Project description DEENESFRITPL Understanding the consequences of machine learning algorithms for contemporary societies Rapid advancements in machine learning technologies are transforming social and political life in ways that uniquely challenge how we live in relation to others. The life chances of a person are now intimately connected to the attributes that an algorithm has learned from the data patterns of unknown others. The EU-funded ALGOSOC project is developing a new approach to understanding and responding to the consequences of machine learning algorithms for contemporary societies. The project will examine how 21st century machine learning algorithms are learning to recognise, attribute and infer the characteristics of entities, more specifically people, groups and objects. Show the project objective Hide the project objective Objective ALGOSOC develops a new approach to understanding and responding to the consequences of machine learning algorithms for contemporary societies. Rapid advancements in machine learning technologies are transforming social and political life in ways that uniquely challenge how we live in relation to others. The life chances of a person are now intimately connected to the attributes that an algorithm has learned from the data patterns of unknown others. From judgements in the criminal justice system to decisions on treatment pathways in health, the outputs of algorithms have become pivotal to the decisions and adjudications on the probable futures of individuals. While there is substantial academic and public emphasis on defining ethical codes of conduct for algorithmic decisions, there is a lack of attention to how machine learning algorithms remake the ethical relations that define a society. In short, existing research is focused on limiting the harms of the actions of algorithms, whereas ALGOSOC focuses on how algorithms are redefining the thresholds of what harmful, good, bad, or risky behaviour means in a society. The ALGOSOC project will examine how 21st century machine learning algorithms are learning to recognize, to attribute, and to infer the characteristics of entities (people, groups, and objects). In order to do this, the project will conduct a series of path-defining studies of societal domains of machine learning that, though they share algorithms in common, have not previously been researched in combination: behavioural biometrics and biomedical object recognition; consumer recommendation and criminal justice scoring; oncology treatment pathways and anomaly detection for security. The ALGOSOC project will provide new social science knowledge of what is taking place as machine learning algorithms travel across different domains and sites, and how precisely they learn by their exposure to different worlds of data. Fields of science medical and health sciencesclinical medicineoncologysocial scienceslawcriminologynatural sciencescomputer and information sciencesartificial intelligencemachine learning Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-2019-ADG - ERC Advanced Grant Call for proposal ERC-2019-ADG See other projects for this call Funding Scheme ERC-ADG - Advanced Grant Coordinator UNIVERSITY OF DURHAM Net EU contribution € 2 150 686,00 Address Stockton road the palatine centre DH1 3LE Durham United Kingdom See on map Region North East (England) Tees Valley and Durham Durham CC Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00 Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all UNIVERSITY OF DURHAM United Kingdom Net EU contribution € 2 150 686,00 Address Stockton road the palatine centre DH1 3LE Durham See on map Region North East (England) Tees Valley and Durham Durham CC Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
