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.

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

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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