Opendata, web and dolomites


Science and technology for the explanation of AI decision making

Total Cost €


EC-Contrib. €






 XAI project word cloud

Explore the words cloud of the XAI project. It provides you a very rough idea of what is the project "XAI" about.

mechanistic    former    generation    bank    transparency    credit    bad    rules    intertwined    physical    ai    explanation    fraud    big    wrong    why    ethical    statistical    learning    deep    explaining    infrastructure    health    introducing    owner    data    standards    articulated    class    stubborn    physics    lowered    card    rationale    human    interpretation    observational    construct    inherited    prejudices    decisions    score    map    algorithms    decision    crowdsensing    mechanism    provisions    framework    executives    artefacts    capture    explanations    local    global    friend    he    line    continues    logic    unfair    wealthy    teller    lines    algorithm    ml    training    causal    solutions    house    opaque    gdpr    detection    models    compliance    mine    right    vacation    urgent    inference    relationships    ones    turns    black    benchmarking    discover    hidden    biases    automated    strive    box    collection    asks    quest    revealing    language    lack    investigation    expressive    generalization   

Project "XAI" data sheet

The following table provides information about the project.


Organization address
city: ROMA
postcode: 185

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Italy [IT]
 Total cost 2˙500˙000 €
 EC max contribution 2˙500˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-ADG
 Funding Scheme ERC-ADG
 Starting year 2019
 Duration (year-month-day) from 2019-10-01   to  2024-09-30


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    CONSIGLIO NAZIONALE DELLE RICERCHE IT (ROMA) coordinator 1˙265˙750.00
2    UNIVERSITA DI PISA IT (PISA) participant 1˙022˙000.00
3    SCUOLA NORMALE SUPERIORE IT (PISA) participant 212˙250.00


 Project objective

A wealthy friend of mine asks for a vacation credit card to his bank, to discover that the credit he is offered is very low. The bank teller cannot explain why. My stubborn friend continues his quest for explanation up to the bank executives, to discover that an algorithm lowered his credit score. Why? After a long investigation, it turns out that the reason is: bad credit by the former owner of my friend’s house.

Black box AI systems for automated decision making, often based on ML over (big) data, map a user’s features into a class or a score without explaining why. This is problematic for lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artefacts hidden in the training data, which may lead to unfair or wrong decisions.

I strive for solutions of the urgent challenge of how to construct meaningful explanations of opaque AI/ML systems, introducing the local-to-global framework for black box explanation, articulated along 3 lines: a) the language for explanations in terms of expressive logic rules, with statistical and causal interpretation; b) the inference of local explanations for revealing the decision rationale for a specific case; c), the bottom-up generalization of many local explanations into simple global ones. An intertwined line of research will investigate both causal explanations, i.e., models that capture the causal relationships among the features and the decision, and mechanistic/physical models of complex system physics, that capture the data generation mechanism behind specific deep learning models. I will also develop: an infrastructure for benchmarking, for the users' assessment of the explanations and the crowdsensing of observational decision data; an ethical-legal framework, for compliance and impact of our results on legal standards and on the “right of explanation” provisions of the GDPR; case studies in explanation-by-design, with a priority in health and fraud detection.

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The information about "XAI" are provided by the European Opendata Portal: CORDIS opendata.

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