Constantine E. Kontokosta: “This paper presents the conceptual framework and justification for a “Quantified Community” (QC) and a networked experimental environment of neighborhood labs. The QC is a fully instrumented urban neighborhood that uses an integrated, expandable, and participatory sensor network to support the measurement, integration, and analysis of neighborhood conditions, social interactions and behavior, and sustainability metrics to support public decision-making. Through a diverse range of sensor and automation technologies — combined with existing data generated through administrative records, surveys, social media, and mobile sensors — information on human, physical, and environmental elements can be processed in real-time to better understand the interaction and effects of the built environment on human well-being and outcomes. The goal is to create an “informatics overlay” that can be incorporated into future urban development and planning that supports the benchmarking and evaluation of neighborhood conditions, provides a test-bed for measuring the impact of new technologies and policies, and responds to the changing needs and preferences of the local community….(More)”
Nudge 2.0
Philipp Hacker: “This essay is both a review of the excellent book “Nudge and the Law. A European Perspective”, edited by Alberto Alemanno and Anne-Lise Sibony, and an assessment of the major themes and challenges that the behavioural analysis of law will and should face in the immediate future.
The book makes important and novel contributions in a range of topics, both on a theoretical and a substantial level. Regarding theoretical issues, four themes stand out: First, it highlights the differences between the EU and the US nudging environments. Second, it questions the reliance on expertise in rulemaking. Third, it unveils behavioural trade-offs that have too long gone unnoticed in behavioural law and economics. And fourth, it discusses the requirement of the transparency of nudges and the related concept of autonomy. Furthermore, the different authors discuss the impact of behavioural regulation on a number of substantial fields of law: health and lifestyle regulation, privacy law, and the disclosure paradigm in private law.
This paper aims to take some of the book’s insights one step further in order to point at crucial challenges – and opportunities – for the future of the behavioural analysis of law. In the last years, the movement has gained tremendously in breadth and depth. It is now time to make it scientifically even more rigorous, e.g. by openly embracing empirical uncertainty and by moving beyond the neo-classical/behavioural dichotomy. Simultaneously, the field ought to discursively readjust its normative compass. Finally and perhaps most strikingly, however, the power of big data holds the promise of taking behavioural interventions to an entirely new level. If these challenges can be overcome, this paper argues, the intersection between law and behavioural sciences will remain one of the most fruitful approaches to legal analysis in Europe and beyond….(More)”
The importance of human innovation in A.I. ethics
John C. Havens at Mashable: “….While welcoming the feedback that sensors, data and Artificial Intelligence provide, we’re at a critical inflection point. Demarcating the parameters between assistance and automation has never been more central to human well-being. But today, beauty is in the AI of the beholder. Desensitized to the value of personal data, we hemorrhage precious insights regarding our identity that define the moral nuances necessary to navigate algorithmic modernity.
If no values-based standards exist for Artificial Intelligence, then the biases of its manufacturers will define our universal code of human ethics. But this should not be their cross to bear alone. It’s time to stop vilifying the AI community and start defining in concert with their creations what the good life means surrounding our consciousness and code.
The intention of the ethics
“Begin as you mean to go forward.” Michael Stewart is founder, chairman & CEO of Lucid, an Artificial Intelligence company based in Austin that recently announced the formation of the industry’s first Ethics Advisory Panel (EAP). While Google claimed creation of a similar board when acquiring AI firm DeepMind in January 2014, no public realization of its efforts currently exist (as confirmed by a PR rep from Google for this piece). Lucid’s Panel, by comparison, has already begun functioning as a separate organization from the analytics side of the business and provides oversight for the company and its customers. “Our efforts,” Stewart says, “are guided by the principle that our ethics group is obsessed with making sure the impact of our technology is good.”
Kay Firth-Butterfield is chief officer of the EAP, and is charged with being on the vanguard of the ethical issues affecting the AI industry and society as a whole. Internally, the EAP provides the hub of ethical behavior for the company. Someone from Firth-Butterfield’s office even sits on all core product development teams. “Externally,” she notes, “we plan to apply Cyc intelligence (shorthand for ‘encyclopedia,’ Lucid’s AI causal reasoning platform) for research to demonstrate the benefits of AI and to advise Lucid’s leadership on key decisions, such as the recent signing of the LAWS letter and the end use of customer applications.”
