Northern Europe

Denmark is appointing an ambassador to big tech

Curated on February 27, 2017August 3, 2018 by Stefaan Verhulst

Matthew Hughes in The Next Web: “Question: Is Facebook a country? It sounds silly, but when you think about it, it does have many attributes in common with nation states. For starters, it’s got a population that’s bigger than that of India, and its 2016 revenue wasn’t too far from Estonia’s GDP. It also has a ‘national ethos’. If America’s philosophy is capitalism, Cuba’s is communism, and Sweden’s is social democracy, Facebook’s is ‘togetherness’, as corny as that may sound.

Given all of the above, is it really any surprise that Denmark is considering appointing a ‘big tech ambassador’ whose job is to establish and manage the country’s relationship with the world’s most powerful tech companies?

Denmark’s “digital ambassador” is a first. No country has ever created such a role. Their job will be to liase with the likes of Google, Twitter, Facebook.

Given the fraught relationship many European countries have with American big-tech – especially on issues of taxation, privacy, and national security – Denmark’s decision to extend an olive branch seems sensible.

Speaking with the Washington Post, Danish Foreign Minister Anders Samuelsen said, “just as we engage in a diplomatic dialogue with countries, we also need to establish and prioritize comprehensive relations with tech actors, such as Google, Facebook, Apple and so on. The idea is, we see a lot of companies and new technologies that will in many ways involve and be part of everyday life of citizens in Denmark.”….(More)”

Data in public health

Curated on February 21, 2017August 3, 2018 by Stefaan Verhulst

Jeremy Berg in Science: “In 1854, physician John Snow helped curtail a cholera outbreak in a London neighborhood by mapping cases and identifying a central public water pump as the potential source. This event is considered by many to represent the founding of modern epidemiology. Data and analysis play an increasingly important role in public health today. This can be illustrated by examining the rise in the prevalence of autism spectrum disorders (ASDs), where data from varied sources highlight potential factors while ruling out others, such as childhood vaccines, facilitating wise policy choices…. A collaboration between the research community, a patient advocacy group, and a technology company (www.mss.ng) seeks to sequence the genomes of 10,000 well-phenotyped individuals from families affected by ASD, making the data freely available to researchers. Studies to date have confirmed that the genetics of autism are extremely complicated—a small number of genomic variations are closely associated with ASD, but many other variations have much lower predictive power. More than half of siblings, each of whom has ASD, have different ASD-associated variations. Future studies, facilitated by an open data approach, will no doubt help advance our understanding of this complex disorder….

A new data collection strategy was reported in 2013 to examine contagious diseases across the United States, including the impact of vaccines. Researchers digitized all available city and state notifiable disease data from 1888 to 2011, mostly from hard-copy sources. Information corresponding to nearly 88 million cases has been stored in a database that is open to interested parties without restriction (www.tycho.pitt.edu). Analyses of these data revealed that vaccine development and systematic vaccination programs have led to dramatic reductions in the number of cases. Overall, it is estimated that ∼100 million cases of serious childhood diseases have been prevented through these vaccination programs.

These examples illustrate how data collection and sharing through publication and other innovative means can drive research progress on major public health challenges. Such evidence, particularly on large populations, can help researchers and policy-makers move beyond anecdotes—which can be personally compelling, but often misleading—for the good of individuals and society….(More)”

Big data is adding a whole new dimension to public spaces – here’s how

Curated on February 8, 2017August 3, 2018 by Stefaan Verhulst

Silvio Carta at the Conversation: “Most of us encounter public spaces in our daily lives: whether it’s physical space (a sidewalk, a bench, or a road), a visual element (a panorama, a cityscape) or a mode of transport (bus, train or bike share). But over the past two decades, digital technologies such as smart phones and the internet of things are adding extra layers of information to our public spaces, and transforming the urban environment.

Traditionally, public spaces have been carefully designed by urban planners and architects, and managed by private companies or public bodies. The theory goes that people’s attention and behaviour in public spaces can be guided by the way that architects plan the built environment. Take, for example, Leicester Square in London: the layout of green areas, pathways and benches makes it clear where people are supposed to walk, sit down and look at the natural elements. The public space is a given, which people receive and use within the terms and guidelines provided.

While these ideas are still relevant today, information is now another key material in public spaces. It changes the way that people experience the city. Uber shows us the position of its closest drivers, even when they’re out of sight; route-finding apps such as Google Maps helps us to navigate through unfamiliar territory; Pokemon Go places otherworldly creatures on the pavement right before our eyes.

