Biometric Mirror

University of Melbourne: “Biometric Mirror exposes the possibilities of artificial intelligence and facial analysis in public space. The aim is to investigate the attitudes that emerge as people are presented with different perspectives on their own, anonymised biometric data distinguished from a single photograph of their face. It sheds light on the specific data that people oppose and approve, the sentiments it evokes, and the underlying reasoning. Biometric Mirror also presents an opportunity to reflect on whether the plausible future of artificial intelligence is a future we want to see take shape.

Big data and artificial intelligence are some of today’s most popular buzzwords. Both are promised to help deliver insights that were previously too complex for computer systems to calculate. With examples ranging from personalised recommendation systems to automatic facial analyses, user-generated data is now analysed by algorithms to identify patterns and predict outcomes. And the common view is that these developments will have a positive impact on society.

Within the realm of artificial intelligence (AI), facial analysis gains popularity. Today, CCTV cameras and advertising screens increasingly link with analysis systems that are able to detect emotions, age, gender and demographic information of people passing by. It has proven to increase advertising effectiveness in retail environments, since campaigns can now be tailored to specific audience profiles and situations. But facial analysis models are also being developed to predict your aggression levelsexual preferencelife expectancy and likeliness of being a terrorist (or an academic) by simply monitoring surveillance camera footage or analysing a single photograph. Some of these developments have gained widespread media coverage for their innovative nature, but often the ethical and social impact is only a side thought.

Current technological developments approach ethical boundaries of the artificial intelligence age. Facial recognition and analysis in public space raise concerns as people are photographed without prior consent, and their photos disappear into a commercial operator’s infrastructure. It remains unclear how the data is processed, how the data is tailored for specific purposes and how the data is retained or disposed of. People also do not have the opportunity to review or amend their facial recognition data. Perhaps most worryingly, artificial intelligence systems may make decisions or deliver feedback based on the data, regardless of its accuracy or completeness. While facial recognition and analysis may be harmless for tailored advertising in retail environments or to unlock your phone, it quickly pushes ethical boundaries when the general purpose is to more closely monitor society… (More).

Social media big data analytics: A survey

Norjihan Abdul Ghani et al in Computers in Human Behavior: “Big data analytics has recently emerged as an important research area due to the popularity of the Internet and the advent of the Web 2.0 technologies. Moreover, the proliferation and adoption of social media applications have provided extensive opportunities and challenges for researchers and practitioners. The massive amount of data generated by users using social media platforms is the result of the integration of their background details and daily activities.

This enormous volume of generated data known as “big data” has been intensively researched recently. A review of the recent works is presented to obtain a broad perspective of the social media big data analytics research topic. We classify the literature based on important aspects. This study also compares possible big data analytics techniques and their quality attributes. Moreover, we provide a discussion on the applications of social media big data analytics by highlighting the state-of-the-art techniques, methods, and the quality attributes of various studies. Open research challenges in big data analytics are described as well….(More)”.

When Westlaw Fuels Ice Surveillance: Ethics in the Big Data Policing Era

Sarah Lamdan at New York University Review of Law & Social Change: “Legal research companies are selling surveillance data and services to U.S. Immigration and Customs Enforcement (ICE) and other law enforcement agencies.

This article discusses ethical issues that arise when lawyers buy and use legal research services sold by the vendors that build ICE’s surveillance systems. As the legal profession collectively pays millions of dollars for computer assisted legal research services, lawyers should consider whether doing so in the era of big data policing compromises their confidentiality requirements and their obligation to supervise third party vendors….(More)”

Technology is threatening our democracy. How do we save it?

MIT Technology Review: “Our newest issue is live today, in which we dive into the many ways that technology is changing politics.

A major shift: In 2013 we emblazoned our cover with the words, “Big Data Will Save Politics.” When we chose that headline, Barack Obama had just won reelection with the help of a crack team of data scientists. The Arab Spring had already cooled into an Arab Winter, but the social-media platforms that had powered the uprisings were still basking in the afterglow. As our editor in chief Gideon Lichfield writes, today, with Cambridge Analytica, fake news, election hacking, and the shrill cacophony that dominates social media, technology feels as likely to destroy politics as to save it.

The political impact: From striking data visualizations that take a close look at the famed “filter bubble” effect that’s blamed for political polarization to an examination of how big data is disrupting the cozy world of political lobbying, we’re analyzing how emerging technologies are shaping the political landscape, eroding trust, and, possibly, becoming a part of the solution….(More)”.

Data-Driven Law: Data Analytics and the New Legal Services

Book by Edward J. Walters: “For increasingly data-savvy clients, lawyers can no longer give “it depends” answers rooted in anecdata. Clients insist that their lawyers justify their reasoning, and with more than a limited set of war stories. The considered judgment of an experienced lawyer is unquestionably valuable. However, on balance, clients would rather have the considered judgment of an experienced lawyer informed by the most relevant information required to answer their questions.