Ensuring the impact of AI technology is positive doesn’t happen by default. But as Lucid is demonstrating, ethics doesn’t have to stymie innovation by dwelling solely in the realm of risk mitigation. Ethical processes aligning with a company’s core values can provide more deeply relevant products and increased public trust. Transparently including your customer’s values in these processes puts the person back into personalization….(Mashable)”
Data-Driven Innovation: Big Data for Growth and Well-Being
“A new OECD report on data-driven innovation finds that countries could be getting much more out of data analytics in terms of economic and social gains if governments did more to encourage investment in “Big Data” and promote data sharing and reuse.
The migration of economic and social activities to the Internet and the advent of The Internet of Things – along with dramatically lower costs of data collection, storage and processing and rising computing power – means that data-analytics is increasingly driving innovation and is potentially an important new source of growth.
The report suggest countries act to seize these benefits, by training more and better data scientists, reducing barriers to cross-border data flows, and encouraging investment in business processes to incorporate data analytics.
Few companies outside of the ICT sector are changing internal procedures to take advantage of data. For example, data gathered by companies’ marketing departments is not always used by other departments to drive decisions and innovation. And in particular, small and medium-sized companies face barriers to the adoption of data-related technologies such as cloud computing, partly because they have difficulty implementing organisational change due to limited resources, including the shortage of skilled personnel.
At the same time, governments will need to anticipate and address the disruptive effects of big data on the economy and overall well-being, as issues as broad as privacy, jobs, intellectual property rights, competition and taxation will be impacted. Read the Policy Brief…
TABLE OF CONTENTS
| Preface | |
| Foreword | |
| Executive summary | |
| The phenomenon of data-driven innovation | |
| Mapping the global data ecosystem and its points of control | |
| How data now drive innovation | |
| Drawing value from data as an infrastructure | |
| Building trust for data-driven innovation | |
| Skills and employment in a data-driven economy | |
| Promoting data-driven scientific research | |
| The evolution of health care in a data-rich environment | |
| Cities as hubs for data-driven innovation | |
| Governments leading by example with public sector data |
Health Data Governance: Privacy, Monitoring and Research
OECD publishing: “All countries are investing in health data, however; there are significant cross-country differences in data availability and use. Some countries stand out for their innovative practices enabling privacy-protective respectful data use; while others are falling behind with insufficient data and restrictions that limit access to and use of data, even by government itself. Countries that develop a data governance framework that enables privacy-protective data use will not only have the information needed to promote quality, efficiency and performance in their health systems, they will become a more attractive centre for medical research. After examining the current situation in OECD countries, a multi-disciplinary advisory panel of experts identified eight key data governance mechanisms to maximise benefits to patients and to societies from the collection, linkage and analysis of health data and to, at the same time, minimise risks to the privacy of patients and to the security of health data. These mechanisms include coordinated developming of high-value, privacy-protective health information systems; legislation that permits privacy-protective data use; open and transparent public communication ; accreditation or certification of health data processors; transparent and fair project approval processes; data de-identification and data security practices that meet legal requirements and public expectations without compromising data utility; and a process to continually assess and renew the data governance framework as new data and new risks emerge…”
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Big Data Privacy Scenarios
E. Bruce, K. Sollins, M. Vernon, and D. Weitzner at D-Space@MIT: “This paper is the first in a series on privacy in Big Data. As an outgrowth of a series of workshops on the topic, the Big Data Privacy Working Group undertook a study of a series of use scenarios to highlight the challenges to privacy that arise in the Big Data arena. This is a report on those scenarios. The deeper question explored by this exercise is what is distinctive about privacy in the context of Big Data. In addition, we discuss an initial list of issues for privacy that derive specifically from the nature of Big Data. These derive from observations across the real world scenarios and use cases explored in this project as well as wider reading and discussions:
* Scale: The sheer size of the datasets leads to challenges in creating, managing and applying privacy policies.
* Diversity: The increased likelihood of more and more diverse participants in Big Data collection, management, and use, leads to differing agendas and objectives. By nature, this is likely to lead to contradictory agendas and objectives.
* Integration: With increased data management technologies (e.g. cloud services, data lakes, and so forth), integration across datasets, with new and often surprising opportunities for cross-product inferences, will also come new information about individuals and their behaviors.
* Impact on secondary participants: Because many pieces of information are reflective of not only the targeted subject, but secondary, often unattended, participants, the inferences and resulting information will increasingly be reflective of other people, not originally considered as the subject of privacy concerns and approaches.
* Need for emergent policies for emergent information: As inferences over merged data sets occur, emergent information or understanding will occur.