But we’re not just receiving information – we’re also generating it. Whether you’re “liking” something on Facebook, searching Google, shopping online, or even exchanging an email address for Wi-Fi access; all of the data created by these actions are collected, stored, managed, analysed and brokered to generate monetary value.

Data deluge

But as well as creating profits for private companies, these data provide accurate and continuous updates of how society is evolving, which can be used by governments and designers to manage and design public spaces.

Before big data, the architects designed spaces based on mere assumptions about how people were likely to use them. Success was measured by “small”, localised data methods, such as post-occupancy evaluations, where built projects are observed during their use and assessed against the designers’ original intentions, as well as fitness for purpose and performance. For the most part, the people who used public spaces did not have a say in how they were designed or managed….(More)”

The dark side of e-petitions? Exploring anonymous signatures

Curated on February 7, 2017October 23, 2018 by Stefaan Verhulst

Janne Berg at First Monday: “This paper analyzes anonymous political participation in the form of e-petition signing. The purpose of this study is to increase knowledge about patterns behind anonymous e-petition signing. Since online political participation evokes an important discussion about the balance between the need for transparency on the one hand, and the right for anonymity on the other hand, it is crucial to increase our knowledge of the factors affecting citizens’ choices to remain anonymous. Using quantitative content analysis of 220 informal e-petitions on the site adressit.com in Finland, this study seeks to find possible determinants for the share of anonymous signatures. Findings indicate that the type of demand presented in the e-petition is a key factor predicting the share of anonymous signatures…(More)”

Selected Readings on Algorithmic Scrutiny

Curated on February 1, 2017November 9, 2018 by Prianka Srinivasan

By Prianka Srinivasan, Andrew Young and Stefaan Verhulst

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of algorithmic scrutiny was originally published in 2017.

Introduction

From government policy, to criminal justice, to our news feeds; to business and consumer practices, the processes that shape our lives both online and off are more and more driven by data and the complex algorithms used to form rulings or predictions. In most cases, these algorithms have created “black boxes” of decision making, where models remain inscrutable and inaccessible. It should therefore come as no surprise that several observers and policymakers are calling for more scrutiny of how algorithms are designed and work, particularly when their outcomes convey intrinsic biases or defy existing ethical standards.

While the concern about values in technology design is not new, recent developments in machine learning, artificial intelligence and the Internet of Things have increased the urgency to establish processes and develop tools to scrutinize algorithms.

In what follows, we have curated several readings covering the impact of algorithms on:

Information Intermediaries;
Governance
Finance
Justice

In addition we have selected a few readings that provide insight on possible processes and tools to establish algorithmic scrutiny.

Selected Reading List

Information Intermediaries

Nicholas Diakopoulos – Algorithmic Accountability – Examines how algorithms exert power and influence on individuals’ lives, and what framework for “algorithmic accountability,” particularly in journalism, can be introduced to better investigate their effects.
Nicholas Diakopoulos and Michael Koliska – Algorithmic Transparency in the News Media – Analyzes how the increased use of algorithms in journalism—through news bots and automated writing—may compromise the transparency and accountability of the industry.
Philip M. Napoli – The Algorithm as Institution: Toward a Theoretical Framework for Automated Media Production and Consumption – Uses institutional theory to analyze the use of algorithms and automated news production and consumption tools in journalism.
Lucas Introna and Helen Nissenbaum – Shaping the Web: Why the politics of search engines matters – An early paper that analyzes the risks of search engines in influencing politics and introducing bias in our knowledge gathering.
Tarleton Gillespie – The Relevance of Algorithms – Provides a “conceptual map” to interrogate algorithms, their role in the information ecosystem, and the political implications of their use.
Lee Rainie and Janna Anderson – Code-Dependent: Pros and Cons of the Algorithm Age – A report from the Pew Research Center that seeks to determine whether the net effect of the growing use of algorithms will be positive or negative.
Zeynep Tufekci – Algorithmic Harms beyond Facebook and Google: Emergent Challenges of Computational Agency – Establishes some of the risks and harms in regard to algorithmic computation, particularly in their filtering abilities as seen in Facebook and other social media algorithms.
Brent Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter, and Luciano Floridi – The Ethics of Algorithms: Mapping the Debate – Suggests that algorithms are increasingly becoming the mediator between data and action in our societies, which obscures their ethical implications. Develops a framework of assessing the ethics of algorithms.

