Administration by algorithm

A risk management framework

Dados Bibliográficos

ID6149416
AutoresFrank Bannister (0000-0003-3673-0374, Trinity College Dublin, autor correspondente), Robert Connolly (0000-0003-3196-2889, Dublin City University), Regina Connolly (Dublin City University, Dublin, Ireland)
EditoresSarah Giest (0000-0001-8201-6943), Stephan Grimmelikhuijsen (0000-0002-1553-6065)
Ano2020
Volume25
Fascículo4
Páginas471-490
Data de publicação2020-12-04
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoInformation Polity (JOURNAL)
Identificadores do periódicoISSN: 1875-8754 • E-ISSN: 1570-1255
EditoraIOS Press (PUBLISHER • NL)
DOI10.3233/ip-200249
OpenAlexW3112426303
IdiomaEN
Citações recebidas32
Referências citadas17

Algorithmic decision-making is neither a recent phenomenon nor one necessarily associated with artificial intelligence (AI), though advances in AI are increasingly resulting in what were heretofore human decisions being taken over by, or becoming dependent on, algorithms and technologies like machine learning. Such developments promise many potential benefits, but are not without certain risks. These risks are not always well understood. It is not just a question of machines making mistakes; it is the embedding of values, biases and prejudices in software which can discriminate against both individuals and groups in society. Such biases are often hard either to detect or prove, particularly where there are problems with transparency and accountability and where such systems are outsourced to the private sector. Consequently, being able to detect and categorise these risks is essential in order to develop a systematic and calibrated response. This paper proposes a simple taxonomy of decision-making algorithms in the public sector and uses this to build a risk management framework with a number of components including an accountability structure and regulatory governance. This framework is designed to assist scholars and practitioners interested in ensuring structured accountability and legal regulation of AI in the public sphere

Accountability · Business · Computer security · Corporate governance · Economics · Management science · Order (exchange · Political science · Private sector · Public sector · Risk analysis (engineering · Risk management · Taxonomy (biology · Transparency (behavior · Blockchain Technology Applications and Security · Computer Science · Ethics and Social Impacts of AI · Law · Regulation and Compliance Studies · Finance