P.B.Anand and JulioNavío-Marco in Special Issues of Telecommunications Policy: “This editorial introduction to this special issue provides an overview and a conceptual framework of governance and economics of smart cities. We begin with a discussion of the background to smart cities and then it focuses on the key challenges for consideration in smart city economics. Here it is argued that there are four dimensions to smart city economics: the first is regarding the scale of global market for smart cities; the second issue concerns data to be used for smart city projects; the third concerns market competition and structure and the fourth concerns the impact on local economy. Likewise, smart city governance framework has to be considered a layered and multi-level concept focusing on issues of transparency and accountability to the citizens….(More)”.
How data helped visualize the family separation crisis
Chava Gourarie at StoryBench: “Early this summer, at the height of the family separation crisis – where children were being forcibly separated from their parents at our nation’s border – a team of scholars pooled their skills to address the issue. The group of researchers – from a variety of humanities departments at multiple universities – spent a week of non-stop work mapping the immigration detention network that spans the United States. They named the project “Torn Apart/Separados” and published it online, to support the efforts of locating and reuniting the separated children with their parents.
The project utilizes the methods of the digital humanities, an emerging discipline that applies computational tools to fields within the humanities, like literature and history. It was led by members of Columbia University’s Group for Experimental Methods in the Humanities, which had previously used methods such as rapid deployment to responded to natural disasters.
The group has since expanded the project, publishing a second volume that focuses on the $5 billion immigration industry, based largely on public data about companies that contract with the Immigration and Customs Enforcement agency. The visualizations highlight the astounding growth in investment of ICE infrastructure (from $475 million 2014 to $5.1 billion in 2018), as well as who benefits from these contracts, and how the money is spent.
Storybench spoke with Columbia University’s Alex Gil, who worked on both phases of the project, about the process of building “Torn Apart/Separados,” about the design and messaging choices that were made and the ways in which methods of the digital humanities can cross pollinate with those of journalism…(More)”.
How pro-trust initiatives are taking over the Internet
Sara Fisher at Axios: “Dozens of new initiatives have launched over the past few years to address fake news and the erosion of faith in the media, creating a measurement problem of its own.
Why it matters: So many new efforts are launching simultaneously to solve the same problem that it’s become difficult to track which ones do what and which ones are partnering with each other….
Governing artificial intelligence: ethical, legal, and technical opportunities and challenges
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)”.
Here’s What the USMCA Does for Data Innovation
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)”.
Governing Artificial Intelligence: Upholding Human Rights & Dignity
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)”.
A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI
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)”.
Study: Crowdsourced Hospital Ratings May Not Be Fair
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)”.
Statistics and data science degrees: Overhyped or the real deal?
“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)”.
Human Rights in the Big Data World
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)”.