Stefaan Verhulst
Introduction to the Special Issue of the Philosophical Transactions of the Royal Society by Sandra Wachter, Brent Mittelstadt, Luciano Floridi and Corinne Cath: “Artificial intelligence (AI) increasingly permeates every aspect of our society, from the critical, like urban infrastructure, law enforcement, banking, healthcare and humanitarian aid, to the mundane like dating. AI, including embodied AI in robotics and techniques like machine learning, can improve economic, social welfare and the exercise of human rights. Owing to the proliferation of AI in high-risk areas, the pressure is mounting to design and govern AI to be accountable, fair and transparent. How can this be achieved and through which frameworks? This is one of the central questions addressed in this special issue, in which eight authors present in-depth analyses of the ethical, legal-regulatory and technical challenges posed by developing governance regimes for AI systems. It also gives a brief overview of recent developments in AI governance, how much of the agenda for defining AI regulation, ethical frameworks and technical approaches is set, as well as providing some concrete suggestions to further the debate on AI governance…(More)”.
Joshua New at the Center for Data Innovation: “…the Trump administration announced the United States-Mexico-Canada Agreement (USMCA), the trade deal it intends to replace NAFTA with. The parties—Canada, Mexico, and the United States—still have to adopt the deal, and if they do, they will enjoy several welcome provisions that can give a boost to data-driven innovation in all three countries.
First, USMCA is the first trade agreement in the world to promote the publication of open government data. Article 19.18 of the agreement officially recognizes that “facilitating public access to and use of government information fosters economic and social development, competitiveness, and innovation.” Though the deal does not require parties to publish open government data, to the extent they choose to publish this data, it directs them to adhere to best practices for open data, including ensuring it is in open, machine-readable formats. Additionally, the deal directs parties to try to cooperate and identify ways they can expand access to and the use of government data, particularly for the purposes of creating economic opportunity for small and medium-sized businesses. While this is a welcome provision, the United States still needs legislation to ensure that publishing open data becomes an official responsibility of federal government agencies.
Second, Article 19.11 of USMCA prevents parties from restricting “the cross-border transfer of information, including personal information, by electronic means if this activity is for the conduct of the business of a covered person.” Additionally, Article 19.12 prevents parties from requiring people or firms “to use or locate computing facilities in that Party’s territory as a condition for conducting business in that territory.” In effect, these provisions prevent parties from enacting protectionist data localization requirements that inhibit the flow of data across borders. This is important because many countries have disingenuously argued for data localization requirements on the grounds that it protects their citizens from privacy or security harms, despite the location of data having no bearing on either privacy or security, to prop up their domestic data-driven industries….(More)”.
Paul Raeburn at Scientific American: “Researchers are becoming so adept at mining information from genealogical, medical and police genetic databases that it is becoming difficult to protect anyone’s privacy—even those who have never submitted their DNA for analysis.
In one of two separate studies published October 11, researchers report that by testing the 1.28 million samples contained in a consumer gene database, they could match 60 percent of the DNA of the 140 million Americans of European descent to a third cousin or closer relative. That figure, they say in the study published in Science, will soon rise to nearly 100 percent as the number of samples rises in such consumer databases as AncestryDNA and 23andMe.
In the second study, in the journal Cell, a different research group show that police databases—once thought to be made of meaningless DNA useful only for matching suspects with crime scene samples—can be cross-linked with genetic databases to connect individuals to their genetic information. “Both of these papers show you how deeply you can reach into a family and a population,” says Erin Murphy, a professor of law at New York University School of Law. Consumers who decide to share DNA with a consumer database are providing information on their parents, children, third cousins they don’t know about—and even a trace that could point to children who don’t exist yet, she says….(More)”.
Report by Mark Latonero that “…shows how human rights can serve as a “North Star” to guide the development and governance of artificial intelligence.
