Source: https://www.technologyreview.com/2021/03/05/1020376/resist-big-tech-surveillance-data/

by Karen Hao, March 5, 2021

Every day, your life leaves a trail of digital breadcrumbs that tech giants use to track you. You send an email, order some food, stream a show. They get back valuable packets of data to build up their understanding of your preferences. That data is fed into machine-learning algorithms to target you with ads and recommendations. Google cashes your data in for over $120 billion a year of ad revenue.

Increasingly, we can no longer opt out of this arrangement. In 2019 Kashmir Hill, then a reporter for Gizmodo, famously tried to cut five major tech giants out of her life. She spent six weeks being miserable, struggling to perform basic digital functions. The tech giants, meanwhile, didn’t even feel an itch.

Now researchers at Northwestern University are suggesting new ways to redress this power imbalance by treating our collective data as a bargaining chip. Tech giants may have fancy algorithms at their disposal, but they are meaningless without enough of the right data to train on.

In a new paper being presented at the Association for Computing Machinery’s Fairness, Accountability, and Transparency conference next week, researchers including PhD students Nicholas Vincent and Hanlin Li propose three ways the public can exploit this to their advantage:

• Data strikes, inspired by the idea of labor strikes, which involve withholding or deleting your data so a tech firm cannot use it—leaving a platform or installing privacy tools, for instance.
• Data poisoning, which involves contributing meaningless or harmful data. AdNauseam, for example, is a browser extension that clicks on every single ad served to you, thus confusing Google’s ad-targeting algorithms.
• Conscious data contribution, which involves giving meaningful data to the competitor of a platform you want to protest, such as by uploading your Facebook photos to Tumblr instead.

Read more at: https://www.technologyreview.com/2021/03/05/1020376/resist-big-tech-surveillance-data/

Related

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[Submitted on 2 Feb 2021]

The Limits of Global Inclusion in AI Development

Alan Chan, Chinasa T. Okolo, Zachary Terner, Angelina Wang

Those best-positioned to profit from the proliferation of artificial intelligence (AI) systems are those with the most economic power. Extant global inequality has motivated Western institutions to involve more diverse groups in the development and application of AI systems, including hiring foreign labour and establishing extra-national data centers and laboratories. However, given both the propensity of wealth to abet its own accumulation and the lack of contextual knowledge in top-down AI solutions, we argue that more focus should be placed on the redistribution of power, rather than just on including underrepresented groups. Unless more is done to ensure that opportunities to lead AI development are distributed justly, the future may hold only AI systems which are unsuited to their conditions of application, and exacerbate inequality.

PDF available at https://arxiv.org/abs/2102.01265

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18 NOVEMBER 2020

Journals and researchers are under fire for controversial studies using this technology. And a Nature survey reveals that many researchers in this field think there is a problem.

Richard Van Noorden

In September 2019, four researchers wrote to the publisher Wiley to “respectfully ask” that it immediately retract a scientific paper. The study, published in 2018, had trained algorithms to distinguish faces of Uyghur people, a predominantly Muslim minority ethnic group in China, from those of Korean and Tibetan ethnicity¹.

China had already been internationally condemned for its heavy surveillance and mass detentions of Uyghurs in camps in the northwestern province of Xinjiang — which the government says are re-education centres aimed at quelling a terrorist movement. According to media reports, authorities in Xinjiang have used surveillance cameras equipped with software attuned to Uyghur faces.

As a result, many researchers found it disturbing that academics had tried to build such algorithms — and that a US journal had published a research paper on the topic. And the 2018 study wasn’t the only one: journals from publishers including Springer Nature, Elsevier and the Institute of Electrical and Electronics Engineers (IEEE) had also published peer-reviewed papers that describe using facial recognition to identify Uyghurs and members of other Chinese minority groups. (Nature’s news team is editorially independent from its publisher, Springer Nature.)