Governance

Marijn Janssen – The challenges and limits of big data algorithms in technocratic governance – Investigates the lack of accountability and transparency of algorithms in creating policies and influencing governance. Argues that pure transparency is not adequate, but a greater understanding of how algorithms work is needed to improve their accountability.
Natascha Just and Michael Latzer – Governance by Algorithms: Reality Construction by Algorithmic Selection on the Internet – Argues that algorithmic selection can influence our lives in the same way that institutions do, by constructing realities, affecting perceptions of the world, and influencing our behaviors.

Consumer Finance

Mireille Hildebrandt – The Dawn of a Critical Transparency Right for the Profiling Era – Analyzes and attempts to predict changes in profiling capabilities for consumers, and what laws may develop to encourage their transparency.
Brenda Reddix-Smalls – Credit Scoring and Trade Secrecy: An Algorithmic Quagmire or How the Lack of Transparency in Complex Financial Models Scuttled the Finance Market – Argues that the lack of transparency and regulation surrounding algorithms in financial markets—particularly their use in creating finance risk models—increases the likelihood of another financial crisis like the one on 2007-2008.

Justice

Katja Franko Aas – Sentencing Transparency in the Information Age – Looks at the use of computer assisted sentencing tools, and how this can affect trust in society, particularly in a Scandinavian context.
Gregory Cui – Evidence-Based Sentencing and the Taint of Dangerousness – Calls for greater scrutiny of “evidence-based sentencing,” where automated risk-assessment tools are used to profile, measure and predict a defendant’s risk of recidivism.

Tools & Process Toward Algorithmic Scrutiny

Mike Ananny and Kate Crawford – Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability – Looks at the “inadequacy of transparency” for algorithmic systems, and the limitations of traditional ideals of accountability and transparency when it comes to algorithms.
Anupam Datta, Shayak Sen and Yair Zick – Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems – Develops a formal model for algorithmic transparency, called Quantitative Input Influence (QII) which can provide better explanations over the decisions made by algorithmic systems.
Bryce Goodman and Seth Flaxman – European Union regulations on algorithmic decision-making and a “right to explanation.” – Analyzes the content and implications of a new EU law that creates a “right to explanation,” whereby users can ask for an explanation of an algorithmic decision made about them.
Rene F. Kizilcec – How Much Information? Effects of Transparency on Trust in an Algorithmic Interface – Studies how transparent designs of algorithmic interfaces can foster trust in users.
Joshua A. Kroll, Joanna Huey, Solon Barocas, Edward W. Felten, Joel R. Reidenberg, David G. Robinson, and Harlan Yu – Accountable Algorithms – Questions whether transparency itself will solve the accountability challenges of algorithms, and instead suggests that technological tools can help create automated decisions systems more in line with our legal and policy objectives.

Annotated Selected Reading List

Information Intermediaries

Diakopoulos, Nicholas. “Algorithmic accountability: Journalistic investigation of computational power structures.” Digital Journalism 3.3 (2015): 398-415. http://bit.ly/.

This paper attempts to substantiate the notion of accountability for algorithms, particularly how they relate to media and journalism. It puts forward the notion of “algorithmic power,” analyzing the framework of influence such systems exert, and also introduces some of the challenges in the practice of algorithmic accountability, particularly for computational journalists.

Offers a basis for how algorithms can be analyzed, built in terms of the types of decisions algorithms make in prioritizing, classifying, associating, and filtering information.

Diakopoulos, Nicholas, and Michael Koliska. “Algorithmic transparency in the news media.” Digital Journalism (2016): 1-20. http://bit.ly/2hMvXdE.

This paper analyzes the increased use of “computational journalism,” and argues that though transparency remains a key tenet of journalism, the use of algorithms in gathering, producing and disseminating news undermines this principle.

It first analyzes what the ethical principle of transparency means to journalists and the media. It then highlights the findings from a focus-group study, where 50 participants from the news media and academia were invited to discuss three different case studies related to the use of algorithms in journalism.

They find two key barriers to algorithmic transparency in the media: “(1) a lack of business incentives for disclosure, and (2) the concern of overwhelming end-users with too much information.”

The study also finds a variety of opportunities for transparency across the “data, model, inference, and interface” components of an algorithmic system.

