My talk on caring AIs in recent sci-fi novels

I’m giving a talk at an actual f2f academic conference today, Critical Borders, Radical Re(visions) of AI, in Cambridge. I was particularly excited to see this conference because it’s organised by the people who edited AI Narratives A History of Imaginative Thinking about Intelligent Machines, a really useful anthology of essays on stories about AI ranging from the Ancient Greek myths about the autonomous machines Hephaistos built, via medieval ideas about magical mirrors and golems to twentieth century scifi.

Thumbnail image of PDF handout for my talk.

Here is the PDF Handout for my paper “Artificial Intelligence is Social and Embodied: AIs that Care in Contemporary Science Fiction”. I would love feedback, if you have any – I’ll be revising this before it eventually hopefully ends up as a full paper. I’m presenting at 13:20 UK time today, and it’ll be streamed (and I think archived?) on YouTube here, with the many other interesting talks happening today.

Yesterday I was able to sit in an actual auditorium and hear Ruha Benjamin speak, and today the conference itself starts. It’ll be live-streamed on YouTube as well and about 2/3 of the speakers will be remote. It’ll be interesting to see how a hybrid conference works.

My paper is about eleven science fiction novels published in the last five years where caring AIs are main characters:

AuthorTitleYearAI character
Becky ChambersA Closed and Common Orbit2016Sidra, Owl
Annalee NewitzAutonomous2017Paladin, Med
Martha WellsMurderbot series (2017-21)2017Murderbot
Neal Shusterman Thunderhead2018Thunderhead
Yudhanjaya WijeratneThe Salvage Crew2018Amber Rose 348
Ian McEwanMachines Like Me2019Adam
Carole StiversThe Mother Code 2020Rho-Z (Rosie)
Bjørn VatneDød og oppstandelse2020Oda
William GibsonAgency2020UNISS (Eunice)
S. B. DivyaMachinehood2021Welga/dakini
Kazuo IshiguroKlara and the Sun2021Klara

I’m using this to explore how actual (non-fictional) AI is also always social and embodied. This is a work in progress, and I’m trying out the Mumford Method, where you write up a concise handout, present using this several times, revising the handout each time to integrate feedback, and then write it up as a full paper when the ideas are throughly worked through.

If you’d like to look at my thoughts, I would really love feedback, as this is very much a work-in-progress.

I usually just start writing without a clear idea of where I’m going, which often works well, but also often requires a LOT of work and confusion when I’m revising, and has led to many abandonned half written papers. So I’m curious as to whether this method will work for me. I’ve enjoyed writing the handout – I like the constrained space and having the whole structure laid out. It really makes me think about what the point of the paper is rather than just writing out bits I enjoy. I do wonder whether the very concise style of writing will affect my final writing style though. Will I end up less essayistic than I’d like?

Here is the video feed from today’s conference – I’m in the 13:20 panel (UK time) today.

19. October 2021 by Jill
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Google is your first reader: How to write an academic paper that machines can easily read

  1. Put your main concept in the first part of your title, not in the subtitle.
  2. Use the format “X is [simple definition]” in your abstract.
  3. Use images and be aware that the first image will be used in many previews, so consider thinking of it as a graphical abstract.

OK, that was a test. The real title of this blog post is:

Google is your first reader: How to write research papers for machines

You see, after reading more about how Google selects its “Featured Snippets”, I learnt that in order to blog for machines (and thus get more human readers too) you should start with a question and then immediately follow with a short list.

To be honest, I don’t even know whether the rules I’ve listed are what really work, but then, community folklore about how algorithms work is common (Bishop 2019, 2020, van der Nagel 2018), so why not add to it.

And yes, this is all a bit tongue in cheek. I do research on algorithmic culture, and so I’m fascinated by how algorithmic decisions are made. I want to understand how algorithms try to select what is the most important point of an academic paper.

This is not to say that we should all write our papers for machines and ignore human readers, or that we should optimise our research for google rather than aiming to primarily do robust and useful and interesting research. Write the paper you want to write. But if you’re interested in how algorithms and search engines are reading your papers, read on.

Many platforms display the first image in your article when someone shares it, so choose wisely. This is a stock image of clockwork because the screenshots in the rest of the blog post don’t look great when this post is shared on Twitter. Also, metaphors. (Source: Colourbox)
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26. March 2021 by Jill
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Energy marking for privacy?

I’m attending a two-day digital meeting of Personvernskommisjonen, the privacy commission tasked with surveying the state of privacy in Norway and recommending policy for the future. We just had professor of ICT and private law, Frederik Zuiderveen Borgesius, visit for a short talk, and he raised some really pertinent points. The discussion was much richer than I’m able to summarise here, but I wanted to capture a few points.

Borgesius argued that there is a fundamental information asymmetry in privacy online today, where consumers don’t know what data is collected about them or how it will be used or what the consequences could be. In economics, information symmetry tends to be bad not just for consumers but for everyone, as described by Nobel-prize winning economist George Akerlof in his analysis of “The Market for Lemons“. The lemons are used cars, and of course typical consumers can’t really tell whether the car is a “lemon” or not.