The report draws the connections between AI and human rights; reframes recent AI-related controversies through a human rights lens; and reviews current stakeholder efforts at the intersection of AI and human rights.
This report is intended for stakeholders–such as technology companies, governments, intergovernmental organizations, civil society groups, academia, and the United Nations (UN) system–looking to incorporate human rights into social and organizational contexts related to the development and governance of AI….(More)”.
Paper by Sandra Wachter and Brent Mittelstadt: “Big Data analytics and artificial intelligence (AI) draw non-intuitive and unverifiable inferences and predictions about the behaviors, preferences, and private lives of individuals. These inferences draw on highly diverse and feature-rich data of unpredictable value, and create new opportunities for discriminatory, biased, and invasive decision-making. Concerns about algorithmic accountability are often actually concerns about the way in which these technologies draw privacy invasive and non-verifiable inferences about us that we cannot predict, understand, or refute.
Data protection law is meant to protect people’s privacy, identity, reputation, and autonomy, but is currently failing to protect data subjects from the novel risks of inferential analytics. The broad concept of personal datain Europe could be interpreted to include inferences, predictions, and assumptions that refer to or impact on an individual. If seen as personal data, individuals are granted numerous rights under data protection law. However, the legal status of inferences is heavily disputed in legal scholarship, and marked by inconsistencies and contradictions within and between the views of the Article 29 Working Party and the European Court of Justice.
As we show in this paper, individuals are granted little control and oversight over how their personal data is used to draw inferences about them. Compared to other types of personal data, inferences are effectively ‘economy class’ personal data in the General Data Protection Regulation (GDPR). Data subjects’ rights to know about (Art 13-15), rectify (Art 16), delete (Art 17), object to (Art 21), or port (Art 20) personal data are significantly curtailed when it comes to inferences, often requiring a greater balance with controller’s interests (e.g. trade secrets, intellectual property) than would otherwise be the case. Similarly, the GDPR provides insufficient protection against sensitive inferences (Art 9) or remedies to challenge inferences or important decisions based on them (Art 22(3))….
In this paper we argue that a new data protection right, the ‘right to reasonable inferences’, is needed to help close the accountability gap currently posed ‘high risk inferences’ , meaning inferences that are privacy invasive or reputation damaging and have low verifiability in the sense of being predictive or opinion-based. In cases where algorithms draw ‘high risk inferences’ about individuals, this right would require ex-ante justification to be given by the data controller to establish whether an inference is reasonable. This disclosure would address (1) why certain data is a relevant basis to draw inferences; (2) why these inferences are relevant for the chosen processing purpose or type of automated decision; and (3) whether the data and methods used to draw the inferences are accurate and statistically reliable. The ex-ante justification is bolstered by an additional ex-post mechanism enabling unreasonable inferences to be challenged. A right to reasonable inferences must, however, be reconciled with EU jurisprudence and counterbalanced with IP and trade secrets law as well as freedom of expression and Article 16 of the EU Charter of Fundamental Rights: the freedom to conduct a business….(More)”.
Samantha Horton at WFYI: “Though many websites offer non-scientific ratings on a number of services, two Indiana University scientists say judging hospitals that way likely isn’t fair.
Their recently-released study compares the federal government’s Hospital Compare and crowdsourced sites such as Facebook, Yelp and Google. The research finds it’s difficult for people to accurately understand everything a hospital does, and that leads to biased ratings.
Patient experiences with food, amenities and bedside manner often aligns with federal government ratings. But IU professor Victoria Perez says judging quality of care and safety is much more nuanced and people often get it wrong.
“About 20 percent of the hospitals rated best within a local market on social media were rated worst in that market by Hospital Compare in terms of patient health outcomes,” she says.
For the crowdsourced ratings to be more useful, Perez says people would have to know how to cross-reference them with a more reliable data source, such as Hospital Compare. But even that site can be challenging to navigate depending on what the consumer is looking for.
