Dr Anders Kofod-Petersen
Director, Alexandra Institute
Anders is the Deputy Director of the Alexandra Institute, and a Professor of Artificial Intelligence at the Norwegian University of Science and Technology (NTNU) in Trondheim, a leading university in artificial intelligence and machine learning.
In addition, he is the manager of the Alexandra Institute’s Data Science and Engineering Lab, which offers top-level expertise based on the latest technologies and methods to analyze and improve existing software systems and to develop new ones. This lab combines the fields machine learning, IoT and classical software engineering, and has specialists in software architecture, cloud computing, data mining and machine learning as well as in mobile and web-based solutions.
Anders is also project leader of the public-private partnership DABAI (Danish Center for Big Data Analytics Innovation) – with a total budget of DKK 117 million, of which 45 million has been funded by the Innovation Fund Denmark. DABAI is the largest collaboration so far between private companies and the public sector in order to create an overview of data and how to exploit their full potential.
Ethics, bias, fairness and what’s wrong with (artificial) intelligence?
Artificial Intelligence (AI) is in its 65th year in a pretty good shape. In many ways, it has finally grown up and out of the laboratories, into the real world. The application of AI is, on a daily basis solving new interesting problems, creating new value and forcing us to take a critical look at what we mean when we say, “being ethical”, “having bias”, “being fair” or “being trustworthy”. The latter becomes very important when AI is applied to services for the basic needs in Maslow’s hierarchy. This talk will cover why certain properties of AI-methods are the best thing the has ever happen to ethics, fairness and supporting humans.
LISTEN TO ANDERS ON OUR PODCAST
Listen to Anders talk about the Danish Natural Language Processing repository, why collaboration is essential across both companies and disciplines, and the ways in which companies can be encouraged to build AI responsibly without involving regulation. Or read the conversation here.