Seeing, Naming, Knowing

Curated on Posted on by Stefaan Verhulst

Essay by Nora N. Khan for Brooklyn Rail: “…. Throughout this essay, I use “machine eye” as a metaphor for the unmoored orb, a kind of truly omnidirectional camera (meaning, a camera that can look in every direction and vector that defines the dimensions of a sphere), and as a symbolic shorthand for the sum of four distinct realms in which automated vision is deployed as a service. (Vision as a Service, reads the selling tag for a new AI surveillance camera company).10 Those four general realms are: 

1. Massive AI systems fueled by the public’s flexible datasets of their personal images, creating a visual culture entirely out of digitized images. 

2. Facial recognition technologies and neural networks improving atop their databases. 

3. The advancement of predictive policing to sort people by types. 

4. The combination of location-based tracking, license plate-reading, and heat sensors to render skein-like, live, evolving maps of people moving, marked as likely to do X.

Though we live the results of its seeing, and its interpretation of its seeing, for now I would hold on blaming ourselves for this situation. We are, after all, the living instantiations of a few thousand years of such violent seeing globally, enacted through imperialism, colonialism, caste stratification, nationalist purges, internal class struggle, and all the evolving theory to support and galvanize the above. Technology simply recasts, concentrates, and amplifies these “tendencies.” They can be hard to see at first because the eye’s seeing seems innocuous, and is designed to seem so. It is a direct expression of the ideology of software, which reflects its makers’ desires. These makers are lauded as American pioneers, innovators, genius-heroes living in the Bay Area in the late 1970s, vibrating at a highly specific frequency, the generative nexus of failed communalism and an emerging Californian Ideology. That seductive ideology has been exported all over the world, and we are only now contending with its impact.

Because the workings of machine visual culture are so remote from our sense perception, and because it so acutely determines our material (economic, social), and affective futures, I invite you to see underneath the eye’s outer glass shell, its holder, beyond it, to the grid that organizes its “mind.” That mind simulates a strain of ideology about who exactly gets to gather data about those on that grid below, and how that data should be mobilized to predict the movements and desires of the grid dwellers. This mind, a vast computational regime we are embedded in, drives the machine eye. And this computational regime has specific values that determine what is seen, how it is seen, and what that seeing means….(More)”.

Privacy’s not dead. It’s just not evenly distributed

Curated on Posted on by Stefaan Verhulst

Alex Pasternack in Fast Company: “In the face of all the data abuse, many of us have, quite reasonably, thrown up our hands. But privacy didn’t die. It’s just been beaten up, sold, obscured, diffused unevenly across society. What privacy is and why it matters increasingly depends upon who you are, your age, your income, gender, ethnicity, where you’re from, and where you live. To borrow William Gibson’s famous quote about the future and its unevenness and inequalities, privacy is alive—it’s just not evenly distributed. And while we don’t all care about it the same way—we’re even divided on what exactly privacy is—its harms are still real. Even when our own privacy isn’t violated, privacy violations can still hurt us.

Privacy is personal, from the creepy feeling that our phones are literally listening to the endless parade of data breaches that test our ability to care anymore. It’s the unsettling feeling of giving “consent” without knowing what that means, “agreeing” to contracts we didn’t read with companies we don’t really trust. (Forget about understanding all the details; researchers have shown that most privacy policies surpass the reading level of the average person.)

It’s the data about us that’s harvested, bought, sold, and traded by an obscure army of data brokers without our knowledge, feeding marketers, landlords, employers, immigration officials, insurance companies, debt collectors, as well as stalkers and who knows who else. It’s the body camera or the sports arena or the social network capturing your face for who knows what kind of analysis. Don’t think of personal data as just “data.” As it gets more detailed and more correlated, increasingly, our data is us.

And “privacy” isn’t just privacy. It’s also tied up with security, freedom, social justice, free speech, and free thought. Privacy harms aren’t only personal, but societal. It’s not just the multibillion-dollar industry that aims to nab you and nudge you, but the multibillion-dollar spyware industry that helps governments nab dissidents and send them to prison or worse. It’s the supposedly fair and transparent algorithms that aren’t, turning our personal data into risk scores that can help perpetuate race, class, and gender divides, often without our knowing it.

Privacy is about dark ads bought with dark money and the micro-targeting of voters by overseas propagandists or by political campaigns at home. That kind of influence isn’t just the promise of a shadowy Cambridge Analytica or state-run misinformation campaigns, but also the premise of modern-day digital ad campaigns. (Note that Facebook’s research division later hired one of the researchers behind the Cambridge app.) And as the micro-targeting gets more micro, the tech giants that deal in ads are only getting more macro….(More)”

(This story is part of The Privacy Divide, a series that explores the fault lines and disparities–economic, cultural, philosophical–that have developed around digital privacy and its impact on society.)

