Addressing the Alarming Systems of Surveillance Built By Library Vendors

The Scholarly Publishing and Academic Resources Coalition (SPARC) are drawing attention to how we need to be Addressing the Alarming Systems of Surveillance Built By Library Vendors. This was triggered by a story in The Intercept that LexisNexis (is) to provide (a) giant database of personal information to ICE

The company’s databases offer an oceanic computerized view of a person’s existence; by consolidating records of where you’ve lived, where you’ve worked, what you’ve purchased, your debts, run-ins with the law, family members, driving history, and thousands of other types of breadcrumbs, even people particularly diligent about their privacy can be identified and tracked through this sort of digital mosaic. LexisNexis has gone even further than merely aggregating all this data: The company claims it holds 283 million distinct individual dossiers of 99.99% accuracy tied to “LexIDs,” unique identification codes that make pulling all the material collected about a person that much easier. For an undocumented immigrant in the United States, the hazard of such a database is clear. (The Intercept)

That LexisNexis has been building databases on people isn’t new. Sarah Brayne has a book about predictive policing titled Predict and Surveil where, among other things, she describes how the LAPD use Palantir and how police databases integrated in Palantir are enhanced by commercial databases like those sold by LexisNexis. (There is an essay that is an excerpt of the book here, Enter the Dragnet.)

I suspect environments like Palantir make all sorts of smaller and specialized databases more commercially valuable which is leading what were library database providers to expand their business. Before, a database about repossessions might be of interest to only a specialized community. Now it becomes linked to other information and is another dimension of data. In particular these databases provide information about all the people who aren’t in police databases. They provide the breadcrumbs needed to surveil those not documented elsewhere.

The SPARC call points out that we (academics, university libraries) have been funding these database providers. 

Dollars from library subscriptions, directly or indirectly, now support these systems of surveillance. This should be deeply concerning to the library community and to the millions of faculty and students who use their products each day and further underscores the urgency of privacy protections as library services—and research and education more generally—are now delivered primarily online.

This raises the question of our complicity and whether we could do without some of these companies. At a deeper level it raises questions about the curiosity of the academy. We are dedicated to knowledge as an unalloyed good and are at the heart of a large system of surveillance – surveillance of the past, of literature, of nature, of the cosmos, and of ourselves.

Editorial for IRIE Vol. 29 – The International Review of Information Ethics

A short editorial I wrote for the International Review of Information Ethics (IRIE) was just published, Editorial: On IRIE Vol. 29In it I talk about how we need to get beyond principles in the ethics of artificial intelligence as the Google Duplex story shows.

The editorial was for the second part of a collection of articles that came out of a conference that the Kule Institute for Advanced Study organized on AI, Ethics and Society in 2019.

I should add that KIAS has helped move the IRIE from its previous home to the open journal platform run by the University of Alberta Library. We are grateful for the fabulous support from the UofA Library.

Why Automation is Different this Time

How is computerization affecting work and how might AI accelerate change? Erin pointed me to Kurzgesagt – In a Nutshell a series of videos that explain things “in a nutshell” produced by Kurzgesagt, a German information design firm. They have a video (see above) on The Rise of Machines that nicely explains why automation is improving productivity while not increasing the number of jobs. If anything, automation driven by AI seems to be polarizing the market for human work into high-end cognitive jobs and low-end service jobs.

The Whiteness of AI

This paper focuses on the fact that AI is predominantly portrayed as white—in colour, ethnicity, or both. We first illustrate the prevalent Whiteness

The Whiteness of AI” was mentioned in an online panel following The State of AI Ethics report (October 2020) from the Montreal AI Ethics Institute. This article starts from the observation that if you search Google images for “robot” or “AI” you get predominately images of white (or blue) entities. (Go ahead and try it.) From there it moves to the tendency of “White people; and the persistent tendency of members of that group, who dominate the academy in the US and Europe, to refuse to see themselves as racialised or race as a matter of concern at all.” (p. 686)

The paper then proposes three theories about the whiteness of AI to make it strange and to challenge the myth of colour-blindness that many of us in technology related fields live in. Important reading!

Freedom Online Coalition joint statement on artificial intelligence

The Freedom Online Coalition (FOC) has issued a joint statement on artificial intelligence (AI) and human rights.  While the FOC acknowledges that AI systems offer unprecedented opportunities for human development and innovation, the Coalition expresses concern over the documented and ongoing use of AI systems towards repressive and authoritarian purposes, including through facial recognition technology […]

The Freedom Online Coalition is a coalition of countries including Canada that “work closely together to coordinate their diplomatic efforts and engage with civil society and the private sector to support Internet freedom – free expression, association, assembly, and privacy online – worldwide.” It was founded in 2011 at the initiative of the Dutch.

