Trip Report: Royal Society & KNAW – Responsible Data Science
Last week, I had the pleasure to be able to attend a bilateral meeting between the Royal Society and the KNAW. The aim was to strengthen the relation between the UK and Dutch scientific communities. The meeting focused on three scientific areas: quantum physics & technology; nanochemistry; and responsible data science. I was there for the latter. The event was held at Chicheley Hall which is a classic baroque English country house (think Pride & Prejudice). It’s a marvelous venue – very much similar in concept to Dagstuhl (but with an English vibe) where you are really wholly immersed in academic conversation.
One of the fun things about the event was getting a glimpse of what other colleagues from other technical disciplines are doing. It was cool to see Prof. Bert Weckhuysen enthusiasm for using imaging technologies to understand catalysts at the nanoscale. Likewise, seeing both the progress and the investment (!) in quantum computing from Prof. Ian Walmsley was informative. I also got an insider intro to the challenges of engineering a quantum computer from Dr. Ruth Oulton.
The responsible data science track had ~15 people. What I liked was that the organizers not only included computer scientists but also legal scholars, politicians, social scientists, philosophers and policy makers. The session consisted primarily of talks but luckily everyone was open to discussion throughout. Broadly, responsible data science covers the ethics of the practice and implications of data science or put another way:
For more context, I suggest starting with two sources: 1) The Dutch consortium on responsible data science 2) the paper 10 Simple Rules for Responsible Big Data Research. I took away two themes both from the track as well as my various chats with people during coffee breaks, dinner and the bar.
1) The computer science community is engaging
It was apparent through out the meeting that the computer science community is confronting the challenges head on. A compelling example was the talk by Dr. Alastair Beresford from Cambridge about Device Analyzer a system that captures the activity of user’s mobile phones in order to provide data to improve device security, which it has:
He talked compellingly about the trade-offs between consent and privacy and how the project tries to manage these issues. In particular, I thought how they handle data sharing with other researchers was interesting. It reminded me very much of how the Dutch Central Bureau of Statistics manages microdata on populations.
Another example was the discussion by Prof. Maarten De Rijke on the work going on with diversity for recommender and search systems. He called out the Conference on Fairness, Accountability, and Transparency (FAT*) that was happening just after this meeting, where the data science community is engaging on these issues. Indeed, one of my colleagues was tweeting from that meeting:
Julian Huppert, former MP, discussed the independent review board setup up by DeepMind Health to enable transparency about their practices. He is part of that board. Interestingly, Richard Horton, Editor of the Lancet is also part of that board Furthermore, Prof. Bart Jacobs discussed the polymorphic encryption based privacy system he’s developing for a collaboration between Google’s Verily and Radboud University around Parkinson’s disease. This is an example that even the majors are engaged around these notions of responsibility. To emphasize this engagement notion even more, during the meeting a new report on the Malicious Uses of AI came out from a number or well-known organizations.
One thing that I kept thinking is that we need more assets or concrete artifacts that data scientists can apply in practice.
For example, I like the direction outlined in this article from Dr. Virginia Dignum about defining concrete principles using a design for values based approach. See TU Delft’s Design for Values Institute for more on this kind of approach.
2) Other methods needed
As data scientists, we tend to want to use an experimental / data driven approach even to these notions surrounding responsibility.
Even though I think there’s absolutely a role here for a data driven approach, it’s worth looking at other kinds of more qualitative methods, for example, by using survey instruments or an ethnographic approach or even studying the textual representation of the regulatory apparatus. For instance, reflecting on the notion of Thick Data is compelling for data science practice. This was brought home by Dr. Ian Brown in his talk on data science and regulation which combined both an economic and survey view:
Personally, I tried to bring some social science literature to bear when discussing the need for transparency in how we source our data. I also argued for the idea that adopting a responsible approach is also actually good for the operational side of data science practice:
While I think it’s important for computer scientists to look at different methods, it’s also important for other disciplines to gain insight into the actual process of data science itself as Dr. Linnet Taylor grappled within in her talk about observing a data governance project.
Overall, I enjoyed both the setting and the content of the meeting. If we can continue to have these sorts of conversations, I think the data science field will be much better placed to deal with the ethical and other implications of our technology.
- Regulating Code – something for the reading list
- Somebody remind me to bring a jacket next time I go to an English Country house!
- I always love it when egg codes get brought up when talking about provenance.
- I was told that I had a “Californian conceptualization” of things – I don’t think it was meant as a complement – but I’ll take it as such 🙂
- Interesting pointer to work by Seda Gurses about in privacy and software engineering from @1Br0wn
- Lots of discussion of large internet majors and monopolies. There’s lots of academic work on this but I really like Ben Thompson’s notion of aggregator’s as the way to think about them.
- Merkle trees are great – but blockchain is a nicer name 😉