EU has recently release a proposal for regulating the use of AI in companies and regulations. As far as I can see, it is modelled on the GDPR regulations: Assigning responsibility to board and top management, sanctions expressed in terms of percentages of revenues, and (hopefully) som sort of “safe harbor” rules so you can be somewhat confident in that you are not doing anything wrong.
An interesting aspect here is that the EU is early with regards to the use of AI (yes, I know “AI” is a really diffuse concept, but leave that be for a moment) and is again taking the lead in regulation where Silicon Valley (and China) leads in implementation.
This means that managers, board members and researchers will need to learn more. I plan to do this by attending a webinar at Applied Artificial Intelligence Conference 2021. This webinar (May 27, at CDT 1430-1600) is open for everyone who registers. It will be facilitated by Elin Hauge, who is a member of one of the EGN networks I lead.
Recommended – see you there!
Last year (with Chandler Johnson and Alessandra Luzzi) and this year (with Chandler, Jadwiga Supryn and Prakash Raj Paudel), I teach a course called Analytics for Strategic Management. In this course executive students work on real projects for real companies, applying various forms of machine learning (big data, analytics, whatever you want to call it) to business problems. Here is a list (mostly anonymised, except for public organizations) list from this year:
- One group wants to use machine learning to predict fraud in public security contracts in a developing country
- A credit agency wants to predict which of their customers will pay their bills by the end of the month
- An engineering company wants to predict the number of hours needed to meet demand for each month in each department
- One group wants to predict housing prices within Oslo, to help house sellers get a realistic estimate of what their property is worth
- A higher education provider wants to predict which students are likely to fail or not qualify for an exam, to be able to intervene early
- A couple of municipalities want to predict who will accept a kindergarten allocation or not
- A telecommunications company wants to predict which customers will churn
- An Internet product company wants to predict necessary capacity for picking and shipping work every day
- One group wants to predict the likelihood of a road closing due to bad weather, in order to warn truck drivers so they can detour
- One group wants to predict the future financial health of companies based on employee engagement numbers
- One group wants to predict efficiency of production in a wind power park
And last year we had these projects:
- An investment company wanted to predict bankruptcies from media events
- Ruter, Oslo’s public transportation authority wanted to predict the number of passengers (for each station, to great precision) for one line on the metro
- A telecommunications company wanted to predict customer feedback scores from analyzing customer interactions (so the customer does not have to answer a survey afterwards)
- The Norwegian Health directorate wanted to predict general physician “fastlege” churn
- A commercial TV station wanted to predict subscriber churn
- An insurance company wants to identify customers likely to buy a group insurance package
- An online gaming company wanted to predict customer churn
- A large political party wanted to predict membership churn
- One group wanted to start a company based on using machine learning to diagnose hearing problems
- A large retail chain wanted to predict churn based on customer purchase patterns