media

Code-Dependent: Pros and Cons of the Algorithm Age

Curated on February 9, 2017May 29, 2019 by Stefaan Verhulst

Janna Anderson and Lee Rainie at PewResearch Center: “Algorithms are instructions for solving a problem or completing a task. Recipes are algorithms, as are math equations. Computer code is algorithmic. The internet runs on algorithms and all online searching is accomplished through them. Email knows where to go thanks to algorithms. Smartphone apps are nothing but algorithms. Computer and video games are algorithmic storytelling. Online dating and book-recommendation and travel websites would not function without algorithms. GPS mapping systems get people from point A to point B via algorithms. Artificial intelligence (AI) is naught but algorithms. The material people see on social media is brought to them by algorithms. In fact, everything people see and do on the web is a product of algorithms. Every time someone sorts a column in a spreadsheet, algorithms are at play, and most financial transactions today are accomplished by algorithms. Algorithms help gadgets respond to voice commands, recognize faces, sort photos and build and drive cars. Hacking, cyberattacks and cryptographic code-breaking exploit algorithms. Self-learning and self-programming algorithms are now emerging, so it is possible that in the future algorithms will write many if not most algorithms.

Algorithms are often elegant and incredibly useful tools used to accomplish tasks. They are mostly invisible aids, augmenting human lives in increasingly incredible ways. However, sometimes the application of algorithms created with good intentions leads to unintended consequences. Recent news items tie to these concerns:

The British pound dropped 6.1% in value in seconds on Oct. 7, 2016, partly because of currency trades triggered by algorithms.
Microsoft engineers created a Twitter bot named “Tay” this past spring in an attempt to chat with Millennials by responding to their prompts, but within hours it was spouting racist, sexist, Holocaust-denying tweets based on algorithms that had it “learning” how to respond to others based on what was tweeted at it.
Facebook tried to create a feature to highlight Trending Topics from around the site in people’s feeds. First, it had a team of humans edit the feature, but controversy erupted when some accused the platform of being biased against conservatives. So, Facebook then turned the job over to algorithms only to find that they could not discern real news from fake news….(More)”.

A City Is Not a Computer

Curated on February 9, 2017August 3, 2018 by Stefaan Verhulst

Shannon Mattern at Places Journal: “…Modernity is good at renewing metaphors, from the city as machine, to the city as organism or ecology, to the city as cyborgian merger of the technological and the organic. Our current paradigm, the city as computer, appeals because it frames the messiness of urban life as programmable and subject to rational order. Anthropologist Hannah Knox explains, “As technical solutions to social problems, information and communications technologies encapsulate the promise of order over disarray … as a path to an emancipatory politics of modernity.” And there are echoes of the pre-modern, too. The computational city draws power from an urban imaginary that goes back millennia, to the city as an apparatus for record-keeping and information management.

We’ve long conceived of our cities as knowledge repositories and data processors, and they’ve always functioned as such. Lewis Mumford observed that when the wandering rulers of the European Middle Ages settled in capital cities, they installed a “regiment of clerks and permanent officials” and established all manner of paperwork and policies (deeds, tax records, passports, fines, regulations), which necessitated a new urban apparatus, the office building, to house its bureaus and bureaucracy. The classic example is the Uffizi (Offices) in Florence, designed by Giorgio Vasari in the mid-16th century, which provided an architectural template copied in cities around the world. “The repetitions and regimentations of the bureaucratic system” — the work of data processing, formatting, and storage — left a “deep mark,” as Mumford put it, on the early modern city.

Yet the city’s informational role began even earlier than that. Writing and urbanization developed concurrently in the ancient world, and those early scripts — on clay tablets, mud-brick walls, and landforms of various types — were used to record transactions, mark territory, celebrate ritual, and embed contextual information in landscape. Mumford described the city as a fundamentally communicative space, rich in information:

Through its concentration of physical and cultural power, the city heightened the tempo of human intercourse and translated its products into forms that could be stored and reproduced. Through its monuments, written records, and orderly habits of association, the city enlarged the scope of all human activities, extending them backwards and forwards in time. By means of its storage facilities (buildings, vaults, archives, monuments, tablets, books), the city became capable of transmitting a complex culture from generation to generation, for it marshaled together not only the physical means but the human agents needed to pass on and enlarge this heritage. That remains the greatest of the city’s gifts. As compared with the complex human order of the city, our present ingenious electronic mechanisms for storing and transmitting information are crude and limited.

