New paper by Praneetha Vissapragada and Naomi Joswiak: “The Open Government Costing initiative, seeded with funding from the World Bank, was undertaken to develop a practical and actionable approach to pinpointing the full economic costs of various open government programs. The methodology developed through this initiative represents an important step towards conducting more sophisticated cost-benefit analyses – and ultimately understanding the true value – of open government reforms intended to increase citizen engagement, promote transparency and accountability, and combat corruption, insights that have been sorely lacking in the open government community to date. The Open Government Costing Framework and Methods section (Section 2 of this report) outlines the critical components needed to conduct cost analysis of open government programs, with the ultimate objective of putting a price tag on key open government reform programs in various countries at a particular point in time. This framework introduces a costing process that employs six essential steps for conducting a cost study, including (1) defining the scope of the program, (2) identifying types of costs to assess, (3) developing a framework for costing, (4) identifying key components, (5) conducting data collection and (6) conducting data analysis. While the costing methods are built on related approaches used for analysis in other sectors such as health and nutrition, this framework and methodology was specifically adapted for open government programs and thus addresses the unique challenges associated with these types of initiatives. Using the methods outlined in this document, we conducted a cost analysis of two case studies: (1) ProZorro, an e-procurement program in Ukraine; and (2) Sierra Leone’s Open Data Program….(More)”
The Supreme Court Is Allergic To Math
Oliver Roeder at FiveThirtyEight: “The Supreme Court does not compute. Or at least some of its members would rather not. The justices, the most powerful jurists in the land, seem to have a reluctance — even an allergy — to taking math and statistics seriously.
For decades, the court has struggled with quantitative evidence of all kinds in a wide variety of cases. Sometimes justices ignore this evidence. Sometimes they misinterpret it. And sometimes they cast it aside in order to hold on to more traditional legal arguments. (And, yes, sometimes they also listen to the numbers.) Yet the world itself is becoming more computationally driven, and some of those computations will need to be adjudicated before long. Some major artificial intelligence case will likely come across the court’s desk in the next decade, for example. By voicing an unwillingness to engage with data-driven empiricism, justices — and thus the court — are at risk of making decisions without fully grappling with the evidence.
This problem was on full display earlier this month, when the Supreme Court heard arguments in Gill v. Whitford, a case that will determine the future of partisan gerrymandering — and the contours of American democracy along with it. As my colleague Galen Druke has reported, the case hinges on math: Is there a way to measure a map’s partisan bias and to create a standard for when a gerrymandered map infringes on voters’ rights?…(More)”.
Political Ideology and Municipal Size as Incentives for the Implementation and Governance Models of Web 2.0 in Providing Public Services
Manuel Pedro Rodríguez Bolívar and Laura Alcaide Muñoz in the International Journal of Public Administration in the Digital Age: “The growing participation in social networking sites is altering the nature of social relations and changing the nature of political and public dialogue. This paper aims to contribute to the current debate on Web 2.0 technologies and their implications for local governance, through the identification of the perceptions of policy makers in local governments on the use of Web 2.0 in providing public services (reasons, advantages and risks) and on the change of the roles that these technologies could provoke in interactions between local governments and their stakeholders (governance models). This paper also analyzes whether the municipal size is a main factor that could influence on the policy makers’ perceptions regarding these main topics. Findings suggest that policy makers are willing to implement Web 2.0 technologies in providing public services, but preferably under the Bureaucratic model framework, thus retaining a leading role in this implementation. The municipal size is a factor that could influence on policy makers’ perceptions….(More)”.
Fraud Data Analytics Tools and Techniques in Big Data Era
Paper by Sara Makki et al: “Fraudulent activities (e.g., suspicious credit card transaction, financial reporting fraud, and money laundering) are critical concerns to various entities including bank, insurance companies, and public service organizations. Typically, these activities lead to detrimental effects on the victims such as a financial loss. Over the years, fraud analysis techniques underwent a rigorous development. However, lately, the advent of Big data led to vigorous advancement of these techniques since Big Data resulted in extensive opportunities to combat financial frauds. Given that the massive amount of data that investigators need to sift through, massive volumes of data integrated from multiple heterogeneous sources (e.g., social media, blogs) to find fraudulent patterns is emerging as a feasible approach….(More)”.
Crowdsourced Morality Could Determine the Ethics of Artificial Intelligence
Dom Galeon in Futurism: “As artificial intelligence (AI) development progresses, experts have begun considering how best to give an AI system an ethical or moral backbone. A popular idea is to teach AI to behave ethically by learning from decisions made by the average person.
