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.
When it comes to the crucial ethical question of calling one’s mother, most people agreed that not doing so was a moral failing.
Quartz reports on a study in Philosophical Psychology that Ethicists are no more ethical than the rest of us, study finds — Quartz. While one wonders how one can survey how ethical someone is, this is nonetheless a believable result. The contemporary university is structured deliberately not to be a place to change people’s morals, but to educate them. When we teach ethics we don’t assess or grade the morality of the student. Likewise, when we hire, promote, and assess the ethics of a philosophy professor we also don’t assess their personal morality. We assess their research, teaching and service record, all of which can be burnished without actually being ethical. There is, if you will, a professional ethic that research and teaching should not be personal, but be detached.
A focus on the teaching and learning of ethics over personal morality is, despite the appearance of hypocrisy, a good thing. We try to create in the university, in the class, and in publications, an openness to ideas, whoever they come from. By avoiding discussing personal morality we try to create a space where people of different views can enter into dialogue about ethics. Imagine what it would be like if it were otherwise? Imagine if my ethics class was about converting students to some standard of behaviour. Who would decide what that standard was? The ethos of professional ethics is one that emphasizes dialogue over action, history over behaviour, and ethical argumentation over disposition. Would it be ethical any other way?
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.
Article: Applying an Ethics of Care to Internet Research: Gamergate and Digital Humanities
Thanks to Todd Suomela’s lead, we just published an article on Applying an Ethics of Care to Internet Research: Gamergate and Digital Humanities in Digital Studies. This article is a companion to an article I wrote with Bettina Berendt on Information Wants to Be Free, Or Does It? We and others are exploring the Ethics of Care as a different way of thinking about the ethics of digital humanities research.
In 1972, a photo of a Swedish Playboy model was used to engineer the digital image format that would become the JPEG. The model herself was mostly a mystery—until now.
Wired has another story on Finding Lena Forsen, the Patron Saint of JPEGs. This is not, however, the first time her story has been told. I blogged about the use of the Lena image back in 2004. It seems like this story will be rediscovered every decade.
What has changed is that people are calling out the casual sexism of tech culture. An example is Chang’s book Brotopia that starts with the Lena story.
The New York Times has a nice short video on cybersecurity which is increasingly an issue. One of the things they mention is how it was the USA and Israel that may have opened the Pandora’s box of cyberweapons when they used Stuxnet to damage Iran’s nuclear programme. By using a sophisticated worm first we both legitimized the use of cyberwar against other countries which one is not at war with, and we showed what could be done. This, at least, is the argument of a good book on Stuxnet, Countdown to Zero Day.
Now the problem is that the USA, while having good offensive capability, is also one of the most vulnerable countries because of the heavy use of information technology in all walks of life. How can we defend against the weapons we have let loose?
What is particularly worrisome is that cyberweapons are being designed so that they are hard to trace and subtly disruptive in ways that are short of all out war. We are seeing a new form of hot/cold war where countries harass each other electronically without actually declaring war and getting civilian input. After 2016 all democratic countries need to protect against electoral disruption which then puts democracies at a disadvantage over closed societies.
Geoff McMaster of the Folio (U of A’s news site) wrote a nice article about how Making AI accountable easier said than done, says U of A expert. The article quotes me on on accountability and artificial intelligence. What we didn’t really talk about is forms of accountability for automata including:
- Explainability – Can someone get an explanation as to how and why an AI made a decision that affects them? If people can get an explanation that they can understand then they can presumably take remedial action and hold someone or some organization accountable.
- Transparency – Is an automated decision making process fully transparent so that it can be tested, studied and critiqued? Transparency is often seen as a higher bar for an AI to meet than explainability.
- Responsibility – This is the old computer ethics question that focuses on who can be held responsible if a computer or AI harms someone. Who or what is held to account?
In all these cases there is a presumption of process both to determine transparency/responsibility and to then punish or correct for problems. Otherwise people will have no real recourse.
The Folio has a story on the ethics of AI that quotes me with the title, Making AI accountable easier said than done, says U of A expert.
One of issues that interests me the most now is the history of this discussion. We tend to treat the ethics of AI as a new issue, but people have been thinking about how automation would affect people for some time. There have been textbooks for teaching Computer Ethics like that of Deborah G. Johnson since the 1980s. As part of research we did on how computer were presented in the news we found articles in the 1960s about how automation might put people out of work. They weren’t thinking of AI then, but the ethical and social effects that concerned people back then were similar. What few people discussed, however, was how automation affected different groups differently. Michele Landsberg wrote a prescient article on “Will Computer Replace the Working Girl?” in 1964 for the women’s section of The Globe and Mail that argued that is was women in the typing pools that were being put out of work. Likewise I suspect that some groups be more affected by AI than others and that we need to prepare for that.
Addressing the issue of how universities might prepare for the disruption of artificial intelligence is a good book, Robot-Proof: Higher Education in the Age of Artificial Intelligence by Joseph Aoun (MIT Press, 2017).
Instead of educating college students for jobs that are about to disappear under the rising tide of technology, twenty-first-century universities should liberate them from outdated career models and give them ownership of their own futures. They should equip them with the literacies and skills they need to thrive in this new economy defined by technology, as well as continue providing them with access to the learning they need to face the challenges of life in a diverse, global environment.