Article by the National Academy of Sciences, part of a series, From Research to Reward, seeking to demonstrate how advances in our understanding of natural processes often lead to surprising and remarkable benefits for society: “…From the time of quills and cannons to our era of cyber-attacks and drone warfare, mathematics has been crucial to national security. Equations ensure missiles land on the right target, combat materials hold up on the battlefield, and secrets stay locked in code. But when it comes to the human side of conflict—decisions people make about when, how, and why to use these tools—professionals saw little place for the precise language of mathematics. Until recently.
Why big data may be having a big effect on how our politics plays out
The Conversation: “…big data… is an inconceivably vast mass of information, which at first glance would seem a giant mess; just white noise.
Unless you know how to decipher it.
According to a story first published in Zurich-based Das Magazin in December and more recently taken up by Motherboard, events such as Brexit and Trump’s ascendency may have been made possible through just such deciphering. The argument is that technology combining psychological profiling and data analysis may have played a pivotal part in exploiting unconscious bias at the individual voter level. The theory is this was used in the recent US election to increase or suppress votes to benefit particular candidates in crucial locations. It is claimed that the company behind this may be active in numerous countries.
The technology at play is based on the integration of a model of psychological profiling known as OCEAN. This uses the details contained within individuals’ digital footprints to create user-specific profiles. These map to the level of the individual, identifiable voter, who can then be manipulated by exploiting beliefs, preferences and biases that they might not even be aware of, but which their data has revealed about them in glorious detail.
As well as enabling the creation of tailored media content, this can also be used to create scripts of relevant talking points for campaign doorknockers to focus on, according to the address and identity of the householder to whom they are speaking.
This goes well beyond the scope and detail of previous campaign strategies. If the theory about the role of these techniques is correct, it signals a new landscape of political strategising. An active researcher in the field, when writing about the company behind this technology (which Trump paid for services during his election campaign), described the potential scale of such technologies:
Marketers have long tailored their placement of advertisements based on their target group, for example by placing ads aimed at conservative consumers in magazines read by conservative audiences. What is new about the psychological targeting methods implemented by Cambridge Analytica, however, is their precision and scale. According to CEO Alexander Nix, the company holds detailed psycho-demographic profiles of more than 220 million US citizens and used over 175,000 different ad messages to meet the unique motivations of their recipients….(More)”
Rules for a Flat World – Why Humans Invented Law and How to Reinvent It for a Complex Global Economy
Book by Gillian Hadfield: “… picks up where New York Times columnist Thomas Friedman left off in his influential 2005 book, The World is Flat. Friedman was focused on the infrastructure of communications and technology-the new web-based platform that allows business to follow the hunt for lower costs, higher value and greater efficiency around the planet seemingly oblivious to the boundaries of nation states. Hadfield peels back this technological platform to look at the ‘structure that lies beneath’—our legal infrastructure, the platform of rules about who can do what, when and how. Often taken for granted, economic growth throughout human history has depended at least as much on the evolution of new systems of rules to support ever-more complex modes of cooperation and trade as it has on technological innovation. When Google rolled out YouTube in over one hundred countries around the globe simultaneously, for example, it faced not only the challenges of technology but also the staggering problem of how to build success in the context of a bewildering and often conflicting patchwork of nation-state-based laws and legal systems affecting every aspect of the business-contract, copyright, encryption, censorship, advertising and more. Google is not alone. A study presented at the World Economic Forum in Davos in 2011 found that for global firms, the number one challenge of the modern economy is increasing complexity, and the number one source of complexity is law. Today, even our startups, the engines of economic growth, are global from Day One.
Put simply, the law and legal methods on which we currently rely have failed to evolve along with technology. They are increasingly unable to cope with the speed, complexity, and constant border-crossing of our new globally inter-connected environment. Our current legal systems are still rooted in the politics-based nation state platform on which the industrial revolution was built. Hadfield argues that even though these systems supported fantastic growth over the past two centuries, today they are too slow, costly, cumbersome and localized to support the exponential rise in economic complexity they fostered. …
The answer to our troubles with law, however, is not the one critics usually reach for—to have less of it. Recognizing that law provides critical infrastructure for the cooperation and collaboration on which economic growth is built is the first step, Hadfield argues, to building a legal environment that does more of what we need it to do and less of what we don’t. …(More)”
A City Is Not a Computer
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)”
Selected Readings on Algorithmic Scrutiny
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
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)”.
Forged Through Fire
Book by John Ferejohn and Frances McCall Rosenbluth: “Peace, many would agree, is a goal that democratic nations should strive to achieve. But is democracy, in fact, dependent on war to survive?
