Laura Carter

This year, I’m working through Nathalie Tasler’s prompts. I'll keep updating this post as the month goes on.

Day 9

Cheating a little here, with from the Scriberia illustrations commissioned during a Turing Way book dash, back when I was in the very early stage of thinking about this idea.

Day 8

Mastodon: I'm writing a book about how different people are—and are not—represented in different data sets. In particular, it's about how the ways that different actors collect data exclude or misrepresent people who are in some way out of the mainstream: because of their #gender, their way of life, their identity/ies.

LinkedIn: My current book project focuses on exclusion and misrepresentation in data sets. When we collect data about people, the information that we choose to collect is a choice: and not everyone fits neatly into predefined categories.

Day 7

I have a tendency to ramble in my writing, and to use a lot of run-on sentences. I'd like to get better at writing more concisely—and at editing my own writing.

Day 6

I don't take as much care of my readers as I should: at least, not in academic writing. Sometimes this is because I'm racing to meet a deadline and 'done' is better than 'perfect,' sometimes it's because I'm so fed up of a paper that I just want shot of it. And sometimes it's because I feel pressure to 'perform' academic inscrutability. I'm trying to move away from that—my book project idea is for a general reader—but it does creep in, especially when I've been reading a lot of academic literature that tends towards the pompous or the jargon-y.

Day 5

A possible abstract for a paper or conference submission:

Missing drafts Machine learning—including generative AI—relies on training data, whether it is in the form of structured data, unstructured text, or images. But this data didn't spring into existence fully-formed as a .html, .txt or .csv file. This paper will make visible the information and thought that went into creating the training data that is used to build machine learning, large language and image generation models. From court case submissions that are used in crafting the final judgement produced by a judge, to tried-and-failed experiments which never produce academic papers, to the previous drafts of novels which exist only on the writer's computer: this paper will show how this hidden information is vital to understanding how training data was created, and therefore to understanding the performance of machine learning models.

Day 4

To write, I need quiet, a door that closes, to be left alone. I need to spend time settling in to the work, to pick up where I left off before, and to quiet my brain from the day-to-day.

This isn't always feasible! But it's easier to come by in the afternoons.

Day 3

Weakness: sometimes it is useful to spend time looking for a reference! Future Laura isn't always grateful when Past Laura leaves her with all the work to do. Sometimes looking for the reference helps me realise that the point I was trying to make isn't actually that useful or relevant.

Strength: when I am writing well, I am able to put a lot of words down—and it's always easier to edit existing text than come up with it in the first place.

Day 2

When I am writing at my best, I am like a well-oiled machine: the words flow easily from my brain through my fingers and on to the screen. I don't get bogged down in looking for the perfect reference or exploring a footnote – I simply note where these are needed and keep going.

Day 1

I'm hoping to use these prompts to help me think through my current book project!

The public doesn’t want AI ‘mainlined into the veins’ of the UK: at least not when it comes to the public sector.

How do we know? Well, my former colleagues at the Ada Lovelace Institute have been asking them. Over the last six years, Ada has done a lot of research into what the public want from data and AI. Earlier this year, they asked me to help pull together findings from their work with 16,000 people in four nationwide attitudinal surveys and 400 people in deeper qualitative studies, to help answer the question of how the general public wants the UK public sector to use AI.

Here's what we found:

1. There is no single view of AI: public perceptions are nuanced and context-dependent. People assess tools in context and can identify the benefits, opportunities, risks and harms of individual AI use cases.

2. Experiences and demographics shape people’s expectations. People’s understanding, trust and comfort with AI are affected by their personal characteristics, as well as their direct and indirect experiences of technology and the institutions using it. These experiences can exacerbate concerns about AI’s impact on existing inequalities.

3. Private profits, public doubts: there are concerns about the power and profits of technology companies in public services. This intersects with concerns around transparency, regulatory powers and access to data.

4. Support is conditional: the public want evidence, explainability and involvement. The public value explainability above accuracy when it comes to AI tools, and they expect clear evidence on efficacy and impacts to justify use. There is a desire for those affected to have meaningful involvement in shaping decisions about public sector AI.