How do you reduce information asymmetries? Well, in supermarkets, we have systems that make sure all the food is safe to eat. There are also various labelling schemes, showing if food is healthy, organic and so on – but some of these are not very helpful, or are even designed to confuse us. Different countries have different laws. So for instance in Norway, the country that fruit and vegetables comes from has to be marked. But all countries have systems so you can trust that food in the supermarket isn’t poisonous.

So we could imagine legally banning certain kinds of privacy-destroying websites. No collecting personal data for advertising on potentially sensitive sites, for instance, like sites about medical information. But where to draw the line? The articles I choose to read in a newspaper can also be used to infer very sensitive information about my sexuality, mental health or political standpoint, for instance.

Perhaps energy efficiency labelling of fridges and other electrical appliances is a better example? Here the EU has established clear guidelines, and they’re clearly displayed when you buy a new fridge.

Could we imagine something similar for websites? And if so, how would it be implemented?

Arguably, this is what Apple is doing with its privacy labels for apps. I love being able to see this more clearly and in a structured way, and this is a reason I like using Apple products (although I realise that’s a privilege: they’re expensive, which is an issue if only the rich get privacy). But is it a problem that a commercial company is doing this for us, rather than it being democratically defined?

Not all labelling works. You need a lot of basic things in place. (The Norwegian Consumer Council has an overview of labelling in Norway that explains some of the issues.) You also need genuine competition so users have a real choice based on the labelling. That means you need data portability, so users can easily switch to a different social media platform for instance.

Another online pandemic meeting. Speaker’s face blanked out, it’s the privacy commission after all.

We’re not concluding anything yet, and I’m sure there’ll be lots more discussions in the year to come.

09. March 2021 by Jill
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Exhibition setup!

OK, this is extremely exciting: the University Museum is making an exhibition about research in our Machine Vision in Everyday Life project! They’ve been working on it for months, and COVID has made everything look very iffy, but now it really looks as though it is nearly ready, and we hope to be able to open it on March 18. Fingers crossed there isn’t a new lockdown before then…

Marit Amundsen is one of the curators on the project.

You’ll enter the exhibition through this tunnel, which will be lit up and “scan” you – then a mechanic guide (a talking head video) will greet you and instruct you to draw a card that gives you a specific role. You’ll view the exhibition from the point of view of your role – a wonderful touch that our larp development team came up with. (The actual larp is planned for November.)

Kurdin Jacob and Magnus Knustad worked with the curators to map surveillance cameras and other tracking around Bergen.

The exhibition is beautifully thought out. Our whole project team contributed ideas, with Andreas Zingerle, Linda Kronman and Gabriele de Seta leading, and the curator team came up with excellent ideas for how to actually make the exhibition exciting to visit. The focus is on the research, but as we research art and games and narratives there are also a lot of artworks in the exhibition – I am thrilled we were able to include this, because there is a lot of evocative art that directly engages with or critiques machine vision technologies.

Eli Hausken and Marit Amundsen looking at James Bridle’s nearly-fully installed artwork, Cloud Index.
Mattias and Eli working on getting the artwork set up – this is Forensic Architecture’s The Battle of Ilovaisk.

There are a lot of details involved with organising an exhibition. Thankfully the Museum are extremely professional, and Andreas was immensely helpful in coordinating with artists and so on (if any of the artists are reading this, your contracts are on the way, we had a few bureaucratic hurdles that are figured out now).

I also loved learning how the museum curators work when designing an exhibition like this. This is the floor plan they developed after many conversations with the research team.

The floor plan for the exhibition.

Sadly the University Museum isn’t able to accept school classes now due to COVID, which is a real shame, as this is an exhibition that would have been so well suited to school visits – and that is usually a major part of what the Museum does. If we’re lucky we’ll be able to have visiting school classes later this spring.

Woman smiling and holding a poster.
This will be on one of the walls. They let me hold it up for the photo though!

I’ll share more as the exhibition is closer to being ready to show!

26. February 2021 by Jill
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Can I blog my way out of my pandemic slump?

I’m going to try to start blogging again, as a way to make myself more accountable to myself. I used to use my blog as a research journal, writing little bits and pieces and saving links and stray thoughts, and often I would use bits of blog posts when writing papers and books. People don’t really use blogs like that any more. Most blog posts are more like polished essays than thinking-while-you-write, or Thinking With My Fingers as Torill titled her blog years ago. Some people used their blogs to document their research process, but I used mine to do my research. And to think about how to organise my days and my time, or how to deal with new responsibilities and tasks. Now I’m nearly 50 and nobody accuses me of looking like a student anymore as they did in 2005 (ha) and that outsider peeking into the ivory tower stance certainly doesn’t work anymore. I’ll have to find a new voice, perhaps, if I start blogging again.

I started today by writing my notes about a novel I just read as a blog post instead of just for myself.

One step at a time.

Maybe trying to write a short blog post every day is a way to get myself back into research, get myself back, after this pandemic slump of a year.

25. February 2021 by Jill
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Robot mothers: The Mother Code

cover of the novel The Mother Code by Carole Stivers

I’m fascinated by fleshy, emotional ideas about AI and robots. A lot of recent science fiction I’ve been reading explores this: what would a sentient, emotional AI be like? How would they experience the world? What would their material form mean? Would they love? So much of being human is about our bodily emotions and gut feelings and our physical responses to our experiences.

