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NoBIAS SIGNED

Artificial Intelligence without Bias

Total Cost €

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EC-Contrib. €

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Partnership

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 NoBIAS project word cloud

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

underperform    consultancy    risks    chances    15    disciplinary    impacts    deployment    skills    anytime    denied    cohort    society    credit    stages    academia    unfairly    telecommunication    biases    esrs    data    miss    businesses    machine    turn    optimized    learning    move    variety    people    intelligence    ai    decision    performance    document    good    nowadays    news    interdisciplinary    employed    everywhere    sectors    transparency    start    innovation    compliance    broadly    train    decisions    nobias    social    counter    embed    job    capacity    benefiting    automatically    human    solutions    treating    marketing    reaching    leadership    collected    ethical    head    stage    industry    individuals    entailing    training    practical    considerations    rights    understand    give    predictive    medical    acquire    science    bias    everyone    algorithms    law    fairness    contravene    treatment    core    government    artificial    principles    expertise    worse    computer    media    arise    provenance    software    finance    soft   

Project "NoBIAS" data sheet

The following table provides information about the project.

Coordinator
GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVER 

Organization address
address: Welfengarten 1
city: HANNOVER
postcode: 30167
website: www.uni-hannover.de

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 Germany [DE]
 Total cost 3˙994˙775 €
 EC max contribution 3˙994˙775 € (100%)
 Programme 1. H2020-EU.1.3.1. (Fostering new skills by means of excellent initial training of researchers)
 Code Call H2020-MSCA-ITN-2019
 Funding Scheme MSCA-ITN-ETN
 Starting year 2020
 Duration (year-month-day) from 2020-01-01   to  2023-12-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVER DE (HANNOVER) coordinator 758˙365.00
2    UNIVERSITY OF SOUTHAMPTON UK (SOUTHAMPTON) participant 909˙517.00
3    UNIVERSITA DI PISA IT (PISA) participant 522˙999.00
4    GESIS-LEIBNIZ-INSTITUT FUR SOZIALWISSENSCHAFTEN EV DE (MANNHEIM) participant 505˙576.00
5    ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXIS EL (THERMI THESSALONIKI) participant 486˙035.00
6    THE OPEN UNIVERSITY UK (MILTON KEYNES) participant 303˙172.00
7    KATHOLIEKE UNIVERSITEIT LEUVEN BE (LEUVEN) participant 256˙320.00
8    SCHUFA HOLDING AG DE (WIESBADEN) participant 252˙788.00

Map

 Project objective

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and anytime entailing risks, such as being denied a credit, a job, a medical treatment, or specific news. Businesses might miss chances, because biases make AI-driven decisions underperform; much worse, they may contravene human rights when treating people unfairly. Bias may arise at all stages of AI-based decision making processes: (i) when data is collected, (ii) when algorithms turn data into decision making capacity, or (iii) when results of decision making are used in applications. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in the training, design and deployment of AI algorithms to ensure social good while still benefiting from the potential of AI. NoBIAS will develop novel methods for AI-based decision making without bias by taking into account ethical and legal considerations in the design of technical solutions. The core objectives of NoBIAS are to understand legal, social and technical challenges of bias in AI-decision making, to counter them by developing fairness-aware algorithms, to automatically explain AI results, and to document the overall process for data provenance and transparency. We will train a cohort of 15 ESRs (Early-Stage Researchers) to address problems with bias through multi-disciplinary training and research in computer science, data science, machine learning, law and social science. ESRs will acquire practical expertise in a variety of sectors from telecommunication, finance, marketing, media, software, and legal consultancy to broadly foster legal compliance and innovation. Technical, interdisciplinary and soft-skills will give ESRs a head start towards future leadership in industry, academia, or government.

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

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