Here we will outline a policy suggestion that addresses those issues while still providing a solution to the fundamental problem posed by companies not disclosing information about their recommendation systems.

Part two out of three in a blog post series about transparency and recommendation systems: “Providers of consumer-facing recommender systems should be required to reveal how they work to an independent public authority. They would have to describe which types of algorithms are used, and how these are optimized, i.e., their goal functions, and what types of input data have […]”

Part one out of three in a blog post series about transparency and recommendation systems, outlining a proposal for algorithmic transparency.

While it seems that algorithms at present mostly use our myopia to promote engagement, this may also give AI the potential to help us commit to long-term goals. It is a problem, however, that these goals sometimes disagree with the data-driven business models of platform providers.

We are entering a new chapter in the digitalisation of society, advancing from machines as decision support, to machines as “decision makers”. This is made possible by the many breakthroughs in the development of artificial intelligence (AI) in the last decade. There is reason to expect that AI will be used increasingly not only for private decisions, but also for decisions made by public authorities and institutions. Who is responsible when a machine commits a serious error?

AI sees you. Through an ever growing network of applications, microphones and cameras, data about your behavior is continuously collected almost wherever you are. We are getting accustomed to our digital trail being used to tailor ads and to make your Netflix binging more convenient. These things might seem harmless […]

Robots and automation are not things of the future; they are popping up in various places in the public sector. AI is increasingly used for both small and big decisions such as sorting incoming mail […]