Mumford’s city is an assemblage of media forms (vaults, archives, monuments, physical and electronic records, oral histories, lived cultural heritage); agents (architectures, institutions, media technologies, people); and functions (storage, processing, transmission, reproduction, contextualization, operationalization). It is a large, complex, and varied epistemological and bureaucratic apparatus. It is an information processor, to be sure, but it is also more than that.

Were he alive today, Mumford would reject the creeping notion that the city is simply the internet writ large. He would remind us that the processes of city-making are more complicated than writing parameters for rapid spatial optimization. He would inject history and happenstance. The city is not a computer. This seems an obvious truth, but it is being challenged now (again) by technologists (and political actors) who speak as if they could reduce urban planning to algorithms.

Why should we care about debunking obviously false metaphors? It matters because the metaphors give rise to technical models, which inform design processes, which in turn shape knowledges and politics, not to mention material cities. The sites and systems where we locate the city’s informational functions — the places where we see information-processing, storage, and transmission “happening” in the urban landscape — shape larger understandings of urban intelligence….(More)”

‘Collective intelligence’ is not necessarily present in virtual groups

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

Jordan B. Barlow and Alan R. Dennis at LSE: “Do groups of smart people perform better than groups of less intelligent people?

Research published in Science magazine in 2010 reported that groups, like individuals, have a certain level of “collective intelligence,” such that some groups perform consistently well across many different types of tasks, while other groups perform consistently poorly. Collective intelligence is similar to individual intelligence, but at the group level.

Interestingly, the Science study found that collective intelligence was not related to the individual intelligence of group members; groups of people with higher intelligence did not perform better than groups with lower intelligence. Instead, the study found that high performing teams had members with higher social sensitivity – the ability to read the emotions of others using visual facial cues.

Social sensitivity is important when we sit across a table from each other. But what about online, when we exchange emails or text messages? Does social sensitivity matter when I can’t see your face?

We examined the collective intelligence in an online environment in which groups used text-based computer-mediated communication. We followed the same procedures as the original Science study, which used the approach typically used to measure individual intelligence. In individual intelligence tests, a person completes several small “tasks” or problems. An analysis of task scores typically demonstrates that task scores are correlated, meaning that if a person does well on one problem, it is likely that they did well on other problems….

The results were not what we expected. The correlations between our groups’ performance scores were either not statistically significant or significantly negative, as shown in Table 1. The average correlation between any two tasks was -0.05, indicating that performance on one task was not correlated with performance on other tasks. In other words, groups who performed well on one of the tasks were unlikely to perform well on the other tasks…

Our findings challenge the conclusion reported in Science that groups have a general collective intelligence analogous to individual intelligence. Our study shows that no collective intelligence factor emerged when groups used a popular commercial text-based online tool. That is, when using tools with limited visual cues, groups that performed well on one task were no more likely to perform well on a different task. Thus the “collective intelligence” factor related to social sensitivity that was reported in Science is not collective intelligence; it is instead a factor associated with the ability to work well using face-to-face communication, and does not transcend media….(More)”

Social Media and the Internet of Things towards Data-Driven Policymaking in the Arab World: Potential, Limits and Concerns

Curated on February 5, 2017August 3, 2018 by Stefaan Verhulst

Paper by Fadi Salem: “The influence of social media has continued to grow globally over the past decade. During 2016 social media played a highly influential role in what has been described as a “post truth” era in policymaking, diplomacy and political communication. For example, social media “bots” arguably played a key role in influencing public opinion globally, whether on the political or public policy levels. Such practices rely heavily on big data analytics, artificial intelligence and machine learning algorithms, not just in gathering and crunching public views and sentiments, but more so in pro-actively influencing public opinions, decisions and behaviors. Some of these government practices undermined traditional information mediums, triggered foreign policy crises, impacted political communication and disrupted established policy formulation cycles.

On the other hand, the digital revolution has expanded the horizon of possibilities for development, governance and policymaking. A new disruptive transformation is characterized by a fusion of inter-connected technologies where the digital, physical and biological worlds converge. This inter-connectivity is generating — and consuming — an enormous amount of data that is changing the ways policies are conducted, decisions are taken and day-to-day operations are carried out. Within this context, ‘big data’ applications are increasingly becoming critical elements of policymaking. Coupled with the rise of a critical mass of social media users globally, this ubiquitous connectivity and data revolution is promising major transformations in modes of governance, policymaking and citizen-government interaction.

