US Federal Data Strategy: “For the purposes of the Federal Data Strategy, a “Use Case” is a data practice or method that leverages data to support an articulable federal agency mission or public interest outcome. The Federal Data Strategy sought use cases from the public that solve problems or demonstrate solutions that can help inform the four strategy areas: Enterprise Data Governance; Use, Access, and Augmentation; Decision-making and Accountability; and Commercialization, Innovation, and Public Use. The Federal Data Strategy team was in part informed by these submissions, which are posted below…..(More)”.
We Read 150 Privacy Policies. They Were an Incomprehensible Disaster.
Kevin Litman-Navarro at the New York Times: “….I analyzed the length and readability of privacy policies from nearly 150 popular websites and apps. Facebook’s privacy policy, for example, takes around 18 minutes to read in its entirety – slightly above average for the policies I tested….
Despite efforts like the General Data Protection Regulation to make policies more accessible, there seems to be an intractable tradeoff between a policy’s readability and length. Even policies that are shorter and easier to read can be impenetrable, given the amount of background knowledge required to understand how things like cookies and IP addresses play a role in data collection….
So what might a useful privacy policy look like?
Consumers don’t need a technical understanding of data collection processes in order to protect their personal information. Instead of explaining the excruciatingly complicated inner workings of the data marketplace, privacy policies should help people decide how they want to present themselves online. We tend to go on the internet privately – on our phones or at home – which gives the impression that our activities are also private. But, often, we’re more visible than ever.
A good privacy policy would help users understand how exposed they are: Something as simple as a list of companies that might purchase and use your personal information could go a long way towards setting a new bar for privacy-conscious behavior. For example, if you know that your weather app is constantly tracking your whereabouts and selling your location data as marketing research, you might want to turn off your location services entirely, or find a new app.
Until we reshape privacy policies to meet our needs — or we find a suitable replacement — it’s probably best to act with one rule in mind. To be clear and concise: Someone’s always watching….(More)”.
Data & Policy: A new venue to study and explore policy–data interaction
Opening editorial by Stefaan G. Verhulst, Zeynep Engin and Jon Crowcroft: “…Policy–data interactions or governance initiatives that use data have been the exception rather than the norm, isolated prototypes and trials rather than an indication of real, systemic change. There are various reasons for the generally slow uptake of data in policymaking, and several factors will have to change if the situation is to improve. ….
- Despite the number of successful prototypes and small-scale initiatives, policy makers’ understanding of data’s potential and its value proposition generally remains limited (Lutes, 2015). There is also limited appreciation of the advances data science has made the last few years. This is a major limiting factor; we cannot expect policy makers to use data if they do not recognize what data and data science can do.
- The recent (and justifiable) backlash against how certain private companies handle consumer data has had something of a reverse halo effect: There is a growing lack of trust in the way data is collected, analyzed, and used, and this often leads to a certain reluctance (or simply risk-aversion) on the part of officials and others (Engin, 2018).
- Despite several high-profile open data projects around the world, much (probably the majority) of data that could be helpful in governance remains either privately held or otherwise hidden in silos (Verhulst and Young, 2017b). There remains a shortage not only of data but, more specifically, of high-quality and relevant data.
- With few exceptions, the technical capacities of officials remain limited, and this has obviously negative ramifications for the potential use of data in governance (Giest, 2017).
- It’s not just a question of limited technical capacities. There is often a vast conceptual and values gap between the policy and technical communities (Thompson et al., 2015; Uzochukwu et al., 2016); sometimes it seems as if they speak different languages. Compounding this difference in world views is the fact that the two communities rarely interact.
- Yet, data about the use and evidence of the impact of data remain sparse. The impetus to use more data in policy making is stymied by limited scholarship and a weak evidential basis to show that data can be helpful and how. Without such evidence, data advocates are limited in their ability to make the case for more data initiatives in governance.