Data-Driven Law: Data Analytics and the New Legal Services helps legal professionals meet the challenges posed by a data-driven approach to delivering legal services. Its chapters are written by leading experts who cover such topics as:

  • Mining legal data
  • Computational law
  • Uncovering bias through the use of Big Data
  • Quantifying the quality of legal services
  • Data mining and decision-making
  • Contract analytics and contract standards

In addition to providing clients with data-based insight, legal firms can track a matter with data from beginning to end, from the marketing spend through to the type of matter, hours spent, billed, and collected, including metrics on profitability and success. Firms can organize and collect documents after a matter and even automate them for reuse. Data on marketing related to a matter can be an amazing source of insight about which practice areas are most profitable….(More)”.

A roadmap for restoring trust in Big Data

Mark Lawler et al in the Lancet: “The fallout from the Cambridge Analytica–Facebook scandal marks a significant inflection point in the public’s trust concerning Big Data. The health-science community must use this crisis-in-confidence to redouble its commitment to talk openly and transparently about benefits and risks and to act decisively to deliver robust effective governance frameworks, under which personal health data can be responsibly used. Activities such as the Innovative Medicines Initiative’s Big Data for Better Outcomes emphasise how a more granular data-driven understanding of human diseases including cancer could underpin innovative therapeutic intervention.
 Health Data Research UK is developing national research expertise and infrastructure to maximise the value of health data science for the National Health Service and ultimately British citizens.
Comprehensive data analytics are crucial to national programmes such as the US Cancer Moonshot, the UK’s 100 000 Genomes project, and other national genomics programmes. Cancer Core Europe, a research partnership between seven leading European oncology centres, has personal data sharing at its core. The Global Alliance for Genomics and Health recently highlighted the need for a global cancer knowledge network to drive evidence-based solutions for a disease that kills more than 8·7 million citizens annually worldwide. These activities risk being fatally undermined by the recent data-harvesting controversy.
We need to restore the public’s trust in data science and emphasise its positive contribution in addressing global health and societal challenges. An opportunity to affirm the value of data science in Europe was afforded by Digital Day 2018, which took place on April 10, 2018, in Brussels, and where European Health Ministers signed a declaration of support to link existing or future genomic databanks across the EU, through the Million European Genomes Alliance.
So how do we address evolving challenges in analysis, sharing, and storage of information, ensure transparency and confidentiality, and restore public trust? We must articulate a clear Social Contract, where citizens (as data donors) are at the heart of decision-making. We need to demonstrate integrity, honesty, and transparency as to what happens to data and what level of control people can, or cannot, expect. We must embed ethical rigour in all our data-driven processes. The Framework for Responsible Sharing of Genomic and Health Related Data represents a practical global approach, promoting effective and ethical sharing and use of research or patient data, while safeguarding individual privacy through secure and accountable data transfer…(More)”.

Data Colonialism: Rethinking Big Data’s Relation to the Contemporary Subject

Nick Couldry and Ulises Mejias in Television & New Media (TVNM): “...Data colonialism combines the predatory extractive practices of historical colonialism with the abstract quantification methods of computing. Understanding Big Data from the Global South means understanding capitalism’s current dependence on this new type of appropriation that works at every point in space where people or things are attached to today’s infrastructures of connection. The scale of this transformation means that it is premature to map the forms of capitalism that will emerge from it on a global scale. Just as historical colonialism over the long-run provided the essential preconditions for the emergence of industrial capitalism, so over time, we can expect that data colonialism will provide the preconditions for a new stage of capitalism that as yet we can barely imagine, but for which the appropriation of human life through data will be central.

Right now, the priority is not to speculate about that eventual stage of capitalism, but to resist the data colonialism that is under way. This is how we understand Big Data from the South. Through what we call ‘data relations’ (new types of human relations which enable the extraction of data for commodification), social life all over the globe becomes an ‘open’ resource for extraction that is somehow ‘just there’ for capital. These global flows of data are as expansive as historic colonialism’s appropriation of land, resources, and bodies, although the epicentre has somewhat shifted. Data colonialism involves not one pole of colonial power (‘the West’), but at least two: the USA and China. This complicates our notion of the geography of the Global South, a concept which until now helped situate resistance and disidentification along geographic divisions between former colonizers and colonized. Instead, the new data colonialism works both externally — on a global scale — and internally on its own home populations. The elites of data colonialism (think of Facebook) benefit from colonization in both dimensions, and North-South, East-West divisions no longer matter in the same way.

It is important to acknowledge both the apparent similarities and the significant differences between our argument and the many preceding critical arguments about Big Data…(More)”

From Code to Cure

David J. Craig at Columbia Magazine: “Armed with enormous amounts of clinical data, teams of computer scientists, statisticians, and physicians are rewriting the rules of medical research….

The deluge is upon us.

We are living in the age of big data, and with every link we click, every message we send, and every movement we make, we generate torrents of information.

In the past two years, the world has produced more than 90 percent of all the digital data that has ever been created. New technologies churn out an estimated 2.5 quintillion bytes per day. Data pours in from social media and cell phones, weather satellites and space telescopes, digital cameras and video feeds, medical records and library collections. Technologies monitor the number of steps we walk each day, the structural integrity of dams and bridges, and the barely perceptible tremors that indicate a person is developing Parkinson’s disease. These are the building blocks of our knowledge economy.

