Value Sensitive Design and Dark Patterns

Dark Patterns are tricks used in websites and apps that make you buy or sign up for things that you didn’t mean to. The purpose of this site is to spread awareness and to shame companies that use them.

Reading about Value Sensitive Design I came across a link to Harry Brignul’s Dark Patterns. The site is about ways that web designers try to manipulate users. They have a Hall of Shame that is instructive and a Reading List if you want to follow up. It is interesting to see attempts to regulate certain patterns of deception.

Values are expressed and embedded in technology; they have real and often non-obvious impacts on users and society.

The alternative is introduce values and ethics into the design process. This is where Value Sensitive Design comes. As developed by Batya Friedman and colleagues it is an approach that includes methods for thinking-through the ethics of a project from the beginning. Some of the approaches mentioned in the article include:

  • Mapping out what a design will support, hinder or prevent.
  • Consider the stakeholders, especially those that may not have any say in the deployment or use of a technology.
  • Try to understand the underlying assumptions of technologies.
  • Broaden our gaze as to the effects of a technology on human experience.

They have even produced a set of Envisioning Cards for sale.

In Isolating Times, Can Robo-Pets Provide Comfort? – The New York Times

As seniors find themselves cut off from loved ones during the pandemic, some are turning to automated animals for company.

I’m reading about Virtual Assistants and thinking that in some ways the simplest VAs are the robopets that are being given to lonely elderly people who are isolated. See In Isolating Times, Can Robo-Pets Provide Comfort? Robo-cats and dogs (and even seals) seem to provide comfort the way a stuffed pet might. They aren’t even that smart, but can give comfort to an older person suffering from isolation.

These pets, like PARO (an expensive Japanese robotic seal seen above) or the much cheaper Joy for All pets, can possibly fool people with dementia. What are the ethics of this? Are we comfortable fooling people for their own good?

The Future of Digital Assistants Is Queer

AI assistants continue to reinforce sexist stereotypes, but queering these devices could help reimagine their relationship to gender altogether.

Wired has a nice article on how the The Future of Digital Assistants Is Queer. The article looks at the gendering of virtual assistants like Siri and how it is not enough to just offer male voices, but we need to queer the voices. It mentions the ethical issue of how voice conveys information like whether the VA is a bot or not.

The Proliferation of AI Ethics Principles: What’s Next?

The Proliferation of AI Ethics Principles: What’s Next?

The Montreal AI Ethics Institute has republished a nice article by Ravit Dotan, The Proliferation of AI Ethics Principles: What’s Next? Dotan starts by looking at some of the meta studies and then goes on to argue that we are unlikely to ever come up with a “unique set of core AI principles”, nor should we want to. She points out the lack of diversity in the sets we have. Different types of institutions will need different types of principles. She ends with these questions:

How do we navigate the proliferation of AI ethics principles? What should we use for regulation, for example? Should we seek to create new AI ethics principles which incorporate more perspectives? What if it doesn’t result in a unique set of principles, only increasing the multiplicity of principles? Is it possible to develop approaches for AI ethics governance that don’t rely on general AI ethics principles?

I am personally convinced that a more fruitful way forward is to start trading stories. These stories could take the form of incidents or cases or news or science fiction or even AI generated stories. We need to develop our ethical imagination. Hero Laird made this point in a talk on AI, Ethics and Law that was part of a salon we organize at AI4Society. They quoted from Thomas King’s The Truth About Stories to the effect that,

The truth about stories is that that’s all we are.

What stories do artificial intelligences tell themselves?

Artificial Intelligence Incident Database

I discovered the Artificial Intelligence Incident Database developed by the Partnership on AI. The Database contains reports on things that have gone wrong with AIs like the Australian Centerlink robodebt debacle.

The Incident Database was developed to help educate developers and encourage learning from mistakes. They have posted a paper to arXiv on Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database.

Ask Delphi

Delphi Screen Shot

Ask Delphi is an intriguing AI that you can use to ponder ethical questions. You type in a situation and it will tell you if it is morally acceptable or not. It is apparently built not on Reddit data, but on crowdsourced data, so it shouldn’t be as easy to provoke into giving toxic answers.

In their paper, Delphi: Towards Machine Ethics and Norms they say that they have created a Commonsense Norm Bank, “a collection of 1.7M ethical judgments on diverse real-life situations.” This contributes to Delphi’s sound pronouncements, but it doesn’t seem available for others yet.