Napoli, Philip M. “The algorithm as institution: Toward a theoretical framework for automated media production and consumption.” Fordham University Schools of Business Research Paper (2013). http://bit.ly/2hKBHqo

This paper puts forward an analytical framework to discuss the algorithmic content creation of media and journalism in an attempt to “close the gap” on theory related to automated media production.

By borrowing concepts from institutional theory, the paper finds that algorithms are distinct forms of media institutions, and the cultural and political implications of this interpretation.

It urges further study in the field of “media sociology” to further unpack the influence of algorithms, and their role in institutionalizing certain norms, cultures and ways of thinking.

Introna, Lucas D., and Helen Nissenbaum. “Shaping the Web: Why the politics of search engines matters.” The Information Society 16.3 (2000): 169-185. http://bit.ly/2ijzsrg.

This paper, published 16 years ago, provides an in-depth account of some of the risks related to search engine optimizations, and the biases and harms these can introduce, particularly on the nature of politics.

Suggests search engines can be designed to account for these political dimensions, and better correlate with the ideal of the World Wide Web as being a place that is open, accessible and democratic.

According to the paper, policy (and not the free market) is the only way to spur change in this field, though the current technical solutions we have introduce further challenges.

Gillespie, Tarleton. “The Relevance of Algorithms.” Media
technologies: Essays on communication, materiality, and society (2014): 167. http://bit.ly/2h6ASEu.

This paper suggests that the extended use of algorithms, to the extent that they undercut many aspects of our lives, (Tarleton calls this public relevance algorithms) are fundamentally “producing and certifying knowledge.” In this ability to create a particular “knowledge logic,” algorithms are a primary feature of our information ecosystem.

The paper goes on to map 6 dimensions of these public relevance algorithms:
- Patterns of inclusion
- Cycles of anticipation
- The evaluation of relevance
- The promise of algorithmic objectivity
- Entanglement with practice
- The production of calculated publics

The paper concludes by highlighting the need for a sociological inquiry into the function, implications and contexts of algorithms, and to “soberly recognize their flaws and fragilities,” despite the fact that much of their inner workings remain hidden.

Rainie, Lee and Janna Anderson. “Code-Dependent: Pros and Cons of the Algorithm Age.” Pew Research Center. February 8, 2017. http://bit.ly/2kwnvCo.

This Pew Research Center report examines the benefits and negative impacts of algorithms as they become more influential in different sectors and aspects of daily life.

Through a scan of the research and practice, with a particular focus on the research of experts in the field, Rainie and Anderson identify seven key themes of the burgeoning Algorithm Age:
- Algorithms will continue to spread everywhere
- Good things lie ahead
- Humanity and human judgment are lost when data and predictive modeling become paramount
- Biases exist in algorithmically-organized systems
- Algorithmic categorizations deepen divides
- Unemployment will rise; and
- The need grows for algorithmic literacy, transparency and oversight

Tufekci, Zeynep. “Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency.” Journal on Telecommunications & High Technology Law 13 (2015): 203. http://bit.ly/1JdvCGo.

This paper establishes some of the risks and harms in regard to algorithmic computation, particularly in their filtering abilities as seen in Facebook and other social media algorithms.

Suggests that the editorial decisions performed by algorithms can have significant influence on our political and cultural realms, and categorizes the types of harms that algorithms may have on individuals and their society.

Takes two case studies–one from the social media coverage of the Ferguson protests, the other on how social media can influence election turnouts–to analyze the influence of algorithms. In doing so, this paper lays out the “tip of the iceberg” in terms of some of the challenges and ethical concerns introduced by algorithmic computing.

Mittelstadt, Brent, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter, and Luciano Floridi. “The Ethics of Algorithms: Mapping the Debate.” Big Data & Society (2016): 3(2). http://bit.ly/2kWNwL6

This paper provides significant background and analysis of the ethical context of algorithmic decision-making. It primarily seeks to map the ethical consequences of algorithms, which have adopted the role of a mediator between data and action within societies.
Develops a conceptual map of 6 ethical concerns:
- - Inconclusive Evidence
- - Inscrutable Evidence
- - Misguided Evidence
- - Unfair Outcomes
- - Transformative Effects
- Traceability

The paper then reviews existing literature, which together with the map creates a structure to inform future debate.

Governance

Janssen, Marijn, and George Kuk. “The challenges and limits of big data algorithms in technocratic governance.” Government Information Quarterly 33.3 (2016): 371-377. http://bit.ly/2hMq4z6.