To test this assumption, researchers from MIT created the Moral Machine. Visitors to the website were asked to make choices regarding what an autonomous vehicle should do when faced with rather gruesome scenarios. For example, if a driverless car was being forced toward pedestrians, should it run over three adults to spare two children? Save a pregnant woman at the expense of an elderly man?
The Moral Machine was able to collect a huge swath of this data from random people, so Ariel Procaccia from Carnegie Mellon University’s computer science department decided to put that data to work.
In a new study published online, he and Iyad Rahwan — one of the researchers behind the Moral Machine — taught an AI using the Moral Machine’s dataset. Then, they asked the system to predict how humans would want a self-driving car to react in similar but previously untested scenarios….
This idea of having to choose between two morally problematic outcomes isn’t new. Ethicists even have a name for it: the double-effect. However, having to apply the concept to an artificially intelligent system is something humankind has never had to do before, and numerous experts have shared their opinions on how best to go about it.
OpenAI co-chairman Elon Musk believes that creating an ethical AI is a matter of coming up with clear guidelines or policies to govern development, and governments and institutions are slowly heeding Musk’s call. Germany, for example, crafted the world’s first ethical guidelines for self-driving cars. Meanwhile, Google parent company Alphabet’s AI DeepMind now has an ethics and society unit.
Other experts, including a team of researchers from Duke University, think that the best way to move forward is to create a “general framework” that describes how AI will make ethical decisions….(More)”.
TfL’s free open data boosts London’s economy
Press Release by Transport for London: “Research by Deloitte shows that the release of open data by TfL is generating annual economic benefits and savings of up to £130m a year…
TfL has worked with a wide range of professional and amateur developers, ranging from start-ups to global innovators, to deliver new products in the form that customers want. This has led to more than 600 apps now being powered specifically using TfL’s open data feeds, used by 42 per cent of Londoners.
The report found that TfL’s data provides the following benefits:
- Saved time for passengers. TfL’s open data allows customers to plan journeys more accurately using apps with real-time information and advice on how to adjust their routes. This provides greater certainty on when the next bus/Tube will arrive and saves time – estimated at between £70m and £90m per year.
- Better information to plan journeys, travel more easily and take more journeys. Customers can use apps to better plan journeys, enabling them to use TfL services more regularly and access other services. Conservatively, the value of these journeys is estimated at up to £20m per year.
- Creating commercial opportunities for third party developers. A wide range of companies now use TfL’s open data commercially to help generate revenue, many of whom are based in London. Having free and up-to-date access to this data increases the ‘Gross Value Add’ (analogous to GDP) that these companies contribute to the London economy, both directly and across the supply chain and wider economy, of between £12m and £15m per year.
- Leveraging value and savings from partnerships with major customer facing technology platform owners. TfL receives back significant data on areas it does not itself collect data (e.g. crowdsourced traffic data). This allows TfL to get an even better understanding of journeys in London and improve its operations….(More).
How to Use Social Media to Better Engage People Affected by Crises
Guide by the International Red Cross Federation: “Together with ICRC, and with the support of OCHA, we have published a brief guide on how to use social media to better engage people affected by crisis. The guide is geared towards staff in humanitarian organisations who are responsible for official social media channels.
In the past few years, the role of social media and digital technologies in times of disasters and crises has grown exponentially. During disasters like the 2015 Nepal earthquake, for instance, Facebook and Twitter were crucial components of the humanitarian response, allowing mostly local, but also international actors involved in relief efforts, to disseminate lifesaving messages. However, the use of social media by humanitarian organizations to engage and communicate with (not about) affected people is, to date, still vastly untapped and largely under researched and document¬ed in terms of the provision of practical guidance, both thematically and technically, good practices and lessons learned.
This brief guide, trying to address this gap, provides advice on how to use social media effectively to engage with, and be accountable to, affected people through practical tips and case studies from within the Movement and the wider sector…(Guide)”.
Using Facebook data as a real-time census
Phys.org: “Determining how many people live in Seattle, perhaps of a certain age, perhaps from a specific country, is the sort of question that finds its answer in the census, a massive data dump for places across the country.
But just how fresh is that data? After all, the census is updated once a decade, and the U.S. Census Bureau’s smaller but more detailed American Community Survey, annually. There’s also a delay between when data are collected and when they are published. (The release of data for 2016 started gradually in September 2017.)
