Government Information Quarterly  (October 2022) Administrative errors in unemployment insurance (UI) decisions give rise to a public values conflict between efficiency and efficacy. We analyze whether artificial intelligence (AI) – in particular, methods in machine learning (ML) – can be used to detect administrative errors in UI claims decisions, both in terms of accuracy and normative tradeoffs. We use 16 years of US Department of Labor audit and policy data on UI claims to analyze the accuracy of 7 different random forest and deep learning models.

 Asian Development Bank  (March 2021) Digitalization has gained more prominence amid COVID-19 and has highlighted the value of big data for public sector management. The brief explains the potential benefits of big data for public services such as health, social protection, and education and how this can contribute to the post-pandemic recovery. It also assesses the key enablers and policy actions needed to realize big data benefits in the region. 

FrontPageAfrica (25.06.2019) The Government of Liberia, through the Ministry of Gender, Children and Social Protection, Monday signed a US$2 million contract with a Ghanaian company — “Esoko” — to collect social data in four counties Nimba, Bong, Bomi and Maryland Counties.

Cairn.info  - Regards 2017/2 (N° 52)  En 2016, les Caisses d’allocations familiales (CAF) ont détecté près de 43 000 fraudes, soit 8 % de plus que l’année précédente, pour un montant de 275 millions d’euros. La Caisse nationale (CNAF) a expliqué cette hausse par l’accroissement de l’efficacité des dispositifs de contrôle et non par l’augmentation de la fraude elle-même. Ces résultats, ajoutés à ceux des années précédentes sont notamment liés au développement du data mining, un procédé qui repose sur le big data.

Corpus législatif suisse de lutte contre la fraude aux prestations

Explication et légitimité de la lutte contre la fraude dans le domaine de l'emploi

The ongoing digital transformation of economies and societies holds many promises to spur innovation, generate efficiencies, and improve services, and in doing so boost more inclusive and sustainable growth as well as enhance well-being. But these benefits go hand-in-hand with disruptions. Our interactions with one another and with society more broadly are being transformed, as are the nature and structure of organisations and markets, raising important issues around jobs and skills, privacy, security, and how to ensure that technological changes benefit society as a whole, among others.

SAP Institute for Digital Government (May 2018) This series examines two areas of interest to members of the International Social Security Association (ISSA):  contemporary applications of big data analytics within the context of Social Security; and the opportunity for emerging technologies to extend and enhance current analytical techniques to deliver better social and economic outcomes.

SAP Institute for Digital Government (May 2018) This series examines areas of interest to members of the International Social Security Association (ISSA) related to emerging technologies enabling data-driven policy and practice. All modern governments have rich stores of customer and case data, but most government agencies struggle to convert this data into meaningful information and actionable insights.

Many industries have been disrupted by the influx of new technologies in the Information Age. Healthcare is no different.Particularly in the case of automation, machine learning, and artificial intelligence (AI), doctors, hospitals, insurance companies, and industries with ties to healthcare have all been impacted – in many cases in more positive, substantial ways than other industries.