In regarding the centrality of algorithms in enforcing policy and extending governance, this paper analyzes the “technocratic governance” that has emerged by the removal of humans from decision making processes, and the inclusion of algorithmic automation.

The paper argues that the belief in technocratic governance producing neutral and unbiased results, since their decision-making processes are uninfluenced by human thought processes, is at odds with studies that reveal the inherent discriminatory practices that exist within algorithms.

Suggests that algorithms are still bound by the biases of designers and policy-makers, and that accountability is needed to improve the functioning of an algorithm. In order to do so, we must acknowledge the “intersecting dynamics of algorithm as a sociotechnical materiality system involving technologies, data and people using code to shape opinion and make certain actions more likely than others.”

Just, Natascha, and Michael Latzer. “Governance by algorithms: reality construction by algorithmic selection on the Internet.” Media, Culture & Society (2016): 0163443716643157. http://bit.ly/2h6B1Yv.

This paper provides a conceptual framework on how to assess the governance potential of algorithms, asking how technology and software governs individuals and societies.

By understanding algorithms as institutions, the paper suggests that algorithmic governance puts in place more evidence-based and data-driven systems than traditional governance methods. The result is a form of governance that cares more about effects than causes.

The paper concludes by suggesting that algorithmic selection on the Internet tends to shape individuals’ realities and social orders by “increasing individualization, commercialization, inequalities, deterritorialization, and decreasing transparency, controllability, predictability.”

Consumer Finance

Hildebrandt, Mireille. “The dawn of a critical transparency right for the profiling era.” Digital Enlightenment Yearbook 2012 (2012): 41-56. http://bit.ly/2igJcGM.

Analyzes the use of consumer profiling by online businesses in order to target marketing and services to their needs. By establishing how this profiling relates to identification, the author also offers some of the threats to democracy and the right of autonomy posed by these profiling algorithms.

The paper concludes by suggesting that cross-disciplinary transparency is necessary to design more accountable profiling techniques that can match the extension of “smart environments” that capture ever more data and information from users.

Reddix-Smalls, Brenda. “Credit Scoring and Trade Secrecy: An Algorithmic Quagmire or How the Lack of Transparency in Complex Financial Models Scuttled the Finance Market.” UC Davis Business Law Journal 12 (2011): 87. http://bit.ly/2he52ch

Analyzes the creation of predictive risk models in financial markets through algorithmic systems, particularly in regard to credit scoring. It suggests that these models were corrupted in order to maintain a competitive market advantage: “The lack of transparency and the legal environment led to the use of these risk models as predatory credit pricing instruments as opposed to accurate credit scoring predictive instruments.”

The paper suggests that without greater transparency of these financial risk model, and greater regulation over their abuse, another financial crisis like that in 2008 is highly likely.

Justice

Aas, Katja Franko. “Sentencing Transparency in the Information Age.” Journal of Scandinavian Studies in Criminology and Crime Prevention 5.1 (2004): 48-61. http://bit.ly/2igGssK.

This paper questions the use of predetermined sentencing in the US judicial system through the application of computer technology and sentencing information systems (SIS). By assessing the use of these systems between the English speaking world and Norway, the author suggests that such technological approaches to sentencing attempt to overcome accusations of mistrust, uncertainty and arbitrariness often leveled against the judicial system.

However, in their attempt to rebuild trust, such technological solutions can be seen as an attempt to remedy a flawed view of judges by the public. Therefore, the political and social climate must be taken into account when trying to reform these sentencing systems: “The use of the various sentencing technologies is not only, and not primarily, a matter of technological development. It is a matter of a political and cultural climate and the relations of trust in a society.”

Cui, Gregory. “Evidence-Based Sentencing and the Taint of Dangerousness.” Yale Law Journal Forum 125 (2016): 315-315. http://bit.ly/1XLAvhL.

This short essay submitted on the Yale Law Journal Forum calls for greater scrutiny of “evidence based sentencing,” where past data is computed and used to predict future criminal behavior of a defendant. The author suggests that these risk models may undermine the Constitution’s prohibition of bills of attainder, and also are unlawful for inflicting punishment without a judicial trial.

Tools & Processes Toward Algorithmic Scrutiny

Ananny, Mike and Crawford, Kate. “Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability.” New Media & Society. SAGE Publications. 2016. http://bit.ly/2hvKc5x.

This paper attempts to critically analyze calls to improve the transparency of algorithms, asking how historically we are able to confront the limitations of the transparency ideal in computing.