Transparency, Fairness, Data Protection, Neutrality: Data Management Challenges in the Face of New Regulation

Curated on Posted on by Stefaan Verhulst

Paper by Serge Abiteboul and Julia Stoyanovich: “The data revolution continues to transform every sector of science, industry and government. Due to the incredible impact of data-driven technology on society, we are becoming increasingly aware of the imperative to use data and algorithms responsibly — in accordance with laws and ethical norms. In this article we discuss three recent regulatory frameworks: the European Union’s General Data Protection Regulation (GDPR), the New York City Automated Decisions Systems (ADS) Law, and the Net Neutrality principle, that aim to protect the rights of individuals who are impacted by data collection and analysis. These frameworks are prominent examples of a global trend: Governments are starting to recognize the need to regulate data-driven algorithmic technology. 


Our goal in this paper is to bring these regulatory frameworks to the attention of the data management community, and to underscore the technical challenges they raise and which we, as a community, are well-equipped to address. The main .take-away of this article is that legal and ethical norms cannot be incorporated into data-driven systems as an afterthought. Rather, we must think in terms of responsibility by design, viewing it as a systems requirement….(More)”

Seeing and Being Seen

Curated on Posted on by Stefaan Verhulst

Russell C. Bogue in The Hedgehog Review: “On May 20, 2013, a pale, nervous American landed in Hong Kong and made his way to the Mira Hotel. Once there, he met with reporters from The Guardian and the Washington Post and turned over thousands of documents his high-level security clearance had enabled him to acquire while working as a contractor for the National Security Agency. Soon after this exchange, the world learned about PRISM, a top-secret NSA program that granted (court-ordered) direct access to Facebook, Apple, Google, and other US Internet giants, including users’ search histories, e-mails, file transfers, and live chats.1 Additionally, Verizon had been providing information to the NSA on an “ongoing, daily basis” about customers’ telephone calls, including location data and call duration (although not the content of conversations).2 Everyone, in short, was being monitored. Glenn Greenwald, one of the first journalists to meet with Edward Snowden, and one of his most vocal supporters, wrote later that “the NSA is collecting all forms of electronic communications between Americans…and thereby attempting by definition to destroy any remnants of privacy both in the US and globally.”3

According to a 2014 Pew Research Center poll, fully 91 percent of Americans believe they have lost control over their personal information.4 What is such a public to do? Anxious computer owners have taken to covering their devices’ built-in cameras with bits of tape.5Messaging services tout their end-to-end encryption.6 Researchers from Harvard Business School have started investigating the effectiveness of those creepy online ads that seem to know a little too much about your preferences.7

For some, this pushback has come far too late to be of any use. In a recent article in The Atlantic depressingly titled “Welcome to the Age of Privacy Nihilism,” Ian Bogost observes that we have already become unduly reliant on services that ask us to relinquish personal data in exchange for convenience. To reassert control over one’s privacy, one would have to abstain from credit card activity and use the Internet only sparingly. The worst part? We don’t get the simple pleasure of blaming this state of affairs on Big Government or the tech giants. Instead, our enemy is, as Bogost intones, “a hazy murk, a chilling, Lovecraftian murmur that can’t be seen, let alone touched, let alone vanquished.”8

The enemy may be a bit closer to home, however. While we fear being surveilled, recorded, and watched, especially when we are unaware, we also compulsively expose ourselves to others….(More)”.

EU Data Protection Rules and U.S. Implications

Curated on Posted on by Stefaan Verhulst

In Focus by the Congressional Research Service: “U.S. and European citizens are increasingly concerned about ensuring the protection of personal data, especially online. A string of high-profile data breaches at companies such as Facebook and Google have contributed to heightened public awareness. The European Union’s (EU) new General Data Protection Regulation (GDPR)—which took effect on May 25, 2018—has drawn the attention of U.S. businesses and other stakeholders, prompting debate on U.S. data privacy and protection policies.

Both the United States and the 28-member EU assert that they are committed to upholding individual privacy rights and ensuring the protection of personal data, including electronic data. However, data privacy and protection issues have long been sticking points in U.S.-EU economic and security relations, in part because of differences in U.S. and EU legal regimes and approaches to data privacy.

The GDPR highlights some of those differences and poses challenges for U.S. companies doing business in the EU. The United States does not broadly restrict cross-border data flows and has traditionally regulated privacy at a sectoral level to cover certain types of data. The EU considers the privacy of communications and the protection of personal data to be fundamental rights, which are codified in EU law. Europe’s history with fascist and totalitarian regimes informs the EU’s views on data protection and contributes to the demand for strict data privacy controls. The EU regards current U.S. data protection safeguards as inadequate; this has complicated the conclusion of U.S.-EU information-sharing agreements and raised concerns about U.S.-EU data flows….(More).

Big data needs big governance: best practices from Brain-CODE, the Ontario Brain Institute’s neuroinformatics platform

Curated on Posted on by Stefaan Verhulst

Shannon C. Lefaivre et al in Frontiers of Genetics: “The Ontario Brain Institute (OBI) has begun to catalyze scientific discovery in the field of neuroscience through its large-scale informatics platform, known as Brain-CODE. The platform supports the capture, storage, federation, sharing and analysis of different data types across several brain disorders. Underlying the platform is a robust and scalable data governance structure which allows for the flexibility to advance scientific understanding, while protecting the privacy of research participants.