Although each unique data set may have existing privacy policies and enforcement mechanisms, it is not clear that it is possible to develop the requisite and appropriate emerged privacy policies and appropriate enforcement of them automatically…(More)”
Open Data Charter
International Open Data Charter: “Open data sits at the heart of a global movement with the potential to generate significant social and economic benefits around the world. Through the articulation and adoption of common principles in support of open data, governments can work towards enabling more just, and prosperous societies.
In July 2013, G8 leaders signed the G8 Open Data Charter, which outlined a set of five core open data principles. Many nations and open government advocates welcomed the G8 Charter, but there was a general sense that the principles could be refined and improved to support broader global adoption of open data principles. In the months following, a number of multinational groups initiated their own activities to establish more inclusive and representative open data principles, including the Open Government Partnership’s (OGP) Open Data Working Group….
During 2015, open data experts from governments, multilateral organizations, civil society and private sector, worked together to develop an international Open Data Charter, with six principles for the release of data:
- Open by Default;
- Timely and Comprehensive;
- Accessible and Useable;
- Comparable and Interoperable;
- For Improved Governance and Citizen Engagement; and
- For Inclusive Development and Innovation….
Next Steps
- Promote adoption of the Charter.
- Continue to bring together a diverse, inclusive group of stakeholders to engage in the process of adoption of the international Open Data Charter.
- Develop a governance model for the ongoing management of the Charter, setting out the roles and responsibilities of a Charter partnership, and its working groups in the process of developing supporting resources, consultations, promotion, adoption, and oversight.
- Continue development of and consultation on supporting Charter guides, documents and tools, which will be brought together in a searchable, online Resource Centre. ..(More)”
What we can learn from the failure of Google Flu Trends
David Lazer and Ryan Kennedy at Wired: “….The issue of using big data for the common good is far more general than Google—which deserves credit, after all, for offering the occasional peek at their data. These records exist because of a compact between individual consumers and the corporation. The legalese of that compact is typically obscure (how many people carefully read terms and conditions?), but the essential bargain is that the individual gets some service, and the corporation gets some data.
What is left out that bargain is the public interest. Corporations and consumers are part of a broader society, and many of these big data archives offer insights that could benefit us all. As Eric Schmidt, CEO of Google, has said, “We must remember that technology remains a tool of humanity.” How can we, and corporate giants, then use these big data archives as a tool to serve humanity?
Google’s sequel to GFT, done right, could serve as a model for collaboration around big data for the public good. Google is making flu-related search data available to the CDC as well as select research groups. A key question going forward will be whether Google works with these groups to improve the methodology underlying GFT. Future versions should, for example, continually update the fit of the data to flu prevalence—otherwise, the value of the data stream will rapidly decay.
This is just an example, however, of the general challenge of how to build models of collaboration amongst industry, government, academics, and general do-gooders to use big data archives to produce insights for the public good. This came to the fore with the struggle (and delay) for finding a way to appropriately share mobile phone data in west Africa during the Ebola epidemic (mobile phone data are likely the best tool for understanding human—and thus Ebola—movement). Companies need to develop efforts to share data for the public good in a fashion that respects individual privacy.
There is not going to be a single solution to this issue, but for starters, we are pushing for a “big data” repository in Boston to allow holders of sensitive big data to share those collections with researchers while keeping them totally secure. The UN has its Global Pulse initiative, setting up collaborative data repositories around the world. Flowminder, based in Sweden, is a nonprofit dedicated to gathering mobile phone data that could help in response to disasters. But these are still small, incipient, and fragile efforts.
