ARTEFACT Research Center

Artefact Research Center: bridging the gap between academia and industry applications.

ARTEFACT RESEARCH CENTER

Research on more controllable, transparent and ethical models to nurture AI business adoption for the future.

Status in business.

Status in business.

In recent years, AI adoption in businesses has stagnated. To illustrate, here is the share, in percentages, of respondents who say their organizations have adopted AI in at least one role.

Source: McKinsey State of AI 2022

Untrustworthy AI examples.

  • AppleCard grants mortgages based on racist criteria
  • Lensa AI sexualizes selfies of women
  • Racist Facebook Image Classification With afro-american as monkeys
  • Microsoft Twitter chatbot becoming nazi, sexist and aggressive
  • ChatGPT that writes a code stating good scientist are white males
Untrustworthy AI examples.
Current challenge.

Current challenge.

AI models are accurate and easy to deploy in many use cases, but remain uncontrollable due to black boxes & ethical issues.

The Artefact Research Center’s mission.

A complete ecosystem that bridges the gap between fundamental research and tangible industrial applications.

The Artefact Research Center's mission.
Emmanuel MALHERBE

Emmanuel MALHERBE

Head of Research

Research Field: Deep Learning, Machine Learning

Starting with a PhD on NLP models adapted to e-recruitment, Emmanuel has always sought an efficient balance between pure research and impactful applications. His research experience includes 5G time series forecasting for Huawei Technologies and computer vision models for hairdressing and makeup customers at l’Oréal. Prior to joining Artefact, he worked in Shanghai as the head of AI research for L’Oréal Asia. Today, his position at Artefact is a perfect opportunity and an ideal environment to bridge the gap between academia and industry, and to foster his real-world research while impacting industrial applications.

A full ecosystem bridging the gap between fundamental research and industry tangible applications.

A full ecosystem bridging the gap between fundamental research and industry tangible applications.

Transversal research fields.

With our unique positioning, we aim at addressing general challenges of AI, would it be on statistical modelling or management research. Those questions are transversal to all our subjects and nurture our research.

Control & Accountability

  • Controllable Models with guarantees on predictions
  • Interface with Demand planners
  • Category Managers
  • Decision by best model input: enforce reliable prediction even out of train set
  • E.g.: Enforce monotony on input variables

Explainability and Transparency

  • Interpretation of predictions
  • Interface and visualization for non-technical users
  • Adapt the models modules and components to métiers
  • Visualization on understandable inputs, before feature engineering

BIAS & UNCERTAINTY

  • Enrich prediction for better decisions
  • Non-symmetric uncertainty (vs Gaussian) needed by clients
  • Adapted to time-series and assortment optimization

Obstacles & Accelerators OF AI IN BUSINESS

  • Study of Organisations
  • Top CAC 40 stakeholders and decision takers interviews
  • Impact of AI ethics, fairness, interpretability
  • Governance, standards and regulations for AI applications

Subjects.

We work on several PhD topics at the intersection of industrial use cases and state-of-the-art limitations. For each subject, we work in collaboration with university professors and have access to industrial data that allows us to address the major research areas in a given real-world scenario.

1 — Forecasting & pricing.

Model time series as a whole with a controllable, multivariate forecasting model. Such modelling will allow us to address the pricing and promotion planning by finding the optimal parameters that increase sales forecast. With such a holistic approach, we aim at capturing cannibalization and complementarity between products. It will enable us to control the forecast with guarantees that predictions are kept consistent.

Mohamed CHTIBA

Mohamed CHTIBA

Research Scientist
on Forecasting and Pricing

Artefact
Université paris 1 Panthéon sorbonne

Research Field

Deep Learning, Optimization, Statistics

Jean-Marc BARDET

Jean-Marc BARDET

Professor

Laboratoire SAMM

Université paris 1 Panthéon sorbonne

Research Field

Stochastic Processes, Statistics, Probability

Joseph RYNKIEWICZ

Joseph RYNKIEWICZ

Associate Professor

Laboratoire SAMM

Université paris 1 Panthéon sorbonne

Research Field

Temporal Series, Neural Networks, Statistics

2 — Explainable and controllable scoring.

A widely used family of machine learning models is based on decision trees: random forests, boosting. While their accuracy is often state of the art, such models suffer from a black-box feeling, giving limited control to the user. We aim to increase their explainability and transparency, typically by improving the estimation of SHAP values in the case of unbalanced datasets. We also aim to provide some guarantees for such models, e.g., for out-of-training samples or by enabling better monotonic constraints.

Abdoulaye SAKHO

Abdoulaye SAKHO

Research Scientist on
Tree-Based Models

Sorbonne Université

Research Field

Statistics, Explainable AI

Erwan SCORNET

Erwan SCORNET

Professor

Laboratoire LPSM

Sorbonne Université

Research Field

Random forests, Interpretability, Missing values

3 — Assortment optimization.

