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.
Are robots competing for your job?
Probably, but don’t count yourself out.
The New Yorker magazine has a great essay by Jill Lepore about whether Are Robots Competing for Your Job? (Feb. 25, 2019) The essay talks about the various predictions, including the prediction that R.I. (Remote Intelligence or global workers) will take your job too. The fear of robots is the other side of the coin of the fear of immigrants which raises questions about why we are panicking over jobs when unemployment is so low.
Misery likes a scapegoat: heads, blame machines; tails, foreigners. But is the present alarm warranted? Panic is not evidence of danger; it’s evidence of panic. Stoking fear of invading robots and of invading immigrants has been going on for a long time, and the predictions of disaster have, generally, been bananas. Oh, but this time it’s different, the robotomizers insist.
Lepore points out how many job categories have been lost only to be replaced by others which is why economists are apparently dismissive of the anxiety.
Some questions we should be asking include:
Who benefits from all these warnings about job loss?
How do these warnings function rhetorically? What else might they be saying? How are they interpretations of the past by futurists?
How is the panic about job losses tied to worries about immigration?
Today I learned about Pius Adesanmi who died in the recent Ethiopian Airlines crash. From all accounts he was an inspiring professor of English and African Studies at Carelton. You can hear him from a TEDxEuston talk embedded above. Or you can read from his collection of satirical essays titled Naija No Dey Carry Last: Thoughts on a Nation in Progress.
In the TEDx talk he makes a prescient point about new technologies,
We are undertakers. Man will always preside over the funeral of any piece of technology that pretends to replace him.
He connects this prediction about how all new technologies, including AI, will also pass on with a reflection on Africa as a place from which to understand technology.
And that is what Africa understands so well. Should Africa face forward? No. She understands that there will be man to preside over the funeral of these new innovations. She doesn’t need to face forward if she understand human agency. Africa is the forward that the rest of humanities must face.
We need this vision of/from Africa. It gets ahead of the ever returning hype cycle of new technologies. It imagines a position from which we escape the neverending discourse of disruptive innovation which limits our options before AI.
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?
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.
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.
Jingwei, a bright digital humanities student working as a research assistant, has been playing with generative AI approaches from aiweirdness.com – Letting neural networks be weird. Janelle Shane has made neural networks funny by using the to generate things like New My Little Ponies. Jingwei scraped titles of digital humanities conferences from various conference sites and trained and generated new titles just waiting to be proposed as papers:
The Catalogue of the Cultural Heritage Parts
Automatic European Pathworks and Indexte Corpus and Mullisian Descriptions
Minimal Intellectual tools and Actorical Normiels: The Case study of the Digital Humanities Classics
Automatic European Periodical Mexico: The Case of the Digital Hour
TEIviv Industics – Representation dans le perfect textbook
Conceptions of the Digital Homer Centre
Preserving Critical Computational App thinking in DH Languages
DH Potential Works: US Work Film Translation Science
Translation Text Mining and GiS 2.0
DH Facilitating the RIATI of the Digital Scholar
Shape Comparing Data Creating and Scholarly Edition
DH Federation of the Digital Humanities: The Network in the Halleni building and Web Study of Digital Humanities in the Hid-Cloudy
The First Web Study of Build: A “Digitie-Game as the Moreliency of the Digital Humanities: The Case study of the Digital Hour: The Scale Text Story Minimalism: the Case of Public Australian Recognition Translation and Puradopase
The Computational Text of Contemporary Corpora
The Social Network of Linguosation in Data Washingtone
Designing formation of Data visualization
The Computational Text of Context: The Case of the World War and Athngr across Theory
The Film Translation Text Center: The Context of the Cultural Hermental Peripherents
The Social InfrastructurePPA: Artificial Data In a Digital Harl to Mexquise (1950-1936)
EMO Artificial Contributions of the Hauth Past Works of Warla Management Infriction
DAARRhK Platform for Data
Automatic Digital Harlocator and Scholar
Complex Networks of Computational Corpus
IMPArative Mining Trail with DH Portal
Pursour Auchese of the Social Flowchart of European Nation