FOC has just released Joint Statement on Artificial Intelligence and Human Rights that calls for “transparency, traceability and accountability” in the design and deployment of AI systems. They also reaffirm that “states must abide by their obligations under international human rights law to ensure that human rights are fully respected and protected.” The statement ends with a series of recommendations or “Calls to action”.

What is important about this statement is the role of the state recommended. This is not a set of vapid principles that developers should voluntarily adhere to. It calls for appropriate legislation.

States should consider how domestic legislation, regulation and policies can identify, prevent, and mitigate risks to human rights posed by the design, development and use of AI systems, and take action where appropriate. These may include national AI and data strategies, human rights codes, privacy laws, data protection measures, responsible business practices, and other measures that may protect the interests of persons or groups facing multiple and intersecting forms of discrimination.

I note that yesterday the Liberals introduced a Digital Charter Implementation Act that could significantly change the regulations around data privacy. More on that as I read about it.

Thanks to Florence for pointing this FOC statement out to me.

Why basing universities on digital platforms will lead to their demise – Infolet

I’m republishing here a blog essay originally in Italian that Domenico Fiormonte posted on Infolet that is worth reading,

Why basing universities on digital platforms will lead to their demise

By Domenico Fiormonte

(All links removed. They can be found in the original post – English Translation by Desmond Schmidt)

A group of professors from Italian universities have written an open letter on the consequences of using proprietary digital platforms in distance learning. They hope that a discussion on the future of education will begin as soon as possible and that the investments discussed in recent weeks will be used to create a public digital infrastructure for schools and universities.

Dear colleagues and students,

as you already know, since the COVID-19 emergency began, Italian schools and universities have relied on proprietary platforms and tools for distance learning (including exams), which are mostly produced by the “GAFAM” group of companies (Google, Apple, Facebook, Microsoft and Amazon). There are a few exceptions, such as the Politecnico di Torino, which has adopted instead its own custom-built solutions. However, on July 16, 2020 the European Court of Justice issued a very important ruling, which essentially says that US companies do not guarantee user privacy in accordance with the European General Data Protection Regulation (GDPR). As a result, all data transfers from the EU to the United States must be regarded as non-compliant with this regulation, and are therefore illegal.

A debate on this issue is currently underway in the EU, and the European Authority has explicitly invited “institutions, offices, agencies and organizations of the European Union to avoid transfers of personal data to the United States for new procedures or when securing new contracts with service providers.” In fact the Irish Authority has explicitly banned the transfer of Facebook user data to the United States. Finally, some studies underline how the majority of commercial platforms used during the “educational emergency” (primarily G-Suite) pose serious legal problems and represent a “systematic violation of the principles of transparency.”

In this difficult situation, various organizations, including (as stated below) some university professors, are trying to help Italian schools and universities comply with the ruling. They do so in the interests not only of the institutions themselves, but also of teachers and students, who have the right to study, teach and discuss without being surveilled, profiled and catalogued. The inherent risks in outsourcing teaching to multinational companies, who can do as they please with our data, are not only cultural or economic, but also legal: anyone, in this situation, could complain to the privacy authority to the detriment of the institution for which they are working.

However, the question goes beyond our own right, or that of our students, to privacy. In the renewed COVID emergency we know that there are enormous economic interests at stake, and the digital platforms, which in recent months have increased their turnover (see the study published in October by Mediobanca), now have the power to shape the future of education around the world. An example is what is happening in Italian schools with the national “Smart Class” project, financed with EU funds by the Ministry of Education. This is a package of “integrated teaching” where Pearson contributes the content for all the subjects, Google provides the software, and the hardware is the Acer Chromebook. (Incidentally, Pearson is the second largest publisher in the world, with a turnover of more than 4.5 billion euros in 2018.) And for the schools that join, it is not possible to buy other products.

Finally, although it may seem like science fiction, in addition to stabilizing proprietary distance learning as an “offer”, there is already talk of using artificial intelligence to “support” teachers in their work.

For all these reasons, a group of professors from various Italian universities decided to take action. Our initiative is not currently aimed at presenting an immediate complaint to the data protection officer, but at avoiding it, by allowing teachers and students to create spaces for discussion and encourage them to make choices that combine their freedom of teaching with their right to study. Only if the institutional response is insufficient or absent, we will register, as a last resort, a complaint to the national privacy authority. In this case the first step will be to exploit the “flaw” opened by the EU court ruling to push the Italian privacy authority to intervene (indeed, the former President, Antonello Soro, had already done so, but received no response). The purpose of these actions is certainly not to “block” the platforms that provide distance learning and those who use them, but to push the government to finally invest in the creation of a public infrastructure based on free software for scientific communication and teaching (on the model of what is proposed here and
which is already a reality for example in France, Spain and other European countries).