By establishing “transparency as an ideal” the paper tracks the philosophical and historical lineage of this principle, attempting to establish what laws and provisions were put in place across the world to keep up with and enforce this ideal.

The paper goes on to detail the limits of transparency as an ideal, arguing, amongst other things, that it does not necessarily build trust, it privileges a certain function (seeing) over others (say, understanding) and that it has numerous technical limitations.

The paper ends by concluding that transparency is an inadequate way to govern algorithmic systems, and that accountability must acknowledge the ability to govern across systems.

Datta, Anupam, Shayak Sen, and Yair Zick. “Algorithmic Transparency via Quantitative Input Influence.” Proceedings of 37th IEEE Symposium on Security and Privacy. 2016. http://bit.ly/2hgyLTp.

This paper develops what is called a family of Quantitative Input Influence (QII) measures “that capture the degree of influence of inputs on outputs of systems.” The attempt is to theorize a transparency report that is to accompany any algorithmic decisions made, in order to explain any decisions and detect algorithmic discrimination.

QII works by breaking “correlations between inputs to allow causal reasoning, and computes the marginal influence of inputs in situations where inputs cannot affect outcomes alone.”

Finds that these QII measures are useful in scrutinizing algorithms when “black box” access is available.

Goodman, Bryce, and Seth Flaxman. “European Union regulations on algorithmic decision-making and a right to explanation” arXiv preprint arXiv:1606.08813 (2016). http://bit.ly/2h6xpWi.

This paper analyzes the implications of a new EU law, to be enacted in 2018, that calls to “restrict automated individual decision-making (that is, algorithms that make decisions based on user level predictors) which ‘significantly affect’ users.” The law will also allow for a “right to explanation” where users can ask for an explanation behind automated decision made about them.

The paper, while acknowledging the challenges in implementing such laws, suggests that such regulations can spur computer scientists to create algorithms and decision making systems that are more accountable, can provide explanations, and do not produce discriminatory results.

The paper concludes by stating algorithms and computer systems should not aim to be simply efficient, but also fair and accountable. It is optimistic about the ability to put in place interventions to account for and correct discrimination.

Kizilcec, René F. “How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface.” Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2016. http://bit.ly/2hMjFUR.

This paper studies how transparency of algorithms affects our impression of trust by conducting an online field experiment, where participants enrolled in a MOOC a given different explanations for the computer generated grade given in their class.

The study found that “Individuals whose expectations were violated (by receiving a lower grade than expected) trusted the system less, unless the grading algorithm was made more transparent through explanation. However, providing too much information eroded this trust.”

In conclusion, the study found that a balance of transparency was needed to maintain trust amongst the participants, suggesting that pure transparency of algorithmic processes and results may not correlate with high feelings of trust amongst users.

Kroll, Joshua A., et al. “Accountable Algorithms.” University of Pennsylvania Law Review 165 (2016). http://bit.ly/2i6ipcO.

This paper suggests that policy and legal standards need to be updated given the increased use of algorithms to perform tasks and make decisions in arenas that people once did. An “accountability mechanism” is lacking in many of these automated decision making processes.

The paper argues that mere transparency through the divulsion of source code is inadequate when confronting questions of accountability. Rather, technology itself provides a key to create algorithms and decision making apparatuses more inline with our existing political and legal frameworks.

The paper assesses some computational techniques that may provide possibilities to create accountable software and reform specific cases of automated decisionmaking. For example, diversity and anti-discrimination orders can be built into technology to ensure fidelity to policy choices.

Citizens give feedback on city development via Tinder-style app

Curated on February 1, 2017August 3, 2018 by Stefaan Verhulst

Springwise: “CitySwipe is Downtown Santa Monica Inc’s opinion gathering app. The non-profit organization manages the center of the city and is using the app as part of the local government’s consultation on its Downtown Community Plan. The plan provides proposals for the area’s next 20 years of development and includes strategies for increased accessibility and affordable housing and improved public spaces.

The original plan had been to close the consultation period in early 2016 but in order to better reach and interact with as many locals as possible, the review was extended to early 2017. Like Tinder, users of the app swipe left or right depending on their views. Questions are either Yes or No or “Which do you prefer?” and each question is illustrated with a photo. There are 38 questions in total ranging from building design and public art to outdoor concerts and parking. Additional information is gathered by asking users to provide their location and preferred method of transport.