In the Arab region, observations from public sector and decision-making organization suggest that there is limited understanding of the real potential, the limitations, and the public concerns surrounding these big data sources in the Arab region. This report contextualizes the findings in light of the socio-technical transformations taking place in the Arab region, by exploring the growth of social media and building on past editions in the series. The objective is to explore and assess multiple aspects of the ongoing digital transformation in the Arab world and highlight some of the policy implications on a regional level. More specifically, the report aims to better inform our understanding of the convergence of social media and IoT data as sources of big data and their potential impact on policymaking and governance in the region. Ultimately, in light of the availability of massive amount of data from physical objects and people, the questions tackled in the research are: What is the potential for data-driven policymaking and governance in the region? What are the limitations? And most importantly, what are the public concerns that need to be addressed by policymakers while they embark on next phase of the digital governance transformation in the region?

In the Arab region, there are already numerous experiments and applications where data from social media and the “Internet of Things” (IoT) are informing and influencing government practices as sources of big data, effectively changing how societies and governments interact. The report has two main parts. In the first part, we explore the questions discussed in the previous paragraphs through a regional survey spanning the 22 Arab countries. In the second part, it explores growth and usage trends of influential social media platforms across the region, including Facebook, Twitter, Linkedin and, for the first time, Instagram. The findings highlight important changes — and some stagnation — in the ways social media is infiltrating demographic layers in Arab societies, be it gender, age and language. Together, the findings provide important insights for guiding policymakers, business leaders and development efforts. More specifically, these findings can contribute to shaping directions and informing decisions on the future of governance and development in the Arab region….(More)”

It takes more than social media to make a social movement

Curated on February 1, 2017August 20, 2018 by Stefaan Verhulst

Hayley Tsukayama in the Washington Post: “President Trump may have used the power of social media to make his way into the White House, but now social media networks are showing that muscle can work for his opposition, too. Last week, more than 1 million marchers went to Washington and cities around the country — sparked by a Facebook post from one woman with no history of activism. This weekend, the Internet exploded again in discussion about Trump’s travel suspension order, and many used social media to get together and protest the decision.

Twitter said that more than 25 million tweets were sent about the order — as compared with 12 million about Trump’s inauguration. Facebook said that its users generated 151 million “likes, posts, comments and shares” related to the ban, less than the 208 million interactions generated about the inauguration. The companies didn’t reveal how many of those were aimed at organizing, but the social media calls to get people to protest are a testament to the power of these platforms to move people.

The real question, however, is whether this burgeoning new movement can avoid the fate of many so others kick-started by the power of social networks — only to find that it’s much harder to make political change than to make a popular hashtag….

Zeynep Tufekci, an associate professor at the University of North Carolina at Chapel Hill who has written a forthcoming book on the power and fragility of movements borne of social media, found in her research that the very ability for these movements to scale quickly is, in part, why they also can fall apart so quickly compared with traditional grass-roots campaigns….

Now, organizers can bypass the time it takes to build up the infrastructure for a massive march and all the publicity that comes with it. But that also means their high-profile movements skip some crucial organizing steps.

“Digitally networked movements look like the old movements. But by the time the civil rights movement had such a large march, they’d been working on [the issues] for 10 years — if not more,” Tufekci said. The months or even years spent discussing logistics, leafleting and building a coalition, she said, were crucial to the success of the civil rights movements. Other successful efforts, such as the Human Rights Campaign’s efforts to end the “don’t ask, don’t tell” policy against allowing gay people to serve openly in the military were also rooted in organization structures that had been developing and refining their demands for years to present a unified front. Movements organized over social networks often have more trouble jelling, she said, particularly if different factions air their differences on Facebook and Twitter, drawing attention to fractures in a movement….(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.

Documenting Hate

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

Shan Wang at NiemanLab: “A family’s garage vandalized with an image of a swastika and a hateful message targeted at Arabs. Jewish community centers receiving bomb threats. These are just a slice of the incidents of hate across the country after the election of Donald Trump — but getting reliable data on the prevalence of hate and bias crimes to answer questions about whether these sorts of crimes are truly on the rise is nearly impossible.

ProPublica, which led an effort of more than a thousand reporters and students across the U.S. to cover voting problems on Election Day as part of its Electionland project, is now leaning on the collaborative and data-driven Electionland model to track and cover hate crimes.