Recognizing the value of an open science approach to enabling discovery, the governance structure was designed not only to support collaborative research programs, but also to support open science by making all data open and accessible in the future. OBI’s rigorous approach to data sharing maintains the accessibility of research data for big discoveries without compromising privacy and security. Taking a Privacy by Design approach to both data sharing and development of the platform has allowed OBI to establish some best practices related to large scale data sharing within Canada. The aim of this report is to highlight these best practices and develop a key open resource which may be referenced during the development of similar open science initiatives….(More)”.

Balancing information governance obligations when accessing social care data for collaborative research

Curated on Posted on by Stefaan Verhulst

Paper by Malkiat Thiarai, Sarunkorn Chotvijit and Stephen Jarvis: “There is significant national interest in tackling issues surrounding the needs of vulnerable children and adults. This paper aims to argue that much value can be gained from the application of new data-analytic approaches to assist with the care provided to vulnerable children. This paper highlights the ethical and information governance issues raised in the development of a research project that sought to access and analyse children’s social care data.


The paper documents the process involved in identifying, accessing and using data held in Birmingham City Council’s social care system for collaborative research with a partner organisation. This includes identifying the data, its structure and format; understanding the Data Protection Act 1998 and 2018 (DPA) exemptions that are relevant to ensure that legal obligations are met; data security and access management; the ethical and governance approval process.


The findings will include approaches to understanding the data, its structure and accessibility tasks involved in addressing ethical and legal obligations and requirements of the ethical and governance processes….(More)”.

Privacy and Smart Cities: A Canadian Survey

Curated on Posted on by Stefaan Verhulst

Report by Sara Bannerman and Angela Orasch: “This report presents the findings of a national survey of Canadians about smart-city privacy conducted in October and November 2018. Our research questions were: How concerned are Canadians about smart-city privacy? How do these concerns intersect with age, gender, ethnicity, and location? Moreover, what are the expectations of Canadians with regards to their ability to control, use, or opt-out of data collection in smart-city context? What rights and privileges do Canadians feel are appropriate with regard to data self-determination, and what types of data are considered more sensitive than others?

What is a smart city?
A ‘smart city’ adopts digital and data-driven technologies in the planning, management and delivery of municipal services. Information and communications technologies (ICTs), data analytics, and the internet of
things (IoT) are some of the main components of these technologies, joined by web design, online marketing campaigns and digital services. Such technologies can include smart utility and transportation infrastructure, smart cards, smart transit, camera and sensor networks, or data collection by businesses to provide customized advertisements or other services. Smart-city technologies “monitor, manage and regulate city flows and processes, often in real-time” (Kitchin 2014, 2).

In 2017, a framework agreement was established between Waterfront Toronto, the organization charged with revitalizing Toronto’s waterfront, and Sidewalk Labs, parent company of Google, to develop a smart city on Toronto’s Eastern waterfront (Sidewalk Toronto 2018). This news was met with questions and concerns from experts in data privacy and the public at large regarding what was to be included in Sidewalk Lab’s smart-city vision. How would the overall governance structure function? How were the privacy rights of residents going to be protected, and what mechanisms, if any, would ensure that protection? The Toronto waterfront is just one of numerous examples of smart-city developments….(More)”.

Consumers kinda, sorta care about their data

Curated on Posted on by Stefaan Verhulst

Kim Hart at Axios: “A full 81% of consumers say that in the past year they’ve become more concerned with how companies are using their data, and 87% say they’ve come to believe companies that manage personal data should be more regulated, according to a survey out Monday by IBM’s Institute for Business Value.

Yes, but: They aren’t totally convinced they should care about how their data is being used, and many aren’t taking meaningful action after privacy breaches, according to the survey. Despite increasing data risks, 71% say it’s worth sacrificing privacy given the benefits of technology.Show less

By the numbers:

  • 89% say technology companies need to be more transparent about their products
  • 75% say that in the past year they’ve become less likely to trust companies with their personal data
  • 88% say the emergence of technologies like AI increase the need for clear policies about the use of personal data.

The other side: Despite increasing awareness of privacy and security breaches, most consumers aren’t taking consequential action to protect their personal data.

  • Fewer than half (45%) report that they’ve updated privacy settings, and only 16% stopped doing business with an entity due to data misuse….(More)”.

Algorithmic fairness: A code-based primer for public-sector data scientists

Curated on Posted on by Stefaan Verhulst

Paper by Ken Steif and Sydney Goldstein: “As the number of government algorithms grow, so does the need to evaluate algorithmic fairness. This paper has three goals. First, we ground the notion of algorithmic fairness in the context of disparate impact, arguing that for an algorithm to be fair, its predictions must generalize across different protected groups. Next, two algorithmic use cases are presented with code examples for how to evaluate fairness. Finally, we promote the concept of an open source repository of government algorithmic “scorecards,” allowing stakeholders to compare across algorithms and use cases….(More)”.