Mexico City recently conducted a city-wide consultation on its new constitution, and Oslo, Norway, is using an app to involve school children in redesigning safe public walkways and cycle paths….(More)”

Using data and design to support people to stay in work

Curated on January 30, 2017August 3, 2018 by Stefaan Verhulst

Cat Drew at Civil Service Quarterly: “…Data and digital are fairly understandable concepts in policy-making. But design? Why is it one of the three Ds?

Policy Lab believes that design approaches are particularly suited to complex issues that have multiple causes and for which there is no one, simple answer. Design encourages people to think about the user’s needs (not just the organisation’s needs), brings in different perspectives to innovate new ideas, and then prototypes (mocks them up and tries them out) to iteratively improve ideas until they find one that can be scaled up.

Composite graph and segmentation analysis collection — Segmentation analysis of those who reported being on health-related benefits in the Understanding Society survey

Policy Lab also recognises that data alone cannot solve policy problems, and has been experimenting with how to combine numerical and more human practices. Data can explain what is happening, while design research methods – such as ethnography, observing people’s behaviours – can explain why things are happening. Data can be used to automate and tailor public services; while design means frontline delivery staff and citizens will actually know about and use them. Data-rich evidence is highly valued by policy-makers; and design can make it understandable and accessible to a wider group of people, opening up policy-making in the process.

The Lab is also experimenting with new data methods.

Data science can be used to look at complex, unstructured data (social media data, for example), in real time. Digital data, such as social media data or internet searches, can reveal how people behave (rather than how they say they behave). It can also look at huge amounts of data far quicker than humans, and find unexpected patterns hidden in the data. Powerful computers can identify trends from historical data and use these to predict what might happen in the future.

Supporting people in work project

The project took a DDD approach to generating insight and then creating ideas. The team (including the data science organisation Mastodon C and design agency Uscreates) used data science techniques together with ethnography to create a rich picture about what was happening. Then it used design methods to create ideas for digital services with the user in mind, and these were prototyped and tested with users.

The data science confirmed many of the known risk factors, but also revealed some new insights. It told us what was happening at scale, and the ethnography explained why.

The data science showed that people were more likely to go onto sickness benefits if they had been in the job a shorter time. The ethnography explained that the relationship with the line manager and a sense of loyalty were key factors in whether someone stayed in work or went onto benefits.

The data science showed that women with clinical depression were less likely to go onto sickness benefits than men with the same condition. The ethnography revealed how this played out in real life:

For example, Ella [not her real name], a teacher from London who had been battling with depression at work for a long time but felt unable to go to her boss about it. She said she was “relieved” when she got cancer, because she could talk to her boss about a physical condition and got time off to deal with both illnesses.

The data science also allowed the segmentation of groups of people who said they were on health-related benefits. Firstly, the clustering revealed that two groups had average health ratings, indicating that other non-health-related issues might be driving this. Secondly, it showed that these two groups were very different (one older group of men with previously high pay and working hours; the other of much younger men with previously low pay and working hours). The conclusion was that their motivations and needs to stay in work – and policy interventions – would be different.

The ethnography highlighted other issues that were not captured in the data but would be important in designing solutions, such as: a lack of shared information across the system; the need of the general practitioner (GP) to refer patients to other non-health services as well as providing a fit note; and the importance of coaching, confidence-building and planning….(More)”

Policy Diffusion at the Local Level: Participatory Budgeting in Estonia

Curated on January 28, 2017August 3, 2018 by Stefaan Verhulst

Jelizaveta Krenjova and Ringa Raudla in Urban Affairs Review: “The existing studies on participatory budgeting (PB) have paid very limited attention to how this participatory tool has spread across local governments (LGs), what kind of diffusion mechanisms have played a predominant role, and which actors and factors have influenced its adoption. Our article seeks to address this gap in the scholarly discussion by exploring the diffusion of PB across LGs in Estonia, where it is a rather new phenomenon. Our qualitative study demonstrates that the diffusion of PB in Estonia has so far been driven by the interaction of two mechanisms: learning and imitation. We also find that an epistemic go-between, information-technological solutions, and the characteristics of the initial adopter played a significant role in shaping the diffusion process….(More)”

Be the Change: Saving the World with Citizen Science

Curated on January 24, 2017August 3, 2018 by Stefaan Verhulst

Book by Chandra Clarke: “It’s so easy to be overwhelmed by everything that is wrong in the world. In 2010, there were 660,000 deaths from malaria. Dire predictions about climate change suggest that sea levels could rise enough to submerge both Los Angeles and London by 2100. Bees are dying, not by the thousands but by the millions.