As we said above, before appealing to the national authority, a preliminary stage is necessary. Everyone must write to the data protection officer (DPO) requesting some information (attached here is the facsimile of the form for teachers we have prepared). If no response is received within thirty days, or if the response is considered unsatisfactory, we can proceed with the complaint to the national authority. At that point, the conversation will change, because the complaint to the national authority can be made not only by individuals, but also by groups or associations. It is important to emphasize that, even in this avoidable scenario, the question to the data controller is not necessarily a “protest” against the institution, but an attempt to turn it into a better working and study environment for everyone, conforming to European standards.

Creating ethical AI from Indigenous perspectives | Folio

Last week KIAS, AI 4 Society and SKIPP jointly sponsored Jason Lewis presenting on “Reflections on the Indigenous Protocol & Artificial Intelligence Position Paper”.

Prof. Jason Edward Lewis led the Indigenous Protocol and Artificial Intelligence Working Group in providing a starting place for those who want to design and create AI from an ethical position that centres Indigenous perspectives. Dr. Maggie Spivey- Faulkner provided a response.

Lewis talked about the importance of creative explorations from indigenous people experimenting with AI.

The Folio has published a short story on the talk, Creating ethical AI from Indigenous perspectives. The video should be up soon.

Guido Milanese: Filologia, letteratura, computer

Cover of the book "Filologia, Letteratura, Computer"
Philology, Literature, Computer: Ideas and instruments for humanistic informatics

Un manuale ampio ed esauriente che illustra tra teoria e prassi il tema dell’informatica umanistica per l’insegnamento e l’apprendimento universitario.

The publisher (Vita e Pensiero) kindly sent me a copy of Guido Milanese’s Filologia, letteratura, computer (Philology, Literature, Computer), an introduction to thinking about and thinking through the computer and texts. The book is designed to work as a text book that introduces students to the ideas and to key technologies, and then provides short guides to further ideas and readings.

The book focuses, as the title suggests, almost exclusively on digital filology or the computational study of texts. At the end Milanese has a short section on other media, but he is has chosen, rightly I think, to focus on set of technologies in depth rather than try a broad overview. In this he draws on an Italian tradition that goes back to Father Busa, but more importantly includes Tito Orlandi (who wrote the preface) and Numerico, Fiormonte, and Tomasi’s L’umanista digitale (this has been translated into English- see The digital humanist).

Milanese starts with the principle from Giambattista Vico that knowledge is made (verum ipsum factum.) Milanese believes that “reflection on the foundations identifies instruments and operations, and working with instruments and methods leads redefining the reflection on foundations.” (p. 9 – my rather free translation) This is virtuous circle in the digital humanities of theorizing and praxis where either one alone would be barren. Thus the book is not simply a list of tools and techniques one should know, but a series of reflections on humanistic knowledge and how that can be implemented in tools/techniques which in turn may challenge our ideas. This is what Stéfan Sinclair and I have been calling “thinking-through” where thinking through technology is both a way of learning about the thinking and about the technology.

An interesting example of this move from theory to praxis is in chapter 7 on “The Markup of Text.” (“La codifica del testo”) He moves from a discussion of adding metadata to the datafied raw text to Minsky’s idea of frames of knowledge as a way of understanding XML. I had never thought of Minsky’s ideas about articial intelligence contributing to the thinking behind XML, and perhaps Milanese is the first to do so, but it sort of works. The idea, as I understand it, goes something like this – human knowing, which Minsky wants to model for AI, brings frames of knowledge to any situation. If you enter a room that looks like a kitchen you have a frame of knowledge about how kitchens work that lets you infer things like “there must be a fridge somewhere which will have a snack for me.” Frames are Minsky’s way of trying to overcome the poverty of AI models based on collections of logical statements. It is a way of thinking about and actually representing the contextual or common sense knowledge that we bring to any situation such that we know a lot more than what is strictly in sight.

Frame systems are made up of frames and connections to other frames. The room frame connects hierarchically to the kitchen-as-a-type-of-room frame which connects to the fridge frame which then connects to the snack frame. The idea then is to find a way to represent frames of knowledge and their connections such that they can be used by AI systems. This is where Milanese slides over to XML as a hierarchical way of adding metadata to a text that enriches it with a frame of knowledge. I assume the frame (or Platonic form?) would be the DTD or Schema which then lets you do some limited forms of reasoning about an instance of an encoded text. The markup explicitly tells the computer something about the parts of the text like this (<author>Guido Milanese</author>) is the author.