5. Strong governance is a prerequisite for trust. The public increasingly ask for stronger governance of AI, along with clear appeals and redress processes if something goes wrong. The public are not convinced existing regulations are adequate to ensure that public sector AI works for everyone, supports public good and prioritises people over profit.

6. Social impact matters: the public oppose uses of AI that could create a ‘two-tier society’. Discrimination and bias are important concerns, especially in essential services, and people expect AI in the public sector to accommodate pluralism and diversity.

The public is more than capable of understanding different use cases, implementations and outcomes, and they care if, where and how public sector AI is used. Public legitimacy matters: if the UK government wants to roll out AI across the public sector, it is important that it’s done properly.

You can read the whole report here.

I’ve had a lot of thoughts in the wake of the UK Supreme Court’s decision a couple of weeks ago to define women and sex in terms of biology. Others have said better than me that the judgement is transphobic, it will hurt (indeed, has already hurt) people, and that it is a misreading of everything that the Equality Act stands for. Many people—trans and cis—have been protesting the decision, and a judge plans to take the UK to the European Court of Human Rights.

As a human rights expert who started working on legal gender recognition back around 2012, I can say that it is a bad judgement, and I hope that the ECtHR recognises that: but the process will take years. In the meantime, protections against discrimination on the basis of 'transgender status' still exist in the law, but decision-makers are using this ruling to exclude trans people. Toilets—predictably, for anyone who has followed this issue and been exposed to the inherent creepiness of people thinking about the genitalia of the person in the next cubicle—are one area of exclusion. But another area is sport. Excluding trans women, apparently, is necessary for the safety of women and girls.

I was a sporty girl. I wasn't exceptional at any particular sport, but a height advantage (I was taller than most of my classmates from about the age of nine), generally good spatial awareness, and a decent ability to follow instructions and pick up rules quickly made me a competent B-team player in most of the sports I tried: and I liked sports, so I tried most of them. That brought me into contact with a whole load of coaches.

Let's talk about three of them. I'm going to call them Pat, Paul, and Mr S.

Pat taught swimming at my secondary school.

Paul coached the rowing team at my secondary school.

Mr S. was another rowing coach, working for a club we sometimes competed against. Paul and Mr S. also coached elite junior teams in the summer, along with several other school coaches, Some of my teammates were selected for those teams (not me. I remained a B-team player my whole adolescence): some ended up in boats coached by Paul, some by other coaches.

Once a week, starting from year seven, Pat herded my class of girls (it was a girls' school) into the changing rooms to get changed into their swimsuits.

By the summer term, the rowing teams for the junior European and World championships had already been selected, but we were still racing as a school team to finish out the season. We'd sit in the minibus (rowing competitions involved a lot of sitting in minibuses between heats), eating carbohydrates, pointing out who was on which team, and gossiping about their chances.

At one of these competitions, I was sitting in the minibus with Paul and another girl, H. Paul was in the front, doing something with paperwork, and not really listening to us talk. H pointed out a man walking past. That's Mr. S, she said. He's coaching some of the elite teams this summer. Not ones that any of us are in. Which is good, she said, because I've heard he's kind of creepy with the girls he coaches.

Once we were all in the swimming pool changing rooms, Pat...stayed. Sitting on a bench at the side of the classroom, watching as we changed. Watching us take off our uniforms and put on swimsuits. It's been more than 20 years, and I could still draw you the layout of the changing room, and show you exactly where Pat sat and watched.

Paul was listening, it turns out. I can also remember exactly what he said to H and me in the minibus that afternoon. Mr S. isn't coaching any of you, Paul said, because I put my foot down and said I didn't want him coaching any of my girls. Because he's creepy? H asked. Yes, Paul said, and I don't want him around any of you.

This was in the late 90s and early 00s in the UK. There are better laws and policies safeguarding children now. I think—I hope—it would be harder for people like Pat and Mr S. to slip through the net. To abuse their positions as adults working in children's sports, to invade their privacy and make them feel like objects.

Mr S. was a