The complaint, which launched an ongoing investigation, was one foray in a growing push by some scientists and human-rights activists to get the scientific community to take a firmer stance against unethical facial-recognition research. It’s important to denounce controversial uses of the technology, but that’s not enough, ethicists say. Scientists should also acknowledge the morally dubious foundations of much of the academic work in the field — including studies that have collected enormous data sets of images of people’s faces without consent, many of which helped hone commercial or military surveillance algorithms. (A linked feature explores concerns over algorithmic bias in facial-recognition systems.)

Read more at: https://doi.org/10.1038/d41586-020-03187-3

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https://doi.org/10.1111/rego.12331

Abstract

This paper explores the extent to which current interpretations of the notion of agency, as traditionally perceived under human rights law, pose challenges to human rights protection in light of algorithmic surveillance. After examining the notion of agency under the European Convention on Human Rights as a criterion for applications’ admissibility, the paper looks into the safeguards of notification and of redress – crucial safeguards developed by the Court in secret surveillance cases – which are used as examples to illustrate their insufficiency in light of algorithmic surveillance. The use of algorithms creates new surveillance methods and challenges fundamental presuppositions on the notion of agency in human rights protection. Focusing on the victim status does not provide a viable solution to problems arising from the use of Artificial Intelligence in state surveillance. The paper thus raises questions for further research concluding that a new way of thinking about agency for the protection of human rights in the context of algorithmic surveillance is needed in order to offer effective protection to individuals.

6 Conclusions

This paper explored the extent to which current interpretations of the notion of agency, as traditionally perceived under human rights law, pose challenges to human rights protection in light of algorithmic surveillance. It examined the notion of agency under the European Convention on Human Rights as a criterion for applications’ admissibility. It elucidated on the two admissibility criteria: the entities that can file an application and the victim status. The interpretation of the victim status in secret surveillance cases has been expanded in the case law of the ECtHRs in order to accept in abstracto claims under specific criteria.

The safeguards of notification and of redress – crucial safeguards developed by the Court in the context of protection of human rights, and in particular the right to privacy, in secret surveillance cases – were used as examples to illustrate their insufficiency in light of algorithmic surveillance. The use of algorithms for state surveillance creates new surveillance methods and challenges fundamental presuppositions on the notion of agency in human rights protection. A close analysis of the Court’s approach to the admissibility criteria of Article 34 ECHR in recent ECtHR case law on secret surveillance showed that the focus of the Court’s analysis lies on the victim status. The existence of effective remedies is crucial in order to acknowledge the victim status to applicants with or without the need to demonstrate that they are potentially at risk.

This approach however is not providing a viable solution to problems arising from the use of AI in state surveillance, as it assumes that the individuals can suspect that they are potentially at risk and argue that in front of the Court. Even when applicants do not need to show that they are potentially at risk, there would need to be some indication that the secret surveillance measure resulting from the use of AI can be relevant for them. Problems relating to group profiles or dynamic groups discussed in the paper show that in some cases it is impossible to actually know who is or could be victim of the surveillance measure. Therefore, further research is needed in order to find a completely new way of thinking about agency for the protection of human rights in the context of algorithmic surveillance in order to offer effective protection to individuals.

The full paper is available at: https://onlinelibrary.wiley.com/doi/full/10.1111/rego.12331

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Artificial intelligence has moved into the commercial mainstream thanks to the growing prowess of machine learning algorithms that enable computers to train themselves to do things like drive cars, control robots or automate decision-making.

But as AI starts handling sensitive tasks, such as helping pick which prisoners get bail, policy makers are insisting that computer scientists offer assurances that automated systems have been designed to minimize, if not completely avoid, unwanted outcomes such as excessive risk or racial and gender bias.

A team led by researchers at Stanford and the University of Massachusetts Amherst published a paper Nov. 22 in Science suggesting how to provide such assurances. The paper outlines a new technique that translates a fuzzy goal, such as avoiding gender bias, into the precise mathematical criteria that would allow a machine-learning algorithm to train an AI application to avoid that behavior.

