What is the risk of AI?

Happy New Year from all of us at the DataDataData blog. Let's start the year out with a look at Artificial Intelligence - actually a story thereof. That is, "Leave Artificial Intelligence Alone" by Andrew Burt, appearing in last Friday's NYTimes' Op-Ed section.

Would that people could leave AI alone! You cant pick up a the supermarket sales flyer without hearing someone's bit on the subject. As Burt points out, a lot of the discussion is unnecessarily - and unhelpfully - doomy and gloomy. Burt points out that AI lacks definition. You can see the effect in much of the criticism, which lashes out with haymakers at a phantom - one that really comprises very many tributary technologies - quite various ones at that.

Some definition, some narrowing of the problem scope is in order.

If you study the history of consumer data privacy you discover, as Burt reminds, the Equal Credit Opportunity Act of 1974. Consider it as a pathway for data privacy that still can be followed.

Burt also points to SR 11-7 regulations that are intended to provide breadcrumbs back to how and why trading models were constructed, so that there is good understanding of risk involved in the automated pits of Wall Street.

Within the United States’ vast framework of laws and regulatory agencies already lie answers to some of the most vexing challenges created by A.I. In the financial sector, for example, the Federal Reserve enforces a regulation called SR 11-7, which addresses the risks created by the complex algorithms used by today’s banks. SR 11-7’s solution to those challenges is called “effective challenge,” which seeks to embed critical analysis into every stage of an algorithm’s life cycle — from thoroughly examining the data used to train the algorithm to explicitly outlining the assumptions underlying the model, and more. While SR 11-7 is among the most detailed attempts at governing the challenges of complex algorithms, it’s also one of the most overlooked.

Burt sees such staged algorithm analysis as a path for understanding AI and machine learning going forward.

It is good to see there may be previous experience that can be tapped when looking at how to handle AI decision making - as opposed to jumping up and down and yelling 'the sky is falling.'

As he says, it is better to distinguish the elements of AI application according to use cases, and look at regulation specifically in verticals - where needed. 

Spoke with Andrew Burt last year as part of my work for SearchDataManagement - linked to here: Machine learning meets Data Governance.  - Jack Vaughan