Project description
Tackling inequalities in social networks
Social inequalities are growing and threatening education, healthcare, and economies. These inequalities are driven by complex and dynamic structural barriers that remain poorly understood, particularly through the lens of social networks. As AI and machine-learning algorithms increasingly influence our lives, the risk of unintentional consequences amplifies the need to systematically detect, forecast, and address inequalities. In this context, the ERC-funded NetFair project tackles these challenges by developing a network fairness framework based on the topological and temporal features of social interactions. Using dynamical complex network models driven by social theories and big data, the project aims to explore network-driven inequalities. New software will visualise and forecast inequalities, ensuring a data-driven approach to fairness in both research and policy.
Objective
Social inequalities are on the rise and will have devastating impacts on education, healthcare, economies
and societies for many generations to come. Structural barriers to equality, despite being complex and dynamic,
are poorly understood through the lens of complex evolving social networks. More crucially, with
the rise of AI and machine-learning algorithms, it is extremely important to detect, forecast, and mitigate
those inequalities in a systematic manner in order to avoid unintentional algorithmic consequences.
A network fairness framework is proposed, premised on topological and temporal features of social interactions
that shape the formation and evolution of inequalities. These features include (1) people have
multiple and correlated attributes that determine how they identify with groups and interact with others,
(2) people belong to a variety of social groups with different sizes, hierarchies, and historical precedents,
and (3) interactions between people evolve over time in a hybrid space of society and algorithms.
To this end, I will develop a suite of dynamical complex network models of inequality that are driven by
social theories (e.g. homophily, intersectionality, consolidation) and calibrated and evaluated with big data
and network experiments. This will allow us, for the first time, to investigate inequalities that arise from
network-based algorithms in a systematic manner. More importantly, I will devise a novel methodology of
network intervention, a set of data-driven principles for tackling network inequalities in a broad range of
applications. Finally, based on the models developed in this project, I will create a cutting-edge interactive
software – NetFair – to visualize and forecast the evolution of inequalities and implement various fairness
criteria. The software will contribute towards bridging the gap between research and policy applications
in academia and industry.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
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Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
8010 Graz
Austria