AI and DP

Laurence Eastham offers a guarded welcome to a lengthy discussion paper from the ICO

Even those with the keenest interest in AI and its interaction with data protection may have struggled to find time in a busy schedule to read all of the ICO’s latest version of its paper on ‘Big data, artificial intelligence, machine learning and data protection’. But if you have the time, it’s good and the 113-page pdf is here. Or, bearing in mind that it is essentially a discussion paper, you could take a tip from me, following my noble self-sacrifice in reading (most of) it, and just read pp 90 to 98 and save the very useful Annex on ‘Privacy impact assessments for big data analytics’ for a later date.

The bit that interested me most was on Algorithmic transparency (pp 86 to 89). You’ll notice that the section in question is just three and a bit pages out of 113 and that rather reflects the fact that the ICO is more at home with Big Data than with AI. This relatively brief treatment probably reflects the view expressed by Jo Pedder, the ICO’s Interim Head of Policy and Engagement, in a blog post introducing the paper that:

‘whilst the means by which the processing of personal data are changing, the underlying issues remain the same. Are people being treated fairly? Are decisions accurate and free from bias? Is there a legal basis for the processing? These are issues that the ICO has been addressing for many years, through oversight of existing European data protection legislation.’

Up to a point, Lord Copper – up to a point. I think AI in the wild makes life a lot more complicated than that. There are areas where the old answers won’t work

The ICO paper’s own brief summary of its suggestions on algorithmic transparency are as follows:

  • Auditing techniques can be used to identify the factors that influence an algorithmic decision.
  • Interactive visualisation systems can help individuals to understand why a recommendation was made and give them control over future recommendations.
  • Ethics boards can be used to help shape and improve the transparency of the development of machine learning algorithms.
  • A combination of technical and organisational approaches to algorithmic transparency should be used.

It is hard to disagree with the usefulness of each of these suggestions but it feels like a flimsy fence for the AI monsters that might face us. Plus I feel that organisations that have an ‘ethics board’ risk outsourcing ethics when their business model should be imbued with an ethical approach, not to mention the danger of ‘ethics boredom’.

Fortunately, Jo Pedder’s blog post refers to a plan to set up a research fund which will fund research in this area (among others) and I hope that produces more satisfying answers on AI. I also hope that SCL members will contribute to the continued debate on the interface between AI and data protection. These pages are open for that debate.

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