Blog: Developing the ICO AI Auditing Framework: an update

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Blog: Developing the ICO AI Auditing Framework: an update

Simon McDougall, Executive Director for Technology and Innovation reflects on the progress made in developing the ICO approach to auditing Artificial Intelligence (AI), and some of the broad themes emerging from the feedback received so far. 

We launched
this blog in March to provide regular updates on the development of the ICO
Auditing Framework for AI, and encourage organisations to engage with us on
this work. Since then we have set out the proposed
of the framework, and explored the data protection
challenges and possible controls in relation to five specific risk areas:
  1. Meaningful
    human reviews
    in non-solely automated AI systems
  2.  Accuracy of AI systems outputs and performance measures;
  3. Known security risks exacerbated by AI;
  4. Explainability
    of AI decisions to data subjects; and
  5. Human biases
    and discrimination
    in AI systems.
This ongoing and more
informal approach to consultation is new, but we are delighted with the positive
response and engagement it has generated so far.
The blog readership
in particular has exceeded our expectations with each blog receiving more than
10,000 views. In addition, over 50 organisations and individuals have so far
shared their thoughts with us, either by commenting publicly on the individual
posts or via the
. Personally, I would have liked there to have been
more comment and discussion in each blog’s comments section, but in practice
most people have preferred to feedback directly to us via email. This is still
enormously valuable of course, and we will keep trying to innovate in how we
engage with people as we launch other initiatives.
The visibility
generated by the blog also created additional opportunities for the AI
framework team to discuss our work, with both small and large stakeholder
groups, at policy forums, roundtables and conferences.
breadth and depth of this engagement is helping to shape our thinking and approach.
We have received some detailed technical feedback on a number of aspects of AI
and data protection, but a number of common and broader themes have also
A number of people asked us to
clarify what we mean by “AI”. In reality, we use the term AI because it has
become a mainstream way for organisations to refer to a range of technologies
that mimic human thought. Some of these are quite new and ground-breaking, such
as modern deep learning approaches to object recognition, while others have
been around for a while; decision trees, for example. But they all have been
referred to as AI at some point. 
Before we launched the blogs
we gave some thought to working on our definition of AI, but we decided to
focus our efforts on the underlying data protection issues instead. This is because
while AI has become an umbrella term, any technology underneath it will involve
or enable the large scale automated processing of large amounts of data. If
this processing involves the use of personal data for purposes of profiling,
identification and decision-making, then we are concerned with any new or
heightened data protection risks that may arise. The primary objective of our
framework is to give us a solid methodology to assess whether organisations
using any such technologies are compliant with data protection law. When
necessary the framework will also call out when identified risks are specific
to certain technologies, such as facial recognition.
Some respondents have also argued that a number of
issues, such as security, are not unique to AI. We agree that only a few of the
risks arising from the use of AI will be completely unique. For those risks
that are not new, our job is to understand how and to what extent they can be exacerbated
by AI, and whether additional controls are required to identify, measure,
mitigate, and monitor them. We do not expect organisations to redesign their
risk management practices from scratch, but we do expect them to review them
and make sure they remain fit-for-purpose if AI is used to process personal
The post on the topic
in non-solely automated AI systems attracted the
most interest to date, and generated more than 20,000 views. A number of stakeholders
highlighted the trade-offs that having a human-in-the-loop may entail: either in
terms of a further erosion of privacy, if human reviewers need to consider
additional personal data in order to validate or reject an AI generated output,
or the possible reintroduction of human biases at the end of an automated
process. Trade-offs is a key area of risk in our framework, and therefore we
will make sure to reflect this feedback in the associated blog post which we will
publish soon.
Finally, some of the comments
also highlighted an important shortcoming in our initial approach. We were
aware that AI may be developed or run partially or completely by third parties,
rather than in-house. However, the feedback strongly suggests that this is the
case the majority of the time. As we finalise our framework, we will need to consider
further the challenges this presents for data controllers in exercising adequate
levels of oversight and control.

I want to conclude by thanking all stakeholders that have engaged with us so far, and encourage you to continue to do so until the end of October, when this initial consultation phase will conclude. Your feedback is genuinely valuable in improving our work, and we appreciate the time you are taking to help us.

Our plan remains to publish the formal consultation paper on our AI Auditing Framework no later than January 2020.

Simon McDougall is Executive Director for Technology Policy and Innovation at the ICO where he is developing an approach to addressing new technological and online harms. He is particularly focused on artificial intelligence and data ethics.

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