The interesting thing is to refect on this application of Minsky’s theory. To begin, I wonder if it is historically true that the designers of XML (or its parent SGML) were thinking of Minsky’s frames. I doubt it, as SGML is descended from GML that predates Minsky’s 1974 Memo on “A Framework for Representing Knowledge.” That said, what I think Milanese is doing is using Minsky’s frames as a way of explaining what we do when modelling a phenomena like a text (and our knowledge of it.) Modelling is making explicit a particular frame of knowledge about a text. I know that certain blocks are paragraphs so I tag them as such. I also model in the sense of create a paradigmatic version of what my perspective on the text is. This would be the DTD or Schema which defines the parts and their potential relationships. Validating a marked up text would be a way of testing the instance against the model.

This nicely connects back to Vico’s knowing is making. We make digital knowledge not by objectively representing the world in digital form, but by creating frames or models for what can be digital known and then apply those frames to instances. It is a bit like object-oriented programming. You create classes that frame what can be represented about a type of object.

There is an attractive correspondence between the idea of knowledge as a hierarchy of frames and an XML representation of a text as a hierarchy of elements. There is a limit, however, to the move. Minsky was developing a theory of knowing such that knowledge could be artificially represented on a computer that could then do knowing (in the sense of complete AI tasks like image recognition.) Markup and marking up strike me as more limited activities of structuring. A paragraph tag doesn’t actually convey to the computer all that we know about paragraphs. It is just a label in a hierarchy of labels to which styles and processes can be attached. Perhaps the human modeller is thinking about texts in all their complexity, but they have to learn not to confuse what they know with what they can model for the computer. Perhaps a human reader of the XML can bring the frames of knowledge to reconstitute some of what the tagger meant, but the computer can’t.

Another way of thinking about this would be Searle’s Chinese room paradox. The XML is the bits of paper handed under the door in Chinese for the interpreter in the room. An appropriate use of XML will provoke the right operations to get something out (like a legible text on the screen) but won’t mean anything. Tagging a string with <paragraph> doesn’t make it a real paragraph in the fullness of what is known of paragraphs. It makes it a string of characters with associated metadata that may or may not be used by the computer.

Perhaps these limitations of computing is exactly what Milanese wants us to think about in modelling. Frames in the sense of picture frames are a device for limiting the view. For Minsky you can have many frames with which to make sense of any phenomena – each one is a different perspective that bears knowledge, sometimes contradictory. When modelling a text for the computer you have to decide what you want to represent and how to do it so that users can see the text through your frame. You aren’t helping the computer understand the text so much as representing your interpretation for other humans to use and, if they read the XML, re-interpret. This is making a knowing.


Milanese, G. (2020). Filologia, Letteratura, Computer: Idee e strumenti per l’informatica umanistica. Milan, Vita e Pensiero.

Minsky, M. (1974, June). A Framework for Representing Knowledge. MIT-AI Laboratory Memo 306. MIT.

Searle, J. R. (1980). “Minds, Brains and Programs.” Behavioral and Brain Sciences. 3:3. 417-457.

Conference: Artificial Intelligence for Information Accessibility

AI for Society and the Kule Institute for Advanced Research helped organize a conference on Artificial Intelligence for Information Accessibility (AI4IA) on September 28th, 2020. This conference was organized on the International Day for Universal Access to Information which is why the focus was on how AI can be important to access to information. An important partner in the conference was the UNESCO Information For All Programme (IFAP) Working Group on Information Accessibility (WGIA)

International Day for Universal Access to Information focused on the right to information in times of crisis and on the advantages of having constitutional, statutory and/or policy guarantees for public access to information to save lives, build trust and help the formulation of sustainable policies through and beyond the COVID-19 crisis. Speakers talked about how vital access to accurate information is in these pandemic times and the role artificial intelligence could play as we prepare for future crises. Tied to this was a discussion of the important role for international policy initiatives and shared regulation in ensuring that smaller countries, especially in the Global South, benefit from developments in AI. The worry is that some countries won’t have the digital literacy or cadre of experts to critically guide the introduction of AI.

The AI4S Associate Director, Geoffrey Rockwell, kept conference notes on the talks here,  Conference Notes on AI4AI 2020.

Can GPT-3 Pass a Writer’s Turing Test?

While earlier computational approaches focused on narrow and inflexible grammar and syntax, these new Transformer models offer us novel insights into the way language and literature work.

The Journal of Cultural Analytics has a nice article that asks  Can GPT-3 Pass a Writer’s Turing Test? They didn’t actually get access to GPT-3, but did test GPT-2 extensively in different projects and they assessed the output of GPT-3 reproduced in an essay on Philosophers On GPT-3. At the end they marked and commented on a number of the published short essays GPT-3 produced in response to the philosophers. They reflect on how would decide if GPT-3 were as good as an undergraduate writer.

What they never mention is Richard Powers’ novel Galatea 2.2 (Harper Perennial, 1996). In the novel an AI scientist and the narrator set out to see if they can create an AI that could pass a Masters English Literature exam. The novel is very smart and has a tragic ending.

Update: Here is a link to Awesome GPT-3 – a collection of links and articles.