London Data Week wraps up on Sunday, and what a week it’s been! Kudos to organisers Sam Nutt and Jennifer Ding for the huge amount of energy and passion they invested in making this idea a reality, and I’m looking forward to seeing what they have in store for us next year.
My highlights of the week? Well, of course, I really enjoyed being part of the event that was hosted at the Royal Statistical Society on Tuesday. The Statisticians for Society workshop brought together charities and statisticians to explore ways in which data and statistics can support third sector organisations to deliver on their charitable aims as well as demonstrate to communities and funders the impact they are having. There’s a nice selection of case studies of successful past projects on the RSS website, and hopefully the London Data Week event will result in several new additions to this collection in due course.
I wasn’t able to attend this event myself, but I’m really looking forward to viewing the outputs of the Better Images of AI workshop, which was also held on Tuesday. Real World Data Science has used several of the group’s images to illustrate past articles (here, here and here), so I’m excited to see what gets added to the image gallery in the coming weeks.
Sticking with the AI theme, I also got to explore the “AI: Who’s Looking After Me?” exhibition at the Science Gallery, where I found myself unexpectedly moved by one installation in particular – an artificial landfill of broken and discarded tablets and smart speakers, explaining matter-of-factly, but with an unmistakable air of mournfulness, that they had been replaced “by a newer model that is better because it is lighter, or heavier, or bigger, or smaller…”. Fortunately I was able to cheer myself up with another exhibit, which tasks visitors with helping a chatbot to define and understand love.
Later on in my visit to the Science Gallery (which was actually last Thursday, before London Data Week officially began), I listened to a panel debate on “Building Better AI in the Open”, featuring Margaret Mitchell of HuggingFace, Lara Groves of the Ada Lovelace Institute, and Irini Papadimitriou of FutureEverything, facilitated by artist and machine learning design researcher Caroline Sinders. A recording of the panel is below, and well worth a watch for discussion of:
- the advantages of open source versus closed source
- the role of public participation in AI
- what transparency in AI development should look like
- issues of accountability in AI applications.
Jennifer Ding followed up the panel with a thoughtful post on the benefits of open source AI, and for more on trustworthy AI – and the need for transparency, explainability, and fairness – check out Maxine Setiawan and Mira Pijselman’s recent Real World Data Science article, “Trusted AI: translating AI ethics from theory into practice”.
- Copyright and licence
- © 2023 Royal Statistical Society
This article is licensed under a Creative Commons Attribution 4.0 (CC BY 4.0) International licence. Thumbnail image by Benjamin Davies on Unsplash.
- How to cite
- Tarran, Brian. 2023. “Teaching a chatbot about love, and other adventures from London Data Week.” Real World Data Science, July 7, 2023. URL