Documenting the Now develops tools and builds community practices that support the ethical collection, use, and preservation of social media content.
I’ve been talking with the folks at MassMine (I’m on their Advisory Board) about tools that can gather information off the web and I was pointed to the Documenting the Now project that is based at the University of Maryland and the University of Virginia with support from Mellon. DocNow have developed tools and services around documenting the “now” using social media. DocNow itself is an “appraisal” tool for twitter archiving. They then have a great catalog of twitter archives they and others have gathered which looks like it would be great for teaching.
MassMine is at present a command-line tool that can gather different types of social media. They are building a web interface version that will make it easier to use and they are planning to connect it to Voyant so you can analyze results in Voyant. I’m looking forward to something easier to use than Python libraries.
Speaking of which, I found a TAGS (Twitter Archiving Google Sheet) which is a plug-in for Google Sheets that can scrape smaller amounts of Twitter. Another accessible tool is Octoparse that is designed to scrape different database driven web sites. It is commercial, but has a 14 day trial.
One of the impressive features of Documenting the Now project is that they are thinking about the ethics of scraping. They have a Social Labels set for people to indicate how data should be handled.
Australian students who have raised privacy concerns describe the incident involving a Canadian student as ‘freakishly disrespectful’
The Guardian has a story about CEO of exam monitoring software Proctorio apologises for posting student’s chat logs on Reddit. Proctorio provides software for monitoring (proctoring) students on their own laptop while they take exams. It uses the video camera and watches the keyboard to presumably watch whether the student tries to cheat on a timed exam. Apparently a UBC student claimed that he couldn’t get help in a timely fashion from Proctorio when he was using it (presumably with a timer going for the exam.) This led to Australian students criticizing the use of Proctorio which led to the CEO arguing that the UBC student had lied and providing a partial transcript to show that the student was answered in a timely fashion. That the CEO would post a partial transcript shows that:
staff at Proctorio do have access to the logs and transcripts of student behaviour, and
that they don’t have the privacy protection protocols in place to prevent the private information from being leaked.
I can’t help feeling that there is a pattern here since we also see senior politicians sometimes leaking data about citizens who criticize them. The privacy protocols may be in place, but they aren’t observed or can’t be enforced against the senior staff (who are the ones that presumably need to do the enforcing.) You also sense that the senior person feels that the critic abrogated their right to privacy by lying or misrepresenting something in their criticism.
This raises the question of whether someone who misuses or lies about a service deserves the ongoing protection of the service. Of course, we want to say that they should, but nations like the UK have stripped citizens like Shamina Begum of citizenship and thus their rights because they behaved traitorously, joining ISIS. Countries have murdered their own citizens that became terrorists without a trial. Clearly we feel that in some cases one can unilaterally remove someones rights, including the right to life, because of their behaviour.
Smart software controls the prices and products you see when you shop online – and sometimes it can go spectacularly wrong, discovers Chris Baraniuk.
The BBC has a stroy about The bad things that happen when algorithms run online shops. The story describes how e-commerce systems designed to set prices dynamically (in comparison with someone else’s price, for example) can go wrong and end up charging customers much more than they will pay or charging them virtually nothing so the store loses money.
The story links to an instructive blog entry by Michael Eisen about how two algorithms pushed up the price on a book into the millions, Amazon’s $23,698,655.93 book about flies. The blog entry is a perfect little story about about the problems you get when you have algorithms responding iteratively to each other without any sanity checks.
Vinay Prabhu, chief scientist at UnifyID, a privacy startup in Silicon Valley, and Abeba Birhane, a PhD candidate at University College Dublin in Ireland, pored over the MIT database and discovered thousands of images labelled with racist slurs for Black and Asian people, and derogatory terms used to describe women. They revealed their findings in a paper undergoing peer review for the 2021 Workshop on Applications of Computer Vision conference.
Another one of those “what were they thinking when they created the dataset stories” from The Register tells about how MIT apologizes, permanently pulls offline huge dataset that taught AI systems to use racist, misogynistic slurs. The MIT Tiny Images dataset was created automatically using scripts that used the WordNet database of terms which itself held derogatory terms. Nobody thought to check either the terms taken from WordNet or the resulting images scoured from the net. As a result there are not only lots of images for which permission was not secured, but also racists, sexist, and otherwise derogatory labels on the images which in turn means that if you train an AI on these it will generate racist/sexist results.
The article also mentions a general problem with academic datasets. Companies like Facebook can afford to hire actors to pose for images and can thus secure permissions to use the images for training. Academic datasets (and some commercial ones like the Clearview AI database) tend to be scraped and therefore will not have the explicit permission of the copyright holders or people shown. In effect, academics are resorting to mass surveillance to generate training sets. One wonders if we could crowdsource a training set by and for people?
Is “excellence” really the most efficient metric for distributing the resources available to the world’s scientists, teachers, and scholars? Does “excellence” live up to the expectations that academic communities place upon it? Is “excellence” excellent? And are we being excellent to each other in using it?
