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FairSocialComputing SIGNED

Foundations for Fair Social Computing

Total Cost €


EC-Contrib. €






Project "FairSocialComputing" data sheet

The following table provides information about the project.


Organization address
city: Munich
postcode: 80539

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 2˙487˙500 €
 EC max contribution 2˙487˙500 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-ADG
 Funding Scheme ERC-ADG
 Starting year 2018
 Duration (year-month-day) from 2018-07-01   to  2023-06-30


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

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.


year authors and title journal last update
List of publications.
2019 Abhijnan Chakraborty, Saptarshi Ghosh, Niloy Ganguly, Krishna P. Gummadi
Editorial Versus Audience Gatekeeping: Analyzing News Selection and Consumption Dynamics in Online News Media
published pages: 680-691, ISSN: 2329-924X, DOI: 10.1109/tcss.2019.2920000
IEEE Transactions on Computational Social Systems 6/4 2020-03-05
2020 Gourab K Patro, Abhijnan Chakraborty, Niloy Ganguly and Krishna P. Gummadi
Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations
published pages: , ISSN: , DOI:
Proceedings of 34th AAAI Conference on Artificial Intelligence (AAAI) February 2020 2020-03-05
2019 Abhijnan Chakraborty, Saptarshi Ghosh, Niloy Ganguly, Krishna P. Gummadi
Optimizing the recency-relevance-diversity trade-offs in non-personalized news recommendations
published pages: 447-475, ISSN: 1386-4564, DOI: 10.1007/s10791-019-09351-2
Information Retrieval Journal 22/5 2020-03-05
2019 Nina Grgić-Hlača, Christoph Engel, Krishna P. Gummadi
Human Decision Making with Machine Assistance
published pages: 1-25, ISSN: 2573-0142, DOI: 10.1145/3359280
Proceedings of the ACM on Human-Computer Interaction 3/CSCW 2020-03-05

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

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