- Data are not only changing the way policy is developed, but they have also reopened the debate around theory- versus data-driven methods in generating scientific knowledge (Lee, 1973; Kitchin, 2014; Chivers, 2018; Dreyfuss, 2017) and thus directly questioning the evidence base to utilization and implementation of data within policy making. A number of associated challenges are being discussed, such as: (i) traceability and reproducibility of research outcomes (due to “black box processing”); (ii) the use of correlation instead of causation as the basis of analysis, biases and uncertainties present in large historical datasets that cause replication and, in some cases, amplification of human cognitive biases and imperfections; and (iii) the incorporation of existing human knowledge and domain expertise into the scientific knowledge generation processes—among many other topics (Castelvecchi, 2016; Miller and Goodchild, 2015; Obermeyer and Emanuel, 2016; Provost and Fawcett, 2013).
- Finally, we believe that there should be a sound under-pinning a new theory of what we call Policy–Data Interactions. To date, in reaction to the proliferation of data in the commercial world, theories of data management,1 privacy,2 and fairness3 have emerged. From the Human–Computer Interaction world, a manifesto of principles of Human–Data Interaction (Mortier et al., 2014) has found traction, which intends reducing the asymmetry of power present in current design considerations of systems of data about people. However, we need a consistent, symmetric approach to consideration of systems of policy and data, how they interact with one another.
All these challenges are real, and they are sticky. We are under no illusions that they will be overcome easily or quickly….
During the past four conferences, we have hosted an incredibly diverse range of dialogues and examinations by key global thought leaders, opinion leaders, practitioners, and the scientific community (Data for Policy, 2015, 2016, 2017, 2019). What became increasingly obvious was the need for a dedicated venue to deepen and sustain the conversations and deliberations beyond the limitations of an annual conference. This leads us to today and the launch of Data & Policy, which aims to confront and mitigate the barriers to greater use of data in policy making and governance.
Data & Policy is a venue for peer-reviewed research and discussion about the potential for and impact of data science on policy. Our aim is to provide a nuanced and multistranded assessment of the potential and challenges involved in using data for policy and to bridge the “two cultures” of science and humanism—as CP Snow famously described in his lecture on “Two Cultures and the Scientific Revolution” (Snow, 1959). By doing so, we also seek to bridge the two other dichotomies that limit an examination of datafication and is interaction with policy from various angles: the divide between practice and scholarship; and between private and public…
So these are our principles: scholarly, pragmatic, open-minded, interdisciplinary, focused on actionable intelligence, and, most of all, innovative in how we will share insight and pushing at the boundaries of what we already know and what already exists. We are excited to launch Data & Policy with the support of Cambridge University Press and University College London, and we’re looking for partners to help us build it as a resource for the community. If you’re reading this manifesto it means you have at least a passing interest in the subject; we hope you will be part of the conversation….(More)”.
Introducing ‘AI Commons’: A framework for collaboration to achieve global impact
Press Release: “Last week’s 3rd annual AI for Good Global Summit once again showcased the growing number of Artificial Intelligence (AI) projects with promise to advance the United Nations Sustainable Development Goals (SDGs).
Now, using the Summit’s momentum, AI innovators and humanitarian leaders are prepared to take the ‘AI for Good’ movement to the next level.
They are working together to launch an ‘AI Commons’ that aims to scale AI for Good projects and maximize their impact across the world.
The AI Commons will enable AI adopters to connect with AI specialists and data owners to align incentives for innovation and develop AI solutions to precisely defined problems.
“The concept of AI Commons has developed over three editions of the Summit and is now motivating implementation,” said ITU Secretary-General Houlin Zhao in closing remarks to the summit. “AI and data need to be a shared resource if we are serious about scaling AI for good. The community supporting the Summit is creating infrastructure to scale-up their collaboration − to convert the principles underlying the Summit into global impact.”…
The AI Commons will provide an open framework for collaboration, a decentralized system to democratize problem solving with AI.
It aims to be a “knowledge space”, says Banifatemi, answering a key question: “How can problem solving with AI become common knowledge?”
“The goal is to be an open initiative, like a Linux effort, like an open-source network, where everyone can participate and we jointly share and we create an abundance of knowledge, knowledge of how we can solve problems with AI,” said Banifatemi.
AI development and application will build on the state of the art, enabling AI solutions to scale with the help of shared datasets, testing and simulation environments, AI models and associated software, and storage and computing resources….(More)”.
