Europe

Toward a User-Centered Social Sector

Curated on February 9, 2017August 20, 2018 by Stefaan Verhulst

Tris Lumley at Stanford Social Innovation Review: “Over the last three years, a number of trends have crystallized that I believe herald the promise of a new phase—perhaps even a new paradigm—for the social sector. I want to explore three of the most exciting, and sketch out where I believe they might take us and why we’d all do well to get involved.

The rise of feedback
New forms of collaboration
Disruption through technology

Taken individually, these three themes are hugely significant in their potential impact on the work of nonprofits and those that invest in them. But viewed together, as interwoven threads, I believe they have the potential to transform both how we work and the underlying fundamental incentives and structure of the social sector.

The rise of feedback

The nonprofit sector is built on a deep and rich history of community engagement. Yet, in a funding market that incentivizes accountability to funders, this strong tradition of listening, engagement, and ownership by primary constituents—the people and communities nonprofits exist to serve—has sometimes faded. Opportunities for funding can drive strategies. Practitioner experience and research evidence can shape program designs. Engagement with service users can become tokenistic, or shallow….

In recognition of this growing momentum, Keystone Accountability and New Philanthropy Capital (NPC) published a paper in 2016 to explore the relationship between impact measurement and user voice. It is our shared belief that many of the recent criticisms of the impact movement—such as impact reporting being used primarily for fundraising rather than improving programs—would be addressed if impact evidence and user voice were seen as two sides of the same coin, and we more routinely sought to synthesize our understanding of nonprofits’ programs from both aspects at once…

New forms of collaboration

As recent critiques of collective impact have pointed out, the social sector has a long history of collaboration. Yet it has not always been the default operating model of nonprofits or their funders. The fragmented nature of the social sector today exposes an urgent imperative for greater focus on collaboration….

Yet the need for greater collaboration and new forms to incentivize and enable it is increasing. Deepening austerity policies, the shrinking of the state in many countries, and the sheer scale of the social issues we face have driven the “demand” side of collaboration. The collective impact movement has certainly been one driver of momentum on the “supply” side, and a number of other forms of collaboration are emerging.

The Young People’s Foundation model, developed in the UK by the John Lyons Charity, is one response to deepening cuts in nonprofit funding. Young People’s Foundations are new organizations that serve three purposes for nonprofits working with young people in the local area—creating a network, leading on collaborative funding bids and contracting processes, and sharing assets across the network.

Elsewhere, philanthropic donors and foundations are increasingly exploring collaboration in practical terms, through pooled grant funds that provide individual donors unrivalled leverage, and that allow groups of funders to benefit from each other’s strengths through coordination and shared strategies. The Dasra Girl Alliance in India is an example of a pooled fund that brings together philanthropic donors and institutional development funders, and fosters collaboration between the nonprofits it supports….

Disruption through technology

Technology might appear an incongruous companion to feedback and collaboration, which are both very human in nature, yet it’s likely to transform our sector….(More)”

State of Open Corporate Data: Wins and Challenges Ahead

Curated on February 8, 2017October 10, 2018 by Stefaan Verhulst

Sunlight Foundation: “For many people working to open data and reduce corruption, the past year could be summed up in two words: “Panama Papers.” The transcontinental investigation by a team from International Center of Investigative Journalists (ICIJ) blew open the murky world of offshore company registration. It put corporate transparency high on the agenda of countries all around the world and helped lead to some notable advances in access to official company register data….

While most companies are created and operated for legitimate economic activity, there is a small percentage that aren’t. Entities involved in corruption, money laundering, fraud and tax evasion frequently use such companies as vehicles for their criminal activity. “The Idiot’s Guide to Money Laundering from Global Witness” shows how easy it is to use layer after layer of shell companies to hide the identity of the person who controls and benefits from the activities of the network. The World Bank’s “Puppet Masters” report found that over 70% of grand corruption cases, in fact, involved the use of offshore vehicles.

For years, OpenCorporates has advocated for company information to be in the public domain as open data, so it is usable and comparable. It was the public reaction to Panama Papers, however, that made it clear that due diligence requires global data sets and beneficial registries are key for integrity and progress.

The call for accountability and action was clear from the aftermath of the leak. ICIJ, the journalists involved and advocates have called for tougher action on prosecutions and more transparency measures: open corporate registers and beneficial ownership registers. A series of workshops organized by the B20 showed that business also needed public beneficial ownership registers….

Last year the UK became the first country in the world to collect and publish who controls and benefits from companies in a structured format, and as open data. Just a few days later, we were able to add the information in OpenCorporates. The UK data, therefore, is one of a kind, and has been highly anticipated by transparency skeptics and advocates advocates alike. So fa,r things are looking good. 15 other countries have committed to having a public beneficial ownership register including Nigeria, Afghanistan, Germany, Indonesia, New Zealand and Norway. Denmark has announced its first public beneficial ownership data will be published in June 2017. It’s likely to be open data.