This tsunami of information is also providing opportunities to study the world in entirely new ways. Nowhere is this more evident than in medicine. Today, breakthroughs are being made not just in labs but on laptops, as biomedical researchers trained in mathematics, computer science, and statistics use powerful new analytic tools to glean insights from enormous data sets and help doctors prevent, treat, and cure disease.

“The medical field is going through a major period of transformation, and many of the changes are driven by information technology,” says George Hripcsak ’85PS,’00PH, a physician who chairs the Department of Biomedical Informatics at Columbia University Irving Medical Center (CUIMC). “Diagnostic techniques like genomic screening and high-resolution imaging are generating more raw data than we’ve ever handled before. At the same time, researchers are increasingly looking outside the confines of their own laboratories and clinics for data, because they recognize that by analyzing the huge streams of digital information now available online they can make discoveries that were never possible before.” …

Consider, for example, what the young computer scientist has been able to accomplish in recent years by mining an FDA database of prescription-drug side effects. The archive, which contains millions of reports of adverse drug reactions that physicians have observed in their patients, is continuously monitored by government scientists whose job it is to spot problems and pull drugs off the market if necessary. And yet by drilling down into the database with his own analytic tools, Tatonetti has found evidence that dozens of commonly prescribed drugs may interact in dangerous ways that have previously gone unnoticed. Among his most alarming findings: the antibiotic ceftriaxone, when taken with the heartburn medication lansoprazole, can trigger a type of heart arrhythmia called QT prolongation, which is known to cause otherwise healthy people to suddenly drop dead…(More)”

Big Data Is Getting Bigger. So Are the Privacy and Ethical Questions.

Goldie Blumenstyk at The Chronicle of Higher Education: “…The next step in using “big data” for student success is upon us. It’s a little cool. And also kind of creepy.

This new approach goes beyond the tactics now used by hundreds of colleges, which depend on data collected from sources like classroom teaching platforms and student-information systems. It not only makes a technological leap; it also raises issues around ethics and privacy.

Here’s how it works: Whenever you log on to a wireless network with your cellphone or computer, you leave a digital footprint. Move from one building to another while staying on the same network, and that network knows how long you stayed and where you went. That data is collected continuously and automatically from the network’s various nodes.

Now, with the help of a company called Degree Analytics, a few colleges are beginning to use location data collected from students’ cellphones and laptops as they move around campus. Some colleges are using it to improve the kind of advice they might send to students, like a text-message reminder to go to class if they’ve been absent.

Others see it as a tool for making decisions on how to use their facilities. St. Edward’s University, in Austin, Tex., used the data to better understand how students were using its computer-equipped spaces. It found that a renovated lounge, with relatively few computers but with Wi-Fi access and several comfy couches, was one of the most popular such sites on campus. Now the university knows it may not need to buy as many computers as it once thought.

As Gary Garofalo, a co-founder and chief revenue officer of Degree Analytics, told me, “the network data has very intriguing advantages” over the forms of data that colleges now collect.

Some of those advantages are obvious: If you’ve got automatic information on every person walking around with a cellphone, your dataset is more complete than if you need to extract it from a learning-management system or from the swipe-card readers some colleges use to track students’ activities. Many colleges now collect such data to determine students’ engagement with their coursework and campus activities.

Of course, the 24-7 reporting of the data is also what makes this approach seem kind of creepy….

I’m not the first to ask questions like this. A couple of years ago, a group of educators organized by Martin Kurzweil of Ithaka S+R and Mitchell Stevens of Stanford University issued a series of guidelines for colleges and companies to consider as they began to embrace data analytics. Among other principles, the guidelines highlighted the importance of being transparent about how the information is used, and ensuring that institutions’ leaders really understand what companies are doing with the data they collect. Experts at New America weighed in too.

I asked Kurzweil what he makes of the use of Wi-Fi information. Location tracking tends toward the “dicey” side of the spectrum, he says, though perhaps not as far out as using students’ social-media habits, health information, or what they check out from the library. The fundamental question, he says, is “how are they managing it?”… So is this the future? Benz, at least, certainly hopes so. Inspired by the Wi-Fi-based StudentLife research project at Dartmouth College and the experiences Purdue University is having with students’ use of its Forecast app, he’s in talks now with a research university about a project that would generate other insights that might be gleaned from students’ Wi-Fi-usage patterns….(More)

Big Data: the End of the Scientific Method?

Paper by S. Succi and P.V. Coveney at arXiv: “We argue that the boldest claims of Big Data are in need of revision and toning-down, in view of a few basic lessons learned from the science of complex systems. We point out that, once the most extravagant claims of Big Data are properly discarded, a synergistic merging of BD with big theory offers considerable potential to spawn a new scientific paradigm capable of overcoming some of the major barriers confronted by the modern scientific method originating with Galileo. These obstacles are due to the presence of nonlinearity, nonlocality and hyperdimensions which one encounters frequently in multiscale modelling….(More)”.