Artificial Intelligence – Page 14

Making AI accountable easier said than done, says U of A expert

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are about to disappear under the rising tide of technology, twenty-first-century universities should liberate them from outdated career models and give them ownership of their own futures.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.

Letting neural networks be weird

Halloween Costume Names Generated by a Weird AI

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 Infrastructure PPA: 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
The Stefanopology: The Digital Humanities

Anatomy of an AI System

Anatomy of an AI System – The Amazon Echo as an anatomical map of human labor, data and planetary resources. By Kate Crawford and Vladan Joler (2018)

Kate Crawford and Vladan Joler have created a powerful infographic and web site, Anatomy of an AI System. The dark illustration and site are an essay that starts with the Amazon Echo and then sketches out the global anatomy of this apparently simple AI appliance. They do this by looking at where the materials come from, where the labour comes from (and goes), and the underlying infrastructure.

Put simply: each small moment of convenience – be it answering a question, turning on a light, or playing a song – requires a vast planetary network, fueled by the extraction of non-renewable materials, labor, and data.

The essay/visualization is a powerful example of how we can learn by critically examining the technologies around us.

Just as the Greek chimera was a mythological animal that was part lion, goat, snake and monster, the Echo user is simultaneously a consumer, a resource, a worker, and a product.

Big Tech’s Half-Hearted Response To Fake News And Election Hacking

Despite big hand waves, Facebook, Google, and Twitter aren’t doing enough to stop misinformation.

From slashdot I found a story about : Big Tech’s Half-Hearted Response To Fake News And Election Hacking. This Fast Company story talks about ways that social media companies are trying to prevent the misuse of their platforms as we head into the US midterms.

For Facebook, Google, and Twitter the fight against fake news seems to be two-pronged: De-incentivize the targeted content and provide avenues to correct factual inaccuracies. These are both surface fixes, however, akin to putting caulk on the Grand Canyon.

And, despite grand hand waves, both approaches are reactive. They don’t aim at understanding how this problem became prevalent, or creating a method that attacks the systemic issue. Instead these advertising giants implement new mechanisms by which people can report one-off issues—and by which the platforms will be left playing cat-and-mouse games against fake news—all the while giving no real clear glimpse into their opaque ad platforms.

The problem is that these companies make too much money from ads and elections are a chance to get lots of ads, manipulative or not. For that matter, what political ad doesn’t try to manipulate viewers?

The slashdot story was actually about Mozilla’s Responsible Computer Science Challenge which will support initiatives to embedd ethics in computer science courses. Alas, the efficacy of ethics courses is questionable. Aristotle would say that if you don’t have the disposition to be ethical no amount of training would do any good. It just helps the unethical pretend to be ethical.

AI Weirdness

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.

Franken-algorithms: the deadly consequences of unpredictable code

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.

Writing with the machine

“…it’s like writing with a deranged but very well-read parrot on your shoulder.”

Robin Sloan of Mr. Penumbra’s 24-hour Bookstore fame, has been talking about Writing with the machine. He was inspired by presentations like Adrej Karpathy’s blog post on The Unreasonable Effectiveness of Recurrent Neural Networks and Bowman et al’s Generating Sentences from a Continuous Space to try developing a neural net that could generate text. He used as a training corpus a collection of early science-fiction from the Internet Archive and created different text generation tools like the short video of that which you can see above and hear explained in this Eyeo video.

One of the points he emphasizes is that he didn’t do this just for the fun of seeing strange phrases generated, but wants to use it seriously as a writing aide.

I can’t help wondering if this could be used philosophically. Could we generate philosophical or ethical phrases in response to questions?

Re-Imagining Education In An Automating World conference at George Brown

On May 25th I had a chance to attend a gem of a conference organized the Philosophy of Education (POE) committee at George Brown. They organized a conference with different modalities from conversations to formal talks to group work. The topic was Re-Imagining Education in An Automating World (see my conference notes here) and this conference is a seed for a larger one next year.

I gave a talk on Digital Citizenship at the end of the day where I tried to convince people that:

Data analytics are now a matter of citizenship (we all need to understand how we are being manipulated).
We therefore need to teach data literacy in the arts and humanities, so that
Students are prepared to contribute to and critique the ways analytics are used deployed.
This can be done by integrating data and analytical components in any course using field-appropriate data.

Duplex shows Google failing at ethical and creative AI design

Google CEO Sundar Pichai milked the woos from a clappy, home-turf developer crowd at its I/O conference in Mountain View this week with a demo of an in-the-works voice assistant feature that will e…

A number of venues, including TechCruch have discussed the recent Google demonstration of an intelligent agent Duplex who can make appointments. Many of the stories note how Duplex shows Google failing at ethical and creative AI design. The problem is that the agent didn’t (at least during the demo) identify as a robot. Instead it appeared to deceive the person it was talking to. As the TechCrunch article points out, there is really no good reason to deceive if the purpose is to make an appointment.

What I want to know is what are the ethics of dealing with a robot? Do we need to identify as human to the robot? Do we need to be polite and give them the courtesy that we would a fellow human? Would it be OK for me to hang up as I do on recorded telemarketing calls? Most of us have developed habits of courtesy when dealing with people, including strangers, that the telemarketers take advantage of in their scripts. Will the robots now take advantage of that? Or, to be more precise, will those that use the robots to save their time take advantage of us?

A second question is how Google considers the ethical implications of their research? It is easy to castigate them for this demonstration, but the demonstration tells us nothing about a line of research that has been going on for a while and what processes Google may have in place to check the ethics of what they do. As companies explore the possibilities for AI, how are they to check their ethics in the excitement of achievement?

I should note that Google’s parent Alphabet has apparently dropped the “Don’t be evil” motto from their code of conduct. There has also been news about how a number of employees quit over a Google program to apply machine learning to drone footage for the military. This is after over 3000 Google employees signed a letter taking issue with the project. See also the Open Letter in Support of Google Employees and Tech Workers that researchers signed. As they say:

We are also deeply concerned about the possible integration of Google’s data on people’s everyday lives with military surveillance data, and its combined application to targeted killing. Google has moved into military work without subjecting itself to public debate or deliberation, either domestically or internationally. While Google regularly decides the future of technology without democratic public engagement, its entry into military technologies casts the problems of private control of information infrastructure into high relief.