AI Weirdness has a nice story on how she fooled Delphi.

Emojify: Scientists create online games to show risks of AI emotion recognition

Public can try pulling faces to trick the technology, while critics highlight human rights concerns

From the Guardian story, Scientists create online games to show risks of AI emotion recognition, I discovered Emojify, a web site with some games to show how problematic emotion detection is. Researchers are worried by the booming business of emotion detection with artificial intelligence. For example, it is being used in education in China. See the CNN story about how In Hong Kong, this AI reads children’s emotions as they learn.

A Hong Kong company has developed facial expression-reading AI that monitors students’ emotions as they study. With many children currently learning from home, they say the technology could make the virtual classroom even better than the real thing.

With cameras all over, this should worry us. We are not only be identified by face recognition, but now they want to know our inner emotions too. What sort of theory of emotions licenses these systems?

Why people believe Covid conspiracy theories: could folklore hold the answer?

Using Danish witchcraft folklore as a model, the researchers from UCLA and Berkeley analysed thousands of social media posts with an artificial intelligence tool and extracted the key people, things and relationships.

The Guardian has a nice story on Why people believe Covid conspiracy theories: could folklore hold the answer? This reports on research using folklore theory and artificial intelligence to understand conspiracies.

The story maps how Bill Gates connects the coronavirus with 5G for conspiracy fans. They use folklore theory to understand the way conspiracies work.

Folklore isn’t just a model for the AI. Tangherlini, whose specialism is Danish folklore, is interested in how conspiratorial witchcraft folklore took hold in the 16th and 17th centuries and what lessons it has for today.

Whereas in the past, witches were accused of using herbs to create potions that caused miscarriages, today we see stories that Gates is using coronavirus vaccinations to sterilise people. …

The research also hints at a way of breaking through conspiracy theory logic, offering a glimmer of hope as increasing numbers of people get drawn in.

The story then addresses the question of what difference the research might make. What good would a folklore map of a conspiracy theory do? The challenge of research is the more information clearly doesn’t work in a world of information overload.

The paper the story is based on is Conspiracy in the time of corona: automatic detection of emerging Covid-19 conspiracy theories in social media and the news, by Shadi Shahsavari, Pavan Holur, Tianyi Wang , Timothy R Tangherlini and Vwani Roychowdhury.

Apple will scan iPhones for child pornography

Apple unveiled new software Thursday that scans photos and messages on iPhones for child pornography and explicit messages sent to minors in a major new effort to prevent sexual predators from using Apple’s services.

The Washington Post and other news venues are reporting that Apple will scan iPhones for child pornography. As the subtitle to the article puts it “Apple is prying into iPhones to find sexual predators, but privacy activists worry governments could weaponize the feature.” Child porn is the go-to case when organizations want to defend surveillance.

The software will scan without our knowledge or consent which raises privacy issues. What are the chances of false positives? What if the tool is adapted to catch other types of images? Edward Snowden and the EFF have criticized this move. It seems inconsistent with Apple’s firm position on privacy and refusal to even unlock

It strikes me that there is a great case study here.

Pentagon believes its precognitive AI can predict events ‘days in advance’

The US military is testing AI that helps predict events days in advance, helping it make proactive decisions..

Endgadget has a story on how the Pentagon believes its precognitive AI can predict events ‘days in advance’. It is clear that for most the value in AI and surveillance is prediction and yet there are some fundamental contradictions. As Hume pointed out centuries ago, all prediction is based on extrapolation from past behaviour. We simply don’t know the future; the best we can do is select features of past behaviour that seemed to do a good job predicting (retrospectively) and hope they will work in the future. Alas, we get seduced by the effectiveness of retrospective work. As Smith and Cordes put it in The Phantom Pattern Problem:

How, in this modern era of big data and powerful computers, can experts be so foolish? Ironically, big data and powerful computers are part of the problem. We have all been bred to be fooled—to be attracted to shiny patterns and glittery correlations. (p. 11)

What if machine learning and big data were really best suited for suited for studying the past and not predicting the future? Would there be the hype? the investment?

When the next AI winter comes we in the humanities could pick up the pieces and use these techniques to try to explain the past, but I’m getting ahead of myself and predicting another winter.