Algorithms governance is a key topic, which is receiving more and more attention as we enter this 21st century. The rise of this complex and difficult topic is no surprise, since “software is eating the world” – i.e., the part of our lives that is impacted by algorithms is constantly growing – and since software is “getting smarter” every year, with the intensification of techniques such as Machine Learning or Artificial intelligence. The governance question is also made more acute since smarter algorithms are achieved through more emergence, serendipity and weakening of control, following the legendary insight of Kevin Kelly in his 1995 “Out of control” best seller: “ « Investing machines with the ability to adapt on their own, to evolve in their own directions, and grow without human oversight is the next great advance in technology. Giving machines freedom is the only way we can have intelligent control. » Last, the algorithmic governance issue has become a public policy topic since Tim O’Reilly coined the term “Algorithmic Regulation” to designate the use of algorithms for taking decision in public policy matters.
2. Algorithm Regulation
First, I should start with my usual caveat that you should read the report versus this very simplified and partial summary. The five recommendations can be summarized as follows:
- Design a software platform to facilitate the study, the evaluation, and the testing of content / recommendation algorithms in a private/public collaboration opened to research scientists
- Create an algorithm audit capability for public government
- Mandate private companies to communicate about algorithm behavior to their customers, through a “chief algorithm officer role”
- Start a domain-specific consultation process with private/public stakeholders to formalize what these “smart content management services” are and which best practices should be promoted nationally or internationally.
- Better train public servants who use algorithms to deliver their services to citizens
3. Data is the New Code
- Input data
- Purpose (intent) of the algorithm
- “training” / “growing” protocol
- Output data