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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.

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

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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