Obiettivo Social computing represents a societal-scale symbiosis of humans and computational systems, where humans interact via and with computers, actively providing inputs to influence and being influenced by, the outputs of the computations. Recently, several concerns have been raised about the unfairness of social computations pervading our lives ranging from the potential for discrimination in machine learning based predictive analytics and implicit biases in online search and recommendations to their general lack of transparency on what sensitive data about users they use or how they use them.In this proposal, I propose ten fairness principles for social computations. They span across all three main categories of organizational justice, including distributive (fairness of the outcomes or ends of computations), procedural (fairness of the process or means of computations), and informational fairness (transparency of the outcomes and process of computations) and they cover a variety of unfairness perceptions about social computations. I describe the fundamental and novel technical challenges that arise when applying these principles to social computations. These challenges are related to operationalization (measurement), synthesis and analysis of fairness in computations. Tackling these requires applying methodologies from a number of sub-areas within CS, including learning, datamining, IR, game-theory, privacy, and distributed systems. I discuss our recent breakthroughs in tackling some of these challenges, particularly our idea of fairness constraints, a flexible mechanism that allows us to constrain learning models to synthesize fair computations that are non-discriminatory, the first of our ten principles. I outline our plans to build upon our results to tackle the challenges that arise from the other nine fairness principles. Successful execution of the proposal will provide the foundations for fair social computing in the future. Campo scientifico social sciencessociologysocial issuessocial inequalitiesnatural sciencesbiological sciencesbiological behavioural sciencesethologybiological interactionsnatural sciencescomputer and information sciencesartificial intelligencemachine learning Parole chiave Social Computing Computational Social Science Fairness Accountability and Transparency in ML Programma(i) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Argomento(i) ERC-2017-ADG - ERC Advanced Grant Invito a presentare proposte ERC-2017-ADG Vedi altri progetti per questo bando Meccanismo di finanziamento ERC-ADG - Advanced Grant Istituzione ospitante MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV Contribution nette de l'UE € 2 487 500,00 Indirizzo HOFGARTENSTRASSE 8 80539 Munchen Germania Mostra sulla mappa Regione Bayern Oberbayern München, Kreisfreie Stadt Tipo di attività Research Organisations Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 2 487 500,00 Beneficiari (1) Classifica in ordine alfabetico Classifica per Contributo netto dell'UE Espandi tutto Riduci tutto MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV Germania Contribution nette de l'UE € 2 487 500,00 Indirizzo HOFGARTENSTRASSE 8 80539 Munchen Mostra sulla mappa Regione Bayern Oberbayern München, Kreisfreie Stadt Tipo di attività Research Organisations Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 2 487 500,00