The question going forward now is how build on and strengthen these efforts, while still guarding the privacy of individuals and the proprietary interests of the holders of big data….(More)”
Datafication and empowerment: How the open data movement re-articulates notions of democracy, participation, and journalism
Stefan Baack at Big Data and Society: “This article shows how activists in the open data movement re-articulate notions of democracy, participation, and journalism by applying practices and values from open source culture to the creation and use of data. Focusing on the Open Knowledge Foundation Germany and drawing from a combination of interviews and content analysis, it argues that this process leads activists to develop new rationalities around datafication that can support the agency of datafied publics. Three modulations of open source are identified: First, by regarding data as a prerequisite for generating knowledge, activists transform the sharing of source code to include the sharing of raw data. Sharing raw data should break the interpretative monopoly of governments and would allow people to make their own interpretation of data about public issues. Second, activists connect this idea to an open and flexible form of representative democracy by applying the open source model of participation to political participation. Third, activists acknowledge that intermediaries are necessary to make raw data accessible to the public. This leads them to an interest in transforming journalism to become an intermediary in this sense. At the same time, they try to act as intermediaries themselves and develop civic technologies to put their ideas into practice. The article concludes with suggesting that the practices and ideas of open data activists are relevant because they illustrate the connection between datafication and open source culture and help to understand how datafication might support the agency of publics and actors outside big government and big business….(More)”
Harnessing the Data Revolution for Sustainable Development
US State Department Fact Sheet on “U.S. Government Commitments and Collaboration with the Global Partnership for Sustainable Development Data”: “On September 27, 2015, the member states of the United Nations agreed to a set of Sustainable Development Goals (Global Goals) that define a common agenda to achieve inclusive growth, end poverty, and protect the environment by 2030. The Global Goals build on tremendous development gains made over the past decade, particularly in low- and middle-income countries, and set actionable steps with measureable indicators to drive progress. The availability and use of high quality data is essential to measuring and achieving the Global Goals. By harnessing the power of technology, mobilizing new and open data sources, and partnering across sectors, we will achieve these goals faster and make their progress more transparent.
Harnessing the data revolution is a critical enabler of the global goals—not only to monitor progress, but also to inclusively engage stakeholders at all levels – local, regional, national, global—to advance evidence-based policies and programs to reach those who need it most. Data can show us where girls are at greatest risk of violence so we can better prevent it; where forests are being destroyed in real-time so we can protect them; and where HIV/AIDS is enduring so we can focus our efforts and finish the fight. Data can catalyze private investment; build modern and inclusive economies; and support transparent and effective investment of resources for social good…..
The Global Partnership for Sustainable Development Data (Global Data Partnership), launched on the sidelines of the 70th United Nations General Assembly, is mobilizing a range of data producers and users—including governments, companies, civil society, data scientists, and international organizations—to harness the data revolution to achieve and measure the Global Goals. Working together, signatories to the Global Data Partnership will address the barriers to accessing and using development data, delivering outcomes that no single stakeholder can achieve working alone….The United States, through the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR), is joining a consortium of funders to seed this initiative. The U.S. Government has many initiatives that are harnessing the data revolution for impact domestically and internationally. Highlights of our international efforts are found below:
Health and Gender
• Country Data Collaboratives for Local Impact – PEPFAR and the Millennium Challenge Corporation(MCC) are partnering to invest $21.8 million in Country Data Collaboratives for Local Impact in sub-Saharan Africa that will use data on HIV/AIDS, global health, gender equality, and economic growth to improve programs and policies. Initially, the Country Data Collaboratives will align with and support the objectives of DREAMS, a PEPFAR, Bill & Melinda Gates Foundation, and Girl Effect partnership to reduce new HIV infections among adolescent girls and young women in high-burden areas.
• Measurement and Accountability for Results in Health (MA4Health) Collaborative – USAID is partnering with the World Health Organization, the World Bank, and over 20 other agencies, countries, and civil society organizations to establish the MA4Health Collaborative, a multi-stakeholder partnership focused on reducing fragmentation and better aligning support to country health-system performance and accountability. The Collaborative will provide a vehicle to strengthen country-led health information platforms and accountability systems by improving data and increasing capacity for better decision-making; facilitating greater technical collaboration and joint investments; and developing international standards and tools for better information and accountability. In September 2015, partners agreed to a set of common strategic and operational principles, including a strong focus on 3–4 pathfinder countries where all partners will initially come together to support country-led monitoring and accountability platforms. Global actions will focus on promoting open data, establishing common norms and standards, and monitoring progress on data and accountability for the Global Goals. A more detailed operational plan will be developed through the end of the year, and implementation will start on January 1, 2016.
• Data2X: Closing the Gender Gap – Data2X is a platform for partners to work together to identify innovative sources of data, including “big data,” that can provide an evidence base to guide development policy and investment on gender data. As part of its commitment to Data2X—an initiative of the United Nations Foundation, Hewlett Foundation, Clinton Foundation, and Bill & Melinda Gates Foundation—PEPFAR and the Millennium Challenge Corporation (MCC) are working with partners to sponsor an open data challenge to incentivize the use of gender data to improve gender policy and practice….(More)”
See also: Data matters: the Global Partnership for Sustainable Development Data. Speech by UK International Development Secretary Justine Greening at the launch of the Global Partnership for Sustainable Development Data.