I just finished reading The Mother Code by Carole Stiver. I found the book quite annoying in many ways, but towards the end there are some really interesting descriptions of the relationship between “the Mothers” and the children they have incubated, birthed and brought up. The Mothers are repurposed military bots, designed to nurture human babies after an out-of-control bioweapon kills all humans.

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25. February 2021 by Jill
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Forskning på nedkjølingseffekten og personvern (Lit. review of research on the chilling effect and privacy)

I’m a member of Personvernskommisjonen, a committee appointed by the Norwegian government to write a report that assesses the state of current privacy regulations and practices and gives recommendations on policies to meet current challenges to privacy (here is our mandate). I was asked to have a look at research on the “chilling effect” and privacy, and to be honest, I got a bit carried away, because I really love exploring new research areas and seeing all the new connections, and constructing new searches and seeing how everything interconnects. This is the informal summary I wrote for the commission, with an annotated bibliography at the end. It’s in Norwegian, but if you don’t read Norwegian, Google will translate it reasonably well, and the bibliography mostly references English language research so you can scroll down to that as well.

Please let me know if you have suggestions or more to add! We have almost a year more to finish the report so will definitely be looking for more material.

Nedkjølingseffekten (“the chilling effect”) oppstår “i situasjoner hvor utøvelse av legitime handlinger innskrenkes eller motvirkes gjennom trusselen om mulige sanksjoner” (NOU 2016:19: Samhandling for sikkerhet).

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18. January 2021 by Jill
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VR Narratives: A Workshop in VR, about VR

Now that I have a VR headset at home I’m both enjoying VR experiences and I’m exploring social interaction in VR spaces. I’ll write more about the pros and cons of VR meetings vs Zoom later, but right now I want to share this recording of a conference panel we organised in VR about VR narratives, for ELO2020 last week.

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26. July 2020 by Jill
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Can an algorithm feel emotion?

I gave a talk at the Moral Machines symposium in Helsinki last year, and just heard that a revised version of the talk will be published in an anthology tentatively titled The Ethos of Digital Environments: Technology, Literary Theory and Philosophy. The anthology is edited by Hanna-Riikka Roine and Susanna Lindberg and will be published by Routledge, presumably in 2021 or 2022. Here is an excerpt from my draft of the chapter, where I explore the idea that there might not be that much difference between a neural network that can predict when a human would cry and that involuntary tightness we humans sometimes feel in our chests when we watch a sad movie.

Emotions are often conceived as the determining difference between humans and machines, and indeed, between groups of humans and whatever or whoever they wish to define as non-human. “They don’t have the same feelings we do,” the narrator imagines the wives thinking of the handmaids in Margaret Atwood’s novel (1986, 215); “they don’t seem to feel anything, no pleasure, no pain”, the Terrans remark of the indigenous people they rape and beat in Ursula le Guin’s The Word for World is Forest (1972, 18).

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25. June 2020 by Jill
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Situated Data Analysis

My latest paper, “Situated data analysis: a new method for analysing encoded power relationships in social media“, started out as an analysis of the data visualisations in Strava, but ended up as something more ambitious: a method that I think can be used to analyse all kinds of digital platform using personal data in different contexts. Here is a video teaser explaining the key points of situated data analysis:

This paper has been a long time in the works, and started off as part of the INDVIL project on data visualisations, where I was tasked with thinking about the epistemology of data visualisations. Working through revision after revision of my analyses of data visualisations in Strava I found that what really interested me about Strava was the many different ways that the personal data it collects from runners and cyclists are presented—or, more precisely, how the data are situated. Once I’d analysed the different ways the Strava data was situated, I realised that the same method could be applied to any social media platform, app or digital infrastructure that uses personal data. So I decided to change the focus of the paper so it was about the method, not just about Strava.

Donna Haraway coined the term situated knowledges in 1988 to demonstrate that knowledge can never be objective, that it is impossible to see the world (or anything) from a neutral, external standpoint. Haraway calls this fiction of objectivity “the god trick,” a fantasy that omniscience is possible.

With Facebook and Google Earth and smart homes and smartphones vastly more data is collected about us and our behaviour than when Haraway wrote about situated knowledge. The god trick as it occurs when big data are involved has been given many names by researchers of digital media: Anthony McCosker and Rowan Wilken write about the fantasy of “total knowledge” in data visualisations, José van Dijck warns against an uncritical, almost religious “dataism“: a belief that human behaviour can be quantified, and Lisa Gitelman points out that “Raw Data” is an Oxymoron in her anthology on the digital humanities. There is also an extensive body of work on algorithmic bias analysing how machine learning using immense datasets is not objective but reinforces biases in the data sets and inherent in the code itself (there are heaps of references to this in the paper itself if you’re curious!).

Situated data analysis provides us with a method for analysing how data is always situated, always partial and biased. In my paper I use Strava as an example, but let’s look at a different kind of data: how about selfies?

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22. June 2020 by Jill
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