“If you have a condition-specific concern and you can see the clinical measure for a hospital that may be helpful,” says Perez. “But if your particular medical concern is not listed there, it might be hard to extrapolate from the ones that are listed or to know which ones you should be looking at.”
She says consumers would need more information about patient outcomes and other quality metrics to be able to reliably crowdsource a hospital on a site such as Google…(More)”.
“Data science” is hot right now. The number of undergraduate degrees in statistics has tripled in the past decade, and as a statistics professor, I can tell you that it isn’t because freshmen love statistics.
Way back in 2009, economist Hal Varian of Google dubbed statistician the “next sexy job.” Since then, statistician, data scientist and actuary have topped various “best jobs” lists. Not to mention the enthusiastic press coverage of industry applications: Machine learning! Big data! AI! Deep learning!
But is it good advice? I’m going to voice an unpopular opinion for the sake of starting a conversation. Stats is indeed useful, but not in the way that the popular media – and all those online data science degree programs – seem to suggest….
While all the press tends to go to the sensationalist applications – computers that watch cat videos, anyone? – the data science boom reflects a broad increase in demand for data literacy, as a baseline requirement for modern jobs.
The “big data era” doesn’t just mean large amounts of data; it also means increased ease and ability to collect data of all types, in all walks of life. Although the big five tech companies – Google, Apple, Amazon, Facebook and Microsoft – represent about 10 percent of the U.S. market cap and dominate the public imagination, they employ only one-half of one percent of all employees.
Therefore, to be a true revolution, data science will need to infiltrate nontech industries. And it is. The U.S. has seen its impact on political campaigns. I myself have consulted in the medical devices sector. A few years back, Walmart held a data analysis competition as a recruiting tool. The need for people that can dig into the data and parse it is everywhere.
In a speech at the National Academy of Sciences in 2015, Steven “Freakonomics” Levitt related his insights about the need for data-savvy workers, based on his experience as a sought-after consultant in fields ranging from the airline industry to fast food….(More)”.
Interview by Art Kleiner: “In 2015, Robert Wachter published The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age, a skeptical account of digitization in hospitals. Despite the promise offered by the digital transformation of healthcare, electronic health records had not delivered better care and greater efficiency. The cumbersome design, legacy procedures, and resistance from staff were frustrating everyone — administrators, nurses, consultants, and patients. Costs continued to rise, and preventable medical mistakes were not spotted. One patient at Wachter’s own hospital, one of the nation’s finest, was given 39 times the correct dose of antibiotics by an automated system that nobody questioned. The teenager survived, but it was clear that there needed to be a new approach to the management and use of data.
Wachter has for decades considered the delivery of healthcare through a lens focused on patient safety and quality. In 1996, he coauthored a paper in the New England Journal of Medicine that coined the term hospitalist in describing and promoting a new way of managing patients in hospitals: having one doctor — the hospitalist — “own” the patient journey from admission to discharge. The primary goal was to improve outcomes and save lives. Wachter argued it would also reduce costs and increase efficiency, making the business case for better healthcare. And he was right. Today there are more than 50,000 hospitalists, and it took just two years from the article’s publication to have the first data proving his point. In 2016, Wachter was named chair of the Department of Medicine at the University of California, San Francisco (UCSF), where he has worked since 1990.
Today, Wachter is, to paraphrase the title of a recent talk, less grumpy than he used to be about health tech. The hope part of his book’s title has materialized in some areas faster than he predicted. AI’s advances in imaging are already helping the detection of cancers become more accurate. As data collection has become better systematized, big technology firms such as Google, Amazon, and Apple are entering (in Google’s case, reentering) the field and having more success focusing their problem-solving skills on healthcare issues. In his San Francisco office, Wachter sat down with strategy+businessto discuss why the healthcare system may finally be about to change….
Systems for Fresh Thinking
S+B: The changes you appreciate seem to have less to do with technological design and more to do with people getting used to the new systems, building their own variations, and making them work.