This progress isn’t limited to beneficial ownership. It is also being seen in the opening up of corporate registers . These are what OpenCorporates calls “core company data”. In 2016, more countries started releasing company register as open data, including Japan, with over 4.4 million companies, Israel, Virginia, Slovenia, Texas, Singapore and Bulgaria. We’ve also had a great start to 2017 , with France publishing their central company database as open data on January 5th.

As more states have embracing open data, the USA jumped from average score of 19/100 to 30/100. Singapore rose from 0 to 20. The Slovak Republic from 20 to 40. Bulgaria wet from 35 to 90. Japan rose from 0 to 70 — the biggest increase of the year….(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)”

Making the Case for Open Contracting in Healthcare Procurement

Curated on February 6, 2017August 3, 2018 by Stefaan Verhulst

Transparency International “…new report “Making the Case for Open Contracting in Healthcare Procurement” examines the utility of open contracting in healthcare procurement. The process relies on governments to disclose procurement information to businesses and civil society improves stakeholders’ understanding of procurement processes increasing the integrity, fairness and efficiency of public contracting.

In several countries, including Honduras, Ukraine and Nigeria, corruption was significantly reduced throughout the healthcare procurement process following the implementation of open contracting, according to the report. Click here to download the report”

The tiny digital camera on every smartphone has had real impact on African lives

Curated on February 3, 2017August 20, 2018 by Stefaan Verhulst

Calestous Juma at Quartz: “…The first major impact of the technology on African users was to expand global connectivity by making it possible for the youth to access information that was collected using the technology via the Internet.

African engineers have been able use such information to design their own technologies suited to local condition. In 2016 Arthur Zhang, a young Cameroonian medical engineering was awarded the $37,000 Africa Prize for Engineering Innovation by the UK Royal Academy of Engineering. Zhang invented the Cardiopad, a tablet computer takes heart readings and sends them to a heart specialist using the Internet.

Zhang was trained in electronic engineering but gained much of the relevant medical knowledge by watching video online, many which had been posted using digital camera. Many more young Africans are following in Zhang footprints in using such material to acquire knowledge that is available through their regular university courses….

In agriculture, farmers can how take diseased images of the leaves of their crops and share them with scientists around the world for identification and advice. Such digital imaging research is an important addition to other agricultural used of mobile phones that constitute low-cost agricultural extension approaches.

Young African engineers are making extensive use of mobile phones for disease diagnosis. Ugandan researchers developed a jacket that diagnoses pneumonia faster than the standard methods used by doctors. Imaging technologies offer additional ways to expand the range of diagnosis for a wide range of diseases.

In low-cost eye are, for example, EyeNetra uses smartphones as a platform to capture the refractive power of the lenses in eyeglasses. EyeNetra is planning to deploy its technology in Nigeria. It has distributed units to be piloted in Gabon, Gambia, Kenya, Morocco, Rwanda, South Africa and Zimbabwe.

There have been concerns that the emerging era of personalized medicine will create a “health divide” between the industrialized and emerging worlds. This is mainly because of human genetic diversity influences the choice of treatment options. Smartphones are becoming as a low-cost way to pre-empt the emergence such a divide.

Climate change is going to force African scientists to study afresh alternations in the microscopic world. With as little as $15 Micro Phone Lens it will soon be possible to turn a regular smartphone into a microscope that capture images and videos at a magnification range of 15-60 times using the phone’s digital zoom feature. The add-on will inspire a new generation of explorers and scientists….(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)”

Quantifying scenic areas using crowdsourced data

Curated on January 29, 2017August 3, 2018 by Stefaan Verhulst

Chanuki Illushka Seresinhe, Helen Susannah Moat and Tobias Preis in Environment and Planning B: Urban Analytics and City Science: “For centuries, philosophers, policy-makers and urban planners have debated whether aesthetically pleasing surroundings can improve our wellbeing. To date, quantifying how scenic an area is has proved challenging, due to the difficulty of gathering large-scale measurements of scenicness. In this study we ask whether images uploaded to the website Flickr, combined with crowdsourced geographic data from OpenStreetMap, can help us estimate how scenic people consider an area to be. We validate our findings using crowdsourced data from Scenic-Or-Not, a website where users rate the scenicness of photos from all around Great Britain. We find that models including crowdsourced data from Flickr and OpenStreetMap can generate more accurate estimates of scenicness than models that consider only basic census measurements such as population density or whether an area is urban or rural. Our results provide evidence that by exploiting the vast quantity of data generated on the Internet, scientists and policy-makers may be able to develop a better understanding of people’s subjective experience of the environment in which they live….(More)”