networks, Predictive Modeling, Bias, Decision Theory.
Ndeye Niang Keita, Manager of MSDMA CEDRIC CNAM Team.
Co-Director or Co-Supervisor (s):
Genane YOUNESS, Teacher-Researcher, LINEACT CESI.
Abstract thesis subject
This doctoral thesis focuses on confidence in artificial intelligence. The goal is to design and develop a methodology to address key issues raised during the design phase of an AI, such as bias, explanatory, and fairness issues. These questions have begun to gain importance in the literature and have become as necessary as the effectiveness of algorithms. Existing works collectively do not deal with these aspects. The basics of this thesis are to address these issues, starting with the data collection phase when examining the risk of bias. Subsequently, the model construction phase will be completed by resolving the issues of inequality. Finally, the decision-making phase will be dealt with by making the decisions interpretable.
This thesis work is part of the LINEACT CESI Laboratory theme. This is the result of LINEACT Laboratory’s theme 2 “Engineering and Digital Tools” Axis 2 “Digital Systems” “AI and Applied Data Science” research work. The work will be conducted around explanatory artificial intelligence and fairness to support the decision. In particular, the predictive maintenance challenge solved by artificial intelligence has a significant return in Industry 4.0; But this contribution must follow a stage of interpretation of the decisions that strengthen the confidence of these AI strategies. This thesis work will contribute to the development of this context and suggest improved solutions.
Combined Doctoral School: ED SMI (Engineering Sciences)
Procedures: In files and interviews.
Please send your application to Dr. Jenne Eunice Gaines[a]cesi.fr
Your application should include:
A detailed curriculum vitae. In case of break in university course, please give an explanation;
A cover letter explaining your motivation to pursue a doctoral thesis;
ফলাফল Results of Master 1 and 2 and corresponding report card;
চি Letters of support from outside the host laboratory (maximum 2).
Please send all documents to a zip file titled NAME firstname.zip
Scientific and technical skills:
জ্ঞান Machine knowledge and deep learning, artificial intelligence and probability / statistics.
Fluency in algorithms and programming Python, R, etc.
Good writing in English for the purpose of publishing a manuscript.
Be autonomous, have an attitude of initiative and curiosity,
Learn how to work in a team and have good interpersonal skills,
To be strict
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A pioneer in the development of learning and active learning approaches focusing on innovation and technology, CESI works in all areas of activity to meet the employment challenges and competency needs of companies. CESI, on the one hand, provides basic sciences and anthropology and, on the other hand, support systems professionals, tomorrow’s occupations and skills in industry and services, building and the city of the future. It conducts research activities at the Digital Innovation Laboratory (LINEACT CESI).
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