“We want to advance AI that respects the values of its human users and justifies the trust we place in autonomous systems,” said Emma Brunskill, an assistant professor of computer science at Stanford and senior author of the paper.

Read more at: https://www.technologynetworks.com/informatics/news/bots-behaving-badly-can-we-trust-in-an-algorithm-controlled-society-327530?utm_campaign=NEWSLETTER_TN_Breaking%20Science%20News&utm_source=hs_email&utm_medium=email&utm_content=79781295&_hsenc=p2ANqtz-_TmdSunp6ClmtSsWTLS27Iqco6dBUdA9zJDockkECzhIYVZbiqColNIhKO-UalFMMBMUoFp5TZV5gQJEZLz1nUyv4nbg&_hsmi=79781295

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We find our speakers through a combination of literature reviews and calls with experts and community leaders. This year, we wanted to share the fruits of that research. Sources written by or featuring our speakers are listed first and are followed by other sources in alphabetical order.

General Historical Background:

In Our Times, “The Poor Laws,” BBC Radio, Dec. 20, 2018.

https://www.bbc.co.uk/programmes/m0001m73

Simone Browne, Dark Matters: On the Surveillance of Blackness (2015).

https://read.dukeupress.edu/books/book/147/Dark-MattersOn-the-Surveillance-of-Blackness

Matthew Desmond, “In order to understand the brutality of American capitalism, you have to start on the plantation.” The New York Times Magazine, Aug. 14, 2019.

https://www.nytimes.com/interactive/2019/08/14/magazine/slavery-capitalism.html

For more please see the pdf below:

CoS4 Reading List

Snippet:

Automation is coming for truckers. But first, they’re being watched:

https://www.vox.com/videos/2017/11/20/16670266/trucking-eld-surveillance

The post The Color of Surveillance: Monitoring of Poor and Working People Reading List appeared first on EMFSA.

by Kalev Leetaru

Excerpts

Deep learning’s magic-like capabilities mask its very real limitations. To the general public, AI systems stand poised to sweep aside humanity itself, with algorithms replacing human workers and killer robots ready to run amok. Policymakers rush to consider legislation for self-aware machines, the future of capitalism in a job-free world and arms treaties that consider the impact of mechanized precision and speed in wartime strategy. The press runs breathless headlines that alternate between “the machines are taking over” and “the machines are useless.” The truth stands between all of these stances.

Today’s most advanced AI systems are little more than glorified spreadsheets with exciting backstories. Machines that can perform captivating parlor tricks but whose reality is far less impressive.

Even when not given the Hollywood treatment, deep learning algorithms are portrayed to the public as superhuman intelligences that can learn about the world faster and better than any human.

Most troubling, deep learning algorithms are often portrayed as a solution to society’s underlying biases, with “unbiased” machines able to render judgement free of emotion or discrimination.

Unfortunately, today’s AI systems lack the ability to acquire generalized world knowledge or to distill their lessons into their own distinctive worldview through which they can contextualize new information. Instead, their entire knowledge of the world itself comes through the small amounts of training data they are fed. Lacking external knowledge that could counter the biases in this training data, the resulting algorithms have little choice but to simply encode the biases of their input data into their own programming.

Once encoded in software, however, we no longer think of these patterns as biases, laundered as they are through the magic of mathematics. A biased human is considered biased. A biased algorithm is considered to merely be expressing a societal truth through the precision of mathematics encoded in software and executed by infallible machines.

Lacking an understanding of how machines learn, policymakers are unable to craft legislation that might standardize the auditing of algorithms used in critical areas like judicial sentencing, predictive policing and facial recognition.

In the end, as deep learning infuses into every corner of modern life, if we are to have the necessary societal conversations about AI’s role in our society we need to have an informed press, public and policymaking body that can help us have a conversation about our increasingly automated future.

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