After a month or so of being subscribed to Apple News+, today I dropped the subscription. It was one more subscription, and they add up. More importantly, it wasn’t giving me the news. When Notre Dame was burning Apple News+ was feeding me inane lifestyle stories. It didn’t seem to be able to gather and present current news, just glossy magazine articles. Perhaps it wasn’t meant to compete with Google News. Perhaps I wasn’t meant to pay for it.
The conference was opened by Reuben Quinn whose grandfather signed Treaty 6. He challenged us to think about what labels and labelling mean. Later Kim Tallbear challenged us to think about how we want the encounter with other intelligences to go. We don’t have a good track record of encountering the other and respecting intelligence. Now is the time to think about our positionality and to develop protocols for encounters. We should also be open to different forms of intelligence, not just ours.
Data shows 7,456 debts were reduced to zero and another 12,524 partially reduced between July last year and March
The Guardian has a number of stories on the Australian Centrelink scandal including, Centrelink scandal: tens of thousands of welfare debts wiped or reduced. The scandal arose when the government introduce changes to a system for calculating overpayment to welfare recipients and clawing it back that removed a lot of the human oversight. The result was lots of miscalculated debts being automatically assigned to some of the most vulnerable. A report, Paying the Price of Welfare Reform, concluded that,
The research concludes that although welfare reform may be leading to cost savings for the Department of Human Services (DHS), substantial costs are being shifted to vulnerable customers and the community services that support them. It is they that are paying the price of welfare reform.
The law has not caught up. In the United States, the use of facial recognition is almost wholly unregulated.
The New York Times has an opinion piece by Sahil Chinoy on how (they) We Built a (Legal) Facial Recognition Machine for $60. They describe an inexpensive experiment they ran where they took footage of people walking past some cameras installed in Bryant Park and compared them to known people who work in the area (scraped from web sites of organizations that have offices in the neighborhood.) Everything they did used public resources that others could use. The cameras stream their footage here. Anyone can scrape the images. The image database they gathered came from public web sites. The software is a service (Amazon’s Rekognition?) The article asks us to imagine the resources available to law enforcement.
I’m intrigued by how this experiment by the New York Times. It is a form of design thinking where they have designed something to help us understand the implications of a technology rather than just writing about what others say. Or we could say it is a form of journalistic experimentation.
Why does facial recognition spook us? Is recognizing people something we feel is deeply human? Or is it the potential for recognition in all sorts of situations. Do we need to start guarding our faces?
Facial recognition is categorically different from other forms of surveillance, Mr. Hartzog said, and uniquely dangerous. Faces are hard to hide and can be observed from far away, unlike a fingerprint. Name and face databases of law-abiding citizens, like driver’s license records, already exist. And for the most part, facial recognition surveillance can be set up using cameras already on the streets.
This is one of a number of excellent articles by the New York Times that is part of their Privacy Project.
Mt Fuji with the setting behind
Sitting on a hill with a view of Mt. Fuji across the water is the Shonan Village Center where I just finished a research retreat on Modelling Cultural Processes. This was organized by Mits Inaba, Martin Roth, and Gehard Heyer from Ritsumeikan University and the University of Leipzig. It brought together people in computing, linguistics, game studies, political science, literary studies and the digital humanities. My conference notes are here.
Unlike a conference, much of the time was spent in working groups discussing issues like identity, shifting content, and constructions of culture. As part of our working groups we developed a useful model of the research process across the humanities and social sciences such that we can understand where there are shifts in content.
Mt Fuji in the distance across the water
Greene, Hoffmann, and Stark have written a much needed conference paper on Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning (PDF) for the Hawaii International Conference on System Sciences in Maui, HI. They look at a number of the important ethics statements/declarations out there and try to understand their “moral background.” Here is the abstract:
This paper uses frame analysis to examine recent high-profile values statements endorsing ethical design for artificial intelligence and machine learning (AI/ML). Guided by insights from values in design and the sociology of business ethics, we uncover the grounding assumptions and terms of debate that make some conversations about ethical design possible while forestalling alternative visions. Vision statements for ethical AI/ML co-opt the language of some critics, folding them into a limited, technologically deterministic, expert-driven view of what ethical AI/ML means and how it might work.
I get the feeling that various outfits (of experts) are trying to define what ethics in AI/ML is rather then engaging in a dialogue. There is a rush to be the expert on ethics. Perhaps we should imagine a different way of developing an ethical consensus.
For that matter, is there room for critical positions? What it would mean to call for a stop all research into AI/ML as unethical until proven otherwise? Is that even thinkable? Can we imagine another way that the discourse of ethics might play out?
This article is a great start.
What happens to old digital humanities projects? Most vanish without a trace. Some get archived like the work of John Burrows and others at the Centre For Literary And Linguistic Computing (CLLC). Dr. Alexis Antonia kept an archive of CLLC materials which is now available from the Centre For 21st Century Humanities.
In this codebook we will investigate the macro-structure of philosophical literature. As a base for our investigation I have collected about fifty-thousand reco
Stéfan sent me a link to this interesting post, The structure of recent philosophy (II) · Visualizations. Maximilian Noichl has done a fascinating job using the Web of Science to develop a model of the field of Philosophy since the 1950s. In this post he describes his method and the resulting visualization of clusters (see above). In a later post (version III of the project) he gets a more nuanced visualization that seems more true to the breadth of what people do in philosophy. The version above is heavily weighted to anglo-american analytic philosophy while version III has more history of philosophy and continental philosophy.
Here is the final poster (PDF) for version III.
I can’t help wondering if his snowball approach doesn’t bias the results. What if one used full text of major journals?
I just came across a neat site called AI Weirdness. The site describes all sorts of “weird” experiments in learning neural networks. Some examples:
- Cat names like Colzyy, Mumhan and Tygrar
- Paint colours like Boo Snow and Sudden Pine
- Course titles like Genies and Engineering and Language of Circus Processing
The site has a nice FAQ that describes her tools and how to learn how to do it.
The death of a woman hit by a self-driving car highlights an unfolding technological crisis, as code piled on code creates ‘a universe no one fully understands’
The Guardian has a good essay by Andrew Smith about Franken-algorithms: the deadly consequences of unpredictable code. The essay starts with the obvious problems of biased algorithms like those documented by Cathy O’Neil in Weapons of Math Destruction. It then goes further to talk about cases where algorithms are learning on the fly or are so complex that their behaviour becomes unpredictable. An example is high-frequency trading algorithms that trade on the stock market. These algorithmic traders try to outwit each other and learn which leads to unpredictable “flash crashes” when they go rogue.
The problem, he (George Dyson) tells me, is that we’re building systems that are beyond our intellectual means to control. We believe that if a system is deterministic (acting according to fixed rules, this being the definition of an algorithm) it is predictable – and that what is predictable can be controlled. Both assumptions turn out to be wrong.
The good news is that, according to one of the experts consulted this could lead to “a golden age for philosophy” as we try to sort out the ethics of these autonomous systems.