But what can you do? You’re just one person, right? The good news is that you *can* do something.

It’s called citizen science, and it’s a way for ordinary people like you and me to do real, honest-to-goodness, help-answer-the-big-questions science.

This book introduces you to a world in which it is possible to go on a wildlife survey in a national park, install software on your computer to search for a cure for cancer, have your smartphone log the sound pollution in your city, transcribe ancient Greek scrolls, or sift through the dirt from a site where a mastodon died 11,000 years ago—even if you never finished high school….(More)”

How statistics lost their power – and why we should fear what comes next

Curated on January 21, 2017August 3, 2018 by Stefaan Verhulst

William Davies in The Guardian: “In theory, statistics should help settle arguments. They ought to provide stable reference points that everyone – no matter what their politics – can agree on. Yet in recent years, divergent levels of trust in statistics has become one of the key schisms that have opened up in western liberal democracies. Shortly before the November presidential election, a study in the US discovered that 68% of Trump supporters distrusted the economic data published by the federal government. In the UK, a research project by Cambridge University and YouGov looking at conspiracy theories discovered that 55% of the population believes that the government “is hiding the truth about the number of immigrants living here”.

Rather than diffusing controversy and polarisation, it seems as if statistics are actually stoking them. Antipathy to statistics has become one of the hallmarks of the populist right, with statisticians and economists chief among the various “experts” that were ostensibly rejected by voters in 2016. Not only are statistics viewed by many as untrustworthy, there appears to be something almost insulting or arrogant about them. Reducing social and economic issues to numerical aggregates and averages seems to violate some people’s sense of political decency.

Nowhere is this more vividly manifest than with immigration. The thinktank British Future has studied how best to win arguments in favour ofimmigration and multiculturalism. One of its main findings is that people often respond warmly to qualitative evidence, such as the stories of individual migrants and photographs of diverse communities. But statistics – especially regarding alleged benefits of migration to Britain’s economy – elicit quite the opposite reaction. People assume that the numbers are manipulated and dislike the elitism of resorting to quantitative evidence. Presented with official estimates of how many immigrants are in the country illegally, a common response is to scoff. Far from increasing support for immigration, British Future found, pointing to its positive effect on GDP can actually make people more hostile to it. GDP itself has come to seem like a Trojan horse for an elitist liberal agenda. Sensing this, politicians have now largely abandoned discussing immigration in economic terms.

All of this presents a serious challenge for liberal democracy. Put bluntly, the British government – its officials, experts, advisers and many of its politicians – does believe that immigration is on balance good for the economy. The British government did believe that Brexit was the wrong choice. The problem is that the government is now engaged in self-censorship, for fear of provoking people further.

This is an unwelcome dilemma. Either the state continues to make claims that it believes to be valid and is accused by sceptics of propaganda, or else, politicians and officials are confined to saying what feels plausible and intuitively true, but may ultimately be inaccurate. Either way, politics becomes mired in accusations of lies and cover-ups.

The declining authority of statistics – and the experts who analyse them – is at the heart of the crisis that has become known as “post-truth” politics. And in this uncertain new world, attitudes towards quantitative expertise have become increasingly divided. From one perspective, grounding politics in statistics is elitist, undemocratic and oblivious to people’s emotional investments in their community and nation. It is just one more way that privileged people in London, Washington DC or Brussels seek to impose their worldview on everybody else. From the opposite perspective, statistics are quite the opposite of elitist. They enable journalists, citizens and politicians to discuss society as a whole, not on the basis of anecdote, sentiment or prejudice, but in ways that can be validated. The alternative to quantitative expertise is less likely to be democracy than an unleashing of tabloid editors and demagogues to provide their own “truth” of what is going on across society.

Is there a way out of this polarisation? Must we simply choose between a politics of facts and one of emotions, or is there another way of looking at this situation?One way is to view statistics through the lens of their history. We need to try and see them for what they are: neither unquestionable truths nor elite conspiracies, but rather as tools designed to simplify the job of government, for better or worse. Viewed historically, we can see what a crucial role statistics have played in our understanding of nation states and their progress. This raises the alarming question of how – if at all – we will continue to have common ideas of society and collective progress, should statistics fall by the wayside….(More).”