Enter Facebook, which, with some caveats, can serve as an even more current source of information, especially about migrants. That’s the conclusion of a study led by Emilio Zagheni, associate professor of sociology at the University of Washington, published Oct. 11 in Population and Development Review. The study is believed to be the first to demonstrate how present-day migration statistics can be obtained by compiling the same data that advertisers use to target their audience on Facebook, and by combining that source with information from the Census Bureau.
Migration indicates a variety of political and economic trends and is a major driver of population change, Zagheni said. As researchers further explore the increasing number of databases produced for advertisers, Zagheni argues, social scientists could leverage Facebook, LinkedIn and Twitter more often to glean information on geography, mobility, behavior and employment. And while there are some limits to the data – each platform is a self-selected, self-reporting segment of the population – the number of migrants according to Facebook could supplement the official numbers logged by the U.S. Census Bureau, Zagheni said….(Full Paper).
Tech’s fight for the upper hand on open data
Rana Foroohar at the Financial Times: “One thing that’s becoming very clear to me as I report on the digital economy is that a rethink of the legal framework in which business has been conducted for many decades is going to be required. Many of the key laws that govern digital commerce (which, increasingly, is most commerce) were crafted in the 1980s or 1990s, when the internet was an entirely different place. Consider, for example, the US Computer Fraud and Abuse Act.
This 1986 law made it a federal crime to engage in “unauthorised access” to a computer connected to the internet. It was designed to prevent hackers from breaking into government or corporate systems. …While few hackers seem to have been deterred by it, the law is being used in turf battles between companies looking to monetise the most valuable commodity on the planet — your personal data. Case in point: LinkedIn vs HiQ, which may well become a groundbreaker in Silicon Valley.
LinkedIn is the dominant professional networking platform, a Facebook for corporate types. HiQ is a “data-scraping” company, one that accesses publicly available data from LinkedIn profiles and then mixes it up in its own quantitative black box to create two products — Keeper, which tells employers which of their employees are at greatest risk of being recruited away, and Skill Mapper, which provides a summary of the skills possessed by individual workers. LinkedIn allowed HiQ to do this for five years, before developing a very similar product to Skill Mapper, at which point LinkedIn sent the company a “cease and desist” letter, and threatened to invoke the CFAA if HiQ did not stop tapping its user data.
..Meanwhile, a case that might have been significant mainly to digital insiders is being given a huge publicity boost by Harvard professor Laurence Tribe, the country’s pre-eminent constitutional law scholar. He has joined the HiQ defence team because, as he told me, he believes the case is “tremendously important”, not only in terms of setting competitive rules for the digital economy, but in the realm of free speech. According to Prof Tribe, if you accept that the internet is the new town square, and “data is a central type of capital”, then it must be freely available to everyone — and LinkedIn, as a private company, cannot suddenly decide that publicly accessible, Google-searchable data is their private property….(More)”.
How Copyright Law Can Fix Artificial Intelligence’s Implicit Bias Problem
Paper by Amanda Levendowski: “As the use of artificial intelligence (AI) continues to spread, we have seen an increase in examples of AI systems reflecting or exacerbating societal bias, from racist facial recognition to sexist natural language processing. These biases threaten to overshadow AI’s technological gains and potential benefits. While legal and computer science scholars have analyzed many sources of bias, including the unexamined assumptions of its often-homogenous creators, flawed algorithms, and incomplete datasets, the role of the law itself has been largely ignored. Yet just as code and culture play significant roles in how AI agents learn about and act in the world, so too do the laws that govern them. This Article is the first to examine perhaps the most powerful law impacting AI bias: copyright.
Artificial intelligence often learns to “think” by reading, viewing, and listening to copies of human works. This Article first explores the problem of bias through the lens of copyright doctrine, looking at how the law’s exclusion of access to certain copyrighted source materials may create or promote biased AI systems. Copyright law limits bias mitigation techniques, such as testing AI through reverse engineering, algorithmic accountability processes, and competing to convert customers. The rules of copyright law also privilege access to certain works over others, encouraging AI creators to use easily available, legally low-risk sources of data for teaching AI, even when those data are demonstrably biased. Second, it examines how a different part of copyright law — the fair use doctrine — has traditionally been used to address similar concerns in other technological fields, and asks whether it is equally capable of addressing them in the field of AI bias. The Article ultimately concludes that it is, in large part because the normative values embedded within traditional fair use ultimately align with the goals of mitigating AI bias and, quite literally, creating fairer AI systems….(More)”.