Privacy Enhancing Technologies
The Royal Society: “How can technologies help organisations and individuals protect data in practice and, at the same time, unlock opportunities for data access and use?
The Royal Society’s Privacy Enhancing Technologies project has been investigating this question and has launched a report (PDF) setting out the current use, development and limits of privacy enhancing technologies (PETs) in data analysis.
The data we generate every day holds a lot of value and potentially also contains sensitive information that individuals or organisations might not wish to share with everyone. The protection of personal or sensitive data featured prominently in the social and ethical tensions identified in our British Academy and Royal Society report Data management and use: Governance in the 21st century. For example, how can organisations best use data for public good whilst protecting sensitive information about individuals? Under other circumstances, how can they share data with groups with competing interests whilst protecting commercially or otherwise sensitive information?
Realising the full potential of large-scale data analysis may be constrained by important legal, reputational, political, business and competition concerns. Certain risks can potentially be mitigated and managed with a set of emerging technologies and approaches often collectively referred to as ‘Privacy Enhancing Technologies’ (PETs).
This disruptive set of technologies, combined with changes in wider policy and business frameworks, could enable the sharing and use of data in a privacy-preserving manner. They also have the potential to reshape the data economy and to change the trust relationships between citizens, governments and companies.
This report provides a high-level overview of five current and promising PETs of a diverse nature, with their respective readiness levels and illustrative case studies from a range of sectors, with a view to inform in particular applied data science research and the digital strategies of government departments and businesses. This report also includes recommendations on how the UK could fully realise the potential of PETs and to allow their use on a greater scale.
The project was informed by a series of conversations and evidence gathering events, involving a range of stakeholders across academia, government and the private sector (also see the project terms of reference and Working Group)….(More)”.
AI and the Global South: Designing for Other Worlds
Chapter by Chinmayi Arun in Markus D. Dubber, Frank Pasquale, and Sunit Das (eds.), The Oxford Handbook of Ethics of AI: “This chapter is about the ways in which AI affects, and will continue to affect, the Global South. It highlights why the design and deployment of AI in the South should concern us.
Towards this, it discusses what is meant by the South. The term has a history connected with the ‘Third World’ and has referred to countries that share post-colonial history and certain development goals. However scholars have expanded and refined on it to include different kinds of marginal, disenfranchised populations such that the South is now a plural concept – there are Souths.
The risks of the ways in which AI affects Southern populations include concerns of discrimination, bias, oppression, exclusion and bad design. These can be exacerbated in the context of vulnerable populations, especially those without access to human rights law or institutional remedies. This Chapter outlines these risks as well as the international human rights law that is applicable. It argues that a human rights, centric, inclusive, empowering context-driven approach is necessary….(More)”.
Information Sharing as a Dimension of Smartness: Understanding Benefits and Challenges in Two Megacities
Paper by J. Ramon Gil-Garcia, Theresa A. Pardo, and Manuel De Tuya: “Cities around the world are facing increasingly complex problems.
These problems frequently require collaboration and information sharing across agency boundaries.
In our view, information sharing can be seen as an important dimension of what is recently being called smartness in cities and enables the ability to improve decision making and day-to-day operations in urban settings. Unfortunately, what many city managers are learning is that there are important challenges to sharing information both within their city and with others.
Based on nonemergency service integration initiatives in New York City and Mexico City, this article examines important benefits from and challenges to information sharing in the context of what the participants characterize as smart city initiatives, particularly in large metropolitan areas.
The research question guiding this study is as follows: To what extent do previous findings about information sharing hold in the context of city initiatives, particularly in megacities?
The results provide evidence on the importance of some specific characteristics of cities and megalopolises and how they affect benefits and challenges of information sharing. For instance, cities seem to have more managerial flexibility than other jurisdictions such as state governments.
In addition, megalopolises have most of the necessary technical skills and financial resources needed for information sharing and, therefore, these challenges are not as relevant as in other local governments….(More)”.
