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XAI SIGNED

Science and technology for the explanation of AI decision making

Total Cost €

0

EC-Contrib. €

0

Partnership

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 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.

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

Project "XAI" data sheet

The following table provides information about the project.

Coordinator
CONSIGLIO NAZIONALE DELLE RICERCHE 

Organization address
address: PIAZZALE ALDO MORO 7
city: ROMA
postcode: 185
website: www.cnr.it

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

 Partnership

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

Map

 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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