Assortment is a major business problem for retailers that arises when selecting the set of products to be sold in stores. Using large industrial datasets and neural networks, we aim to build more robust and interpretable models that better capture customer choice when faced with an assortment of products. Dealing with cannibalization and complementarities between products, as well as a better understanding of customer clusters, are key to finding a more optimal set of products in a store.

Vincent AURIAU

Vincent AURIAU

Research Scientist on Assortment Optimization

Centrale Supélec
Université Paris Saclay

Research Field

Deep learning,
Operational Research

Vincent MOUSSEAU

Vincent MOUSSEAU

Professor

Laboratoire MICS

Centrale Supélec
Université Paris Saclay

Research Field

Preference Learning, Multicriteria Decision Analysis, Operations Research

Antoine DESIR

Antoine DESIR

Associate Professor

Laboratoire TOM

Insead

Research Field

Choice Modelling, Assortment Optimization, Operations Research

Ali AOUAD

Ali AOUAD

Assistant Professor

Management Science and Operations

London Business School

Research Field

Dynamic Matching, Choice Modelling, Assortment and Inventory Optimization, Approximation Algorithm, Operations Research

4 — AI Adoption in businesses.

The challenge of better adoption of AI in companies is to improve the AI models on the one hand, and to understand the human and organizational aspects on the other. At the crossroads of qualitative management research and social research, this axis seeks to explore where businesses face difficulties when adopting AI tools. The existing frameworks on innovation adoption are not entirely suitable for machine learning innovations, as there are typical differences with regulation, people training or biases when it comes to AI, and more so with generative AI.

Lara ABDEL HALIM

Lara ABDEL HALIM

Research Scientist on AI Adoption in Businesses

Artefact
École Polytechnique

Research Field

Management research, Innovation

Cécile CHAMARET

Cécile CHAMARET

Professor

Laboratoire CRG

École Polytechnique

Research Field

Innovation, Marketing, Qualitative Social Research

5 — Data-driven sustainability.

The project will mobilize qualitative and quantitative research methods and address two key questions: How can companies effectively measure social and environmental sustainability performance? Why do sustainability measures often fail to bring about significant changes in organizational practices?

On the one hand, the project aims to explore data-driven metrics and identify indicators to align organizational procedures with social and environmental sustainability objectives. On the other hand, the project will focus on transforming these sustainability measures into concrete actions within companies.

Oualid Mokhantar

Oualid Mokhantar

Research Scientist on Sustainability

ESCP Business School

Research Field

Management Research, Economics

Gorgi KRLEV

Gorgi KRLEV

Associate Professor

Sustainability Department

ESCP Business School

Research Field

Sustainability, Social innovation, Organizations Theory

6 — Bias in computer vision.

When a model makes a prediction based on an image, for instance showing a face, it has access to sensitive information, such as the ethnicity, gender or age, that can bias its reasoning. We aim at developing a framework to mathematically measure such bias, and propose methodologies to reduce this bias during the model training. Furthermore, our approach would statistically detect zones of strong bias to explain and understand and control where such models reinforce the bias present in the data.

Veronika SHILOVA

Veronika SHILOVA

Research Scientist on Biases in Computer Vision

Université Toulouse 3

Research Field

Deep learning, computer vision, biases

Laurent RISSER

Laurent RISSER

CNRS Research Engineer

Institut Mathématiques de Toulouse

Université Toulouse 3
CNRS

Research Field

Explainable Machine Learning, Image Analysis, Interpretable and Robust AI

Jean-Michel LOUBES

Jean-Michel LOUBES

Professor

Institut Mathématiques de Toulouse

Université Toulouse 3
ANITI

Research Field

Unbiased Learning, Interpretable AI, Optimal Transport and Applications to Statistics, Machine Learning

7 — LLM for information retrieval.

One major application of LLMs is when coupled with a corpus of documents, which represent some industrial knowledge or information. In such a case, there is a step of information retrieval, for which LLMs show some limitations, such as the size of the input text, which is too small for indexing documents. Similarly, the hallucination effect can also happen in the final answer, which we aim at detecting using the retrieved document and model uncertainty at inference time.

Hippolyte GISSEROT-BOUKHLEF

Hippolyte GISSEROT-BOUKHLEF

Research Scientist on Large Language Models for Information Retrieval

Centrale Supélec
Université Paris Saclay

Research Field

Deep Learning, NLP

Pierre COLOMBO

Pierre COLOMBO

Associate Professor

Laboratoire MICS

Centrale Supélec
Université Paris Saclay

Research Field

Large Language Models, Bias in AI, Models Evaluation

Céline HUDELOT

Céline HUDELOT

Professor

Laboratoire MICS

Centrale Supélec
Université Paris Saclay

Research Field

Knowledge Representation, Semantic interpretation, Neural Networks

Artefact’s part-time researchers.

Besides our team dedicated to research, we have several collaborators who spend some time doing scientific research and publishing papers. By working also as consultants inspire them with real-world problems encountered with our clients.

Publications.