WACHTER: The original electronic health record was just a platform play to get the data in digital form. It didn’t do anything particularly helpful in terms of helping the physicians make better decisions or helping to connect one kind of doctor with another kind of doctor. But it was a start.
I remember that when we were starting to develop our electronic health record at UCSF, 12 or 13 years ago, I hired a physician who is now in charge of our health computer system. I said to him, “We don’t have our electronic health record in yet, but I’m pretty sure we will in seven or eight years. What will your job be when that’s done?” I actually thought once the system was fully implemented, we’d be done with the need to innovate and evolve in health IT. That, of course, was asinine.
S+B: That’s like saying to an auto mechanic, “What will your job be when we have automatic transmissions?”
WACHTER: Right, but even more so, because many of us saw electronic health records as the be-all and end-all of digitally facilitated medicine. But putting in the electronic health record is just step one of 10. Then you need to start connecting all the pieces, and then you add analytics that make sense of the data and make predictions. Then you build tools and apps to fit into the workflow and change the way you work.
One of my biggest epiphanies was this: When you digitize, in any industry, nobody is clever enough to actually change anything. All they know how to do is digitize the old practice. You only start seeing real progress when smart people come in, begin using the new system, and say, “Why the hell do we do it that way?” And then you start thinking freshly about the work. That’s when you have a chance to reimagine the work in a digital environment…(More)”.
Article by Jacquelyn Kovarik at NACA: “…Last year, during a high-level event of the General Assembly, a coalition of states along with the European Union and the International Labour Organization announced a new technology for monitoring the rights of Indigenous people. The proposal was a web application called “Indigenous Navigator,” designed to enable native peoples to monitor their rights from within their communities. The project is extremely seductive: why rely on the General Assembly to represent Indigenous peoples when they can represent themselves—remotely and via cutting-edge data-collecting technology? Could an app be the answer to over a decade of failed attempts to include Indigenous peoples in the international body?
The web application, which officially launched in 11 countries early this year, is comprised of four “community-based monitoring tools” that are designed to bridge the gap between Indigenous rights implementation and the United Nations goals. The toolbox, which is available open-access to anyone with internet, consists of: a set of two impressively comprehensive surveys designed to collect data on Indigenous rights at a community and national level; a comparative matrix that illustrates the links between the UN Declaration on Indigenous Rights and the UN development goals; an index designed to quickly compare Indigenous realities across communities, regions, or states; and a set of indicators designed to measure the realization of Indigenous rights in communities or states. The surveys are divided by sections based on the UN Declaration on the Rights of Indigenous Peoples, and include such categories as cultural integrity, land rights, access to justice, health, cross-border contacts, freedom of expression and media, education, and economic and social development. The surveys also include tips for methodological administration. For example, in questions about poverty rates in the community, a tip provided reads: “Most people/communities have their own criteria for defining who are poor and who are not poor. Here you are asked to estimate how many of the men of your people/community are considered poor, according to your own criteria for poverty.” It then suggests that it may be helpful to first discuss what are the perceived characteristics of a poor person within the community, before answering the question….(More)”.
Paper by Francis Kuriakose and Deepa Iyer: “Ethical approach to human rights conceives and evaluates law through the underlying value concerns. This paper examines human rights after the introduction of big data using an ethical approach to rights. First, the central value concerns such as equity, equality, sustainability and security are derived from the history of digital technological revolution. Then, the properties and characteristics of big data are analyzed to understand emerging value concerns such as accountability, transparency, tracability, explainability and disprovability.
Using these value points, this paper argues that big data calls for two types of evaluations regarding human rights. The first is the reassessment of existing human rights in the digital sphere predominantly through right to equality and right to work. The second is the conceptualization of new digital rights such as right to privacy and right against propensity-based discrimination. The paper concludes that as we increasingly share the world with intelligence systems, these new values expand and modify the existing human rights paradigm….(More)”.