@peter-2: ML is now beyond the point of doing broad analyis and coming up with customer groups that are good targets for the next campaign. More and more applications train a model based on generic data and then use that for individual predictions. E.g., you aply for a healthcare policy, provide some data, and then the AI determines that you are 65% likely to be an increased risk so you have to pay more.or even get refused. And yes, I can certainly see how customers would ask for an explanation.

@steve-2: When reading your editorial I actually get concerned over an issue that is broader than GDPR. That concern is to an extent embedded in my response to Peter. Apparently, we are now using tool where even the people that decide to implement those tools and change the business process to use them do not really understand how the results are computed. And yet we use these results and allow them to have high impact on our business decisions. Law enforcement or border control use AI and ML algorithms to decide who gets screened and who can pass. Insurance companies use this data to determine who is accepted for a policy. Perhaps one day (perhaps now already though I don't think so) doctors will use these algoruthms to determine which patient will be the recipient for a kidney that has become available. If nobody can explain how those results are computed, then nobody can verify the results. So why should we trust them? What are we building our future on?