Documenting Hate, launched last week, is a hate and bias crime-tracking project headed up by ProPublica and supported by a coalition of news and digital media organizations, universities, and civil rights groups like Southern Poverty Law Center (which has been tracking hate groups across the country). Like Electionland, the project is seeking local partners, and will share its data with and guide local reporters interested in writing relevant stories.

“Hate crimes are inadequately tracked,” Scott Klein, assistant manager editor at ProPublica, said. “Local police departments do not report up hate crimes in any consistent way, so the federal data is woefully inadequate, and there’s no good national data on hate crimes. The data is at best locked up by local police departments, and the best we can know is a local undercount.”

Documenting Hate offers a form for anyone to report a hate or bias crime (emphasizing that “we are not law enforcement and will not report this information to the police,” nor will it “share your name and contact information with anybody outside our coalition without your permission”). ProPublica is working with Meedan (whose verification platform Check it also used for Electionland) and crowdsourced crisis-mapping group Ushahidi, as well as several journalism schools, to verify reports coming in through social channels. Ken Schwencke, who helped build the infrastructure for Electionland, is now focused on things like building backend search databases for Documenting Hate, which can be shared with local reporters. The hope is that many stories, interactives, and a comprehensive national database will emerge and paint a fuller picture of the scope of hate crimes in the U.S.

ProPublica is actively seeking local partners, who will have access to the data as well as advice on how to report on sensitive information (no partners to announce just yet, though there’s been plenty of inbound interest, according to Klein). Some of the organizations working with ProPublica were already seeking reader stories of their own….(More)”.

Mass Observation: The amazing 80-year experiment to record our daily lives

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

William Cook at BBC Arts: “Eighty years ago, on 30th January 1937, the New Statesman published a letter which launched the largest (and strangest) writers’ group in British literary history.

An anthropologist called Tom Harrisson, a journalist called Charles Madge and a filmmaker called Humphrey Jennings wrote to the magazine asking for volunteers to take part in a new project called Mass Observation. Over a thousand readers responded, offering their services. Remarkably, this ‘scientific study of human social behaviour’ is still going strong today.

Mass Observation was the product of a growing interest in the social sciences, and a growing belief that the mass media wasn’t accurately reflecting the lives of so-called ordinary people. Instead of entrusting news gathering to jobbing journalists, who were under pressure to provide the stories their editors and proprietors wanted, Mass Observation recruited a secret army of amateur reporters, to track the habits and opinions of ‘the man in the street.’

Ironically, the three founders of this egalitarian movement were all extremely well-to-do. They’d all been to public schools and Oxbridge, but this was the ‘Age of Anxiety’, when capitalism was in chaos and dangerous demagogues were on the rise (plus ça change…).

For these idealistic public schoolboys, socialism was the answer, and Mass Observation was the future. By finding out what ‘ordinary’ folk were really doing, and really thinking, they would forge a new society, more attuned to the needs of the common man.

Mass Observation selected 500 citizen journalists, and gave them regular ‘directives’ to report back on virtually every aspect of their daily lives. They were guaranteed anonymity, which gave them enormous freedom. People opened up about themselves (and their peers) to an unprecedented degree.

Even though they were all unpaid, correspondents devoted a great deal of time to this endeavour – writing at great length, in great detail, over many years. As well as its academic value, Mass Observation proved that autobiography is not the sole preserve of the professional writer. For all of us, the urge to record and reflect upon our lives is a basic human need.

The Second World War was the perfect forum for this vast collective enterprise. Mass Observation became a national diary of life on the home front. For historians, the value of such uncensored revelations is enormous. These intimate accounts of air raids and rationing are far more revealing and evocative than the jolly state-sanctioned reportage of the war years.

After the war, Mass Observation became more commercial, supplying data for market research, and during the 1960s this extraordinary experiment gradually wound down. It was rescued from extinction by the historian Asa Briggs….

The founders of Mass Observation were horrified by what they called “the revival of racial superstition.” Hitler, Franco and Mussolini were in the forefront of their minds. “We are all in danger of extinction from such outbursts of atavism,” they wrote, in 1937. “We look to science to help us, only to find that science is too busy forging new weapons of mass destruction.”

For its founders, Mass Observation was a new science which would build a better future. For its countless correspondents, however, it became something more than that – not merely a social science, but a communal work of art….(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)”