Rhetoric of excellence – it looks at how there is little consensus around what excellence between disciplines. Within disciplines it is negotiated and can become conservative.
Is “excellence” good for research – the second section argues that there is little correlation between forms of excellence review and long term metrics. They go on to outline some of the unfortunate side-effects of the push for excellence; how it can distort research and funding by promoting competition rather than collaboration. They also talk about how excellence disincentivizes replication – who wants to bother with replication if
Alternative narratives – the third section looks at alternative ways of distributing funding. They discuss looking at “soundness” and “capacity” as an alternatives to the winner-takes-all of excellence.
So much more could and should be addressed on this subject. I have often wondered about the effect of the success rates in grant programmes (percentage of applicants funded). When the success rate gets really low, as it is with many NEH programmes, it almost becomes a waste of time to apply and superstitions about success abound. SSHRC has healthier success rates that generally ensure that most researchers gets funded if they persist and rework their proposals.
Hypercompetition in turn leads to greater (we might even say more shameless …) attempts to perform this “excellence”, driving a circular conservatism and reification of existing power structures while harming rather than improving the qualities of the underlying activity.
Ultimately the “adjunctification” of the university, where few faculty get tenure, also leads to hypercompetition and an impoverished research environment. Getting tenure could end up being the most prestigious (and fundamental) of grants – the grant of a research career.
Human data encodes human biases by default. Being aware of this is a good start, and the conversation around how to handle it is ongoing. At Google, we are actively researching unintended bias analysis and mitigation strategies because we are committed to making products that work well for everyone. In this post, we’ll examine a few text embedding models, suggest some tools for evaluating certain forms of bias, and discuss how these issues matter when building applications.
On the Google Developvers Blog there is an interesting post on Text Embedding Models Contain Bias. Here’s Why That Matters. The post talks about a technique for using Word Embedding Association Tests (WEAT) to see compare different text embedding algorithms. The idea is to see whether groups of words like gendered words associate with positive or negative words. In the image above you can see the sentiment bias for female and male names for different techniques.
While Google is working on WEAT to try to detect and deal with bias, in our case this technique could be used to identify forms of bias in corpora.
Analyzing the Twitter Conversation Surrounding COVID-19
From Twitter I found out about this excellent visual essay on The Viral Virus by Kate Appel from May 6, 2020. Appel used Voyant to study highly retweeted tweets from January 20th to April 23rd. She divided the tweets into weeks and then used the distinctive words (tf-idf) tool to tell a story about the changing discussion about Covid-19. As you scroll down you see lists of distinctive words and supporting images. At the end she shows some of the topics gained from topic modelling. It is a remarkably simple, but effective use of Voyant.
Users of these apps should know that they are being traced through them, and
Users should consent to their use.
There are a variety of these apps from the system pioneered by Singapore called TraceTogether to its Alberta cousin ABTraceTogether. There are also a variety of approaches to tracing people from using credit card records to apps like TraceTogether. The EFF has a good essay on Protecting Civil Rights During a Public Health Crisis that I adapt here to provide guidelines for when one might gather data without knowledge or consent:
Medically necessary: There should be a clear and compelling explanation as to how this will save lives.
Personal information proportionate to need: The information gathered should fit the need and go no further.
Information handled by health informatics specialists: The gathering and processing should be handled by health informatics units, not signals intelligence or security services.
Deleted: It should be deleted once it is no longer needed.
Not be organized due to vulnerable demographics: The information should not be binned according to stereotypical or vulnerable demographics unless there is a compelling need. We should be very careful that we don’t use the data to further disadvantage groups.
Use reviewed afterwards: The should be a review after the crisis is over.
Transparency: Government should transparent about what they are gathering and why.
Due process: There should be open processes for people to challenge the gathering of their information or to challenge decisions taken as a result of such information.
The era of peak globalisation is over. For those of us not on the front line, clearing the mind and thinking how to live in an altered world is the task at hand.
John Gray has written an essay in the New Statesman on Why this crisis is a turning point in history. He argues that the era of hyperglobalism is at an end and many systems may not survive the shift to something different. Many may think we will, after a bit of isolated pain, return to the good old expanding wealth, but the economic crisis that is now emerging may break that dream. Governments and nations may be broken by collapsing systems.
Neoliberalism shrinks public budgets; solutionism shrinks public imagination.
Evgeny Morozov has crisp essay in The Guardina on how The tech ‘solutions’ for coronavirus take the surveillance state to the next level. He argues that neoliberalist austerity cut our public services back in ways that now we see are endangering lives, but it is solutionism that constraining our ideas about what we can do to deal with situations. If we look for a technical solution we give up on questioning the underlying defunding of the commons.
Back in The Guardian, he ends his essay warning that we should focus on picking between apps – between solutions. We should get beyond solutions like apps to thinking politically.
The feast of solutionism unleashed by Covid-19 reveals the extreme dependence of the actually existing democracies on the undemocratic exercise of private power by technology platforms. Our first order of business should be to chart a post-solutionist path – one that gives the public sovereignty over digital platforms.