How Organizations with Data and Technology Skills Can Play a Critical Role in the 2020 Census
Blog Post by Kathryn L.S. Pettit and Olivia Arena: “The 2020 Census is less than a year away, and it’s facing new challenges that could result in an inaccurate count. The proposed inclusion of a citizenship question, the lack of comprehensive and unified messaging, and the new internet-response option could worsen the undercount of vulnerable and marginalized communities and deprive these groups of critical resources.
The US Census Bureau aims to count every US resident. But some groups are more likely to be missed than others. Communities of color, immigrants, young children, renters, people experiencing homelessness, and people living in rural areas have long been undercounted in the census. Because the census count is used to apportion federal funding and draw legislative districts for political seats, an inaccurate count means that these populations receive less than their fair share of resources and representation.
Local governments and community-based organizations have begun forming Complete Count Committees, coalitions of trusted community voices established to encourage census responses, to achieve a more accurate count in 2020. Local organizations with data and technology skills—like civic tech groups, libraries, technology training organizations, and data intermediaries—can harness their expertise to help these coalitions achieve a complete count.
As the coordinator of the National Neighborhood Indicators Partnership (NNIP), we are learning about 2020 Census mobilization in communities across the country. We have found that data and technology groups are natural partners in this work; they understand what is at risk in 2020, are embedded in communities as trusted data providers, and can amplify the importance of the census.
Threats to a complete count
The proposed citizenship question, currently being challenged in court, would likely suppress the count of immigrants and households in immigrant communities in the US. Though federal law prohibits the Census Bureau from disclosing individual-level data, even to other agencies, people may still be skeptical about the confidentiality of the data or generally distrust the government. Acknowledging these fears is important for organizations partnering in outreach to vulnerable communities.
Another potential hurdle is that, for the first time, the Census Bureau will encourage people to complete their census forms online (though answering by mail or phone will still be options). Though a high tech census could be more cost-effective, the digital divide compounded by the underfunding of the Census Bureau that limited initial testing of new methods and outreach could worsen the undercount….(More)”.
Techno-optimism and policy-pessimism in the public sector big data debate
Paper by Simon Vydra and Bram Klievink: “Despite great potential, high hopes and big promises, the actual impact of big data on the public sector is not always as transformative as the literature would suggest. In this paper, we ascribe this predicament to an overly strong emphasis the current literature places on technical-rational factors at the expense of political decision-making factors. We express these two different emphases as two archetypical narratives and use those to illustrate that some political decision-making factors should be taken seriously by critiquing some of the core ‘techno-optimist’ tenets from a more ‘policy-pessimist’ angle.
In the conclusion we have these two narratives meet ‘eye-to-eye’, facilitating a more systematized interrogation of big data promises and shortcomings in further research, paying appropriate attention to both technical-rational and political decision-making factors. We finish by offering a realist rejoinder of these two narratives, allowing for more context-specific scrutiny and balancing both technical-rational and political decision-making concerns, resulting in more realistic expectations about using big data for policymaking in practice….(More)”.
From Theory to Practice : Open Government Data, Accountability, and Service Delivery
Report by Michael Christopher Jelenic: “Open data and open government data have recently attracted much attention as a means to innovate, add value, and improve outcomes in a variety of sectors, public and private. Although some of the benefits of open data initiatives have been assessed in the past, particularly their economic and financial returns, it is often more difficult to evaluate their social and political impacts. In the public sector, a murky theory of change has emerged that links the use of open government data with greater government accountability as well as improved service delivery in key sectors, including health and education, among others. In the absence of cross-country empirical research on this topic, this paper asks the following: Based on the evidence available, to what extent and for what reasons is the use of open government data associated with higher levels of accountability and improved service delivery in developing countries?
To answer this question, the paper constructs a unique data set that operationalizes open government data, government accountability, service delivery, as well as other intervening and control variables. Relying on data from 25 countries in Sub-Saharan Africa, the paper finds a number of significant associations between open government data, accountability, and service delivery. However, the findings suggest differentiated effects of open government data across the health and education sectors, as well as with respect to service provision and service delivery outcomes. Although this early research has limitations and does not attempt to establish a purely causal relationship between the variables, it provides initial empirical support for claims about the efficacy of open government data for improving accountability and service delivery….(More)”