Having spent their celebrated careers exploring this provocative question, John Ferejohn and Frances McCall Rosenbluth trace the surprising ways in which governments have mobilized armies since antiquity, discovering that our modern form of democracy not only evolved in a brutally competitive environment but also quickly disintegrated when the powerful elite no longer needed their citizenry to defend against existential threats.?
Bringing to vivid life the major battles that shaped our current political landscape, the authors begin with the fierce warrior states of Athens and the Roman Republic. While these experiments in “mixed government” would serve as a basis for the bargain between politics and protection at the heart of modern democracy, Ferejohn and Rosenbluth brilliantly chronicle the generations of bloodshed that it would take for the world’s dominant states to hand over power to the people. In fact, for over a thousand years, even as medieval empires gave way to feudal Europe, the king still ruled. Not even the advancements of gunpowder—which decisively tipped the balance away from the cavalry-dominated militaries and in favor of mass armies—could threaten the reign of monarchs and “landed elites” of yore.?
The incredibly wealthy, however, were not well equipped to handle the massive labor classes produced by industrialization. As we learn, the Napoleonic Wars stoked genuine, bottom-up nationalism and pulled splintered societies back together as “commoners” stepped up to fight for their freedom. Soon after, Hitler and Stalin perfectly illustrated the military limitations of dictatorships, a style of governance that might be effective for mobilizing an army but not for winning a world war. This was a lesson quickly heeded by the American military, who would begin to reinforce their ranks with minorities in exchange for greater civil liberties at home.?
Like Francis Fukuyama and Jared Diamond’s most acclaimed works, Forged Through Fire concludes in the modern world, where the “tug of war” between the powerful and the powerless continues to play out in profound ways. Indeed, in the covert battlefields of today, drones have begun to erode the need for manpower, giving politicians even less incentive than before to listen to the demands of their constituency. With American democracy’s flanks now exposed, this urgent examination explores the conditions under which war has promoted one of the most cherished human inventions: a government of the people, by the people, for the people. The result promises to become one of the most important history books to emerge in our time….(More)”
Analytics, Policy, and Governance
“The first available textbook on the rapidly growing and increasingly important field of government analytics” edited by Benjamin Ginsberg, Kathy Wagner Hill and Jennifer Bachner: “This first textbook on the increasingly important field of government analytics provides invaluable knowledge and training for students of government in the synthesis, interpretation, and communication of “big data,” which is now an integral part of governance and policy making. Integrating all the major components of this rapidly growing field, this invaluable text explores the intricate relationship of data analytics to governance while providing innovative strategies for the retrieval and management of information….(More)”
Scientists have a word for studying the post-truth world: agnotology
But scientists have another word for “post-truth”. You might have heard of epistemology, or the study of knowledge. This field helps define what we know and why we know it. On the flip side of this is agnotology, or the study of ignorance. Agnotology is not often discussed, because studying the absence of something — in this case knowledge — is incredibly difficult.
Doubt is our product
Agnotology is more than the study of what we don’t know; it’s also the study of why we are not supposed to know it. One of its more important aspects is revealing how people, usually powerful ones, use ignorance as a strategic tool to hide or divert attention from societal problems in which they have a vested interest.
A perfect example is the tobacco industry’s dissemination of reports that continuously questioned the link between smoking and cancer. As one tobacco employee famously stated, “Doubt is our product.”
In a similar way, conservative think tanks such as The Heartland Institute work to discredit the science behind human-caused climate change.
Despite the fact that 97% of scientists support the anthropogenic causes of climate change, hired “experts” have been able to populate talk shows, news programmes, and the op-ed pages to suggest a lack of credible data or established consensus, even with evidence to the contrary.
These institutes generate pseudo-academic reports to counter scientific results. In this way, they are responsible for promoting ignorance….
Under agnotology 2.0, truth becomes a moot point. It is the sensation that counts. Public media leaders create an impact with whichever arguments they can muster based in whatever fictional data they can create…Donald Trump entering the White House is the pinnacle of agnotology 2.0. Washington Post journalist Fareed Zakaria has argued that in politics, what matters is no longer the economy but identity; we would like to suggest that the problem runs deeper than that.
The issue is not whether we should search for identity, for fame, or for sensational opinions and entertainment. The overarching issue is the fallen status of our collective search for truth, in its many forms. It is no longer a positive attribute to seek out truth, determine biases, evaluate facts, or share knowledge.
Under agnotology 2.0, scientific thinking itself is under attack. In a post-fact and post-truth era, we could very well become post-science….(More)”.
How statistics lost their power – and why we should fear what comes next
William Davies in The Guardian: “In theory, statistