

Interpretability, Accountability and Robustness in Machine Learning
CNRS  Institut de Mathématiques de Toulouse (2017  .)
3IAANITI project (2019  .)
Context:
Machine Learning based strategies rely on the fact that a decision rule can be learned using a set of observed labeled observations, denoted the training (or learning) sample. Then the learned decision rules are applied to the whole population, which is assumed to follow the same underlying distribution as the training sample. This principle is illustrated below:
Central idea in supervised machine learning: A blackbox decision rules model is trained to later predict optimal scores. More specifically, there are input and output training data. The blackbox model is trained to transform each observed input so that it optimally fits the corresponding output. Predictions can then be performed using new input data once the model is learned. (illustration from
here )
Learning samples may however present biases either due to the presence of a real but unwanted bias in the observations (societal bias, nonrepresentative sample of the whole population, ...) or due to data preprocessing. The goal of my reseach on these questions is then twofolds: The first goal is to detect, to analyze and to remove such biases, which is called fair leaning and is strongly related to the accountability of artificial intelligence algorithms. The second goal is then to understand how biases are created and to provide more robust, certifiable and explainable methods to tackle the distributional effects in machine learning. This work has therefore key applications dealing with societal issues of artificial intelligence. It can also be directly applied to industrial applications where the interpretability, reproducibility and robustness of machine learning algorithms are of high interest. This work was started in 2017 in the context of an AOC teamproject reading group dealing with fair learning and initiated by J.M. Loubes.
Current projects:
Explainability in Artificial Intelligence
A first paper describing
the strategy developed at the Mathematics Institute of Toulouse was
written in collaboration with F. Bachoc (Mcf. Univ. Toulouse), F. Gamboa (Pr. Univ. Toulouse) and J.M. Loubes (Pr. Univ. Toulouse).
It performs global explainability by stressing the variables of a blackbox
model. This makes it possible to efficiently analyze the particular effect of each variable in the decision rule.
A Python toolbox is coming soon.
Result of [BGLR19] obtained on the Boston Housing dataset. Each curve highlights the impact of an input parameter of a trained blackbox prediction model (here Random Forests) when predicting the price of a house in Boston.
Making machine learning algorithms fair
I develop of new computationally efficient algorithms to promote fairness with Optimal
Transport Cost penalty. This work is carriedout in collaboration with J.M. Loubes (Pr. Univ. Toulouse) and N. Couellan (Pr ENAC)) is in progress.
Foundings:
2019: CNRS innovation prize obtained on the project EthikIA, in order to develop a reference Python package dealing with Interpretability, Fairness and Robustness in Machine Learning. Obtained with J.M. Loubes (IMT, Univ. Toulouse).
2018: Maturation program with Toulouse Tech Transfert, in order to work on practical strategies to detect selection biases in Machine Learning. Obtained with J.M. Loubes (IMT, Univ. Toulouse) and P. Besse (IMT, INSA Toulouse).
Links:
Popular Science

Article in Le Monde (Fr  02/2019): link

Article in 20 minutes (Fr  11/2018): link

Article in Le Figaro (Fr  11/2018): link

Article in Nature (US  06/2018): link
Tutorials

Interpretable Machine Learning (C. Molnar): link

Accountability and Fairness in machine learning (P. Besse and J.M.
Loubes): link

Tutorial from FAT 2019: link
Packages and codes

Aequitas: link

Lime: link

Shap: link

R codes from the Mathematics Institute of Toulouse: link

