Non-Markov Decision Processes and Reinforcement Learning
We present non-Markov decision processes, where rewards and dynamics can depend on the history of events. This is contrast with Markov Decision Processes, where the dependency is limited to the last state and action.
ALIGNER Fundamental Rights Impact Assessment
Artificial intelligence can enhance law enforcement agencies’ capabilities to prevent, investigate, detect and prosecute crimes, as well as to predict and anticipate them.
The regulatory perspective
In this webinar, we heard from regulators at different administrative levels, practitioners and from academics about their experiences, needs and challenges.
Recommendation on the Ethics of Artificial Intelligence
This Recommendation aims to provide a basis to make AI systems work for the good of humanity, individuals, societies and the environment and ecosystems, and to prevent harm. It also aims at stimulating the peaceful use of AI systems.
What is the environmental footprint of artificial intelligence?
AI-enabled technologies have huge potential to support positive climate action. As the use of AI increases, governments and policy makers need to understand AI’s environmental impacts so that they can make evidence-based decisions.
OECD FRAMEWORK FOR THE CLASSIFICATION OF AI SYSTEMS
To help policy makers, regulators, legislators and others characterise AI systems deployed in specific contexts, the OECD has developed a user-friendly tool to evaluate AI systems from a policy perspective.