This report provides an overview of AI Watch activities in 2019. AI Watch is the European Commission knowledge service to monitor the development, uptake and impact of Artificial Intelligence (AI) for Europe.,. As part of the European strategy on AI, the European Commission and the Member States pub
This report proposes an operational definition of artificial intelligence to be adopted in the context of AI Watch, the Commission knowledge service to monitor the development, uptake and impact of artificial intelligence for Europe.
In the light of the recent advances in artificial intelligence (AI), the serious negative consequences of its use for EU citizens and organisations have led to multiple initiatives from the European Commission to set up the principles of a trustworthy and secure AI. Among the identified requirements
The AI Index Report tracks, collates, distills, and visualizes data relating to artificial intelligence. Its mission is to provide unbiased, rigorously-vetted data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI.
The Techno-Economics Segment (TES) analytical approach aims to offer a timely representation of an integrated and very dynamic technological domain not captured by official statistics or standard classifications.
On 20.-21. May 2019, the Police and Human Right Programme (PHRP) of Amnesty International Netherlands held an expert meeting on predictive policing...
This report brings together data on patents, scientific publications, trademarks and designs of the world’s top corporate R&D investors to shed some light on the role they play in shaping the future of technologies and AI.
The application of artificial intelligence (AI) to music stretches back many decades, and presents numerous unique opportunities for a variety of uses, such as the recommendation of recorded music from massive commercial archives, or the (semi-)automated creation of music.
Why Machine Learning May Lead to Unfairness: Evidence from Risk Assessment for Juvenile Justice in Catalonia
In this paper we study the limitations of Machine Learning (ML) algorithms for predicting juvenile recidivism. Particularly, we are interested in analyzing the trade-off between predictive performance and fairness. To that extent, we evaluate fairness of ML models in conjunction with SAVRY
This report presents a European view of Artificial Intelligence (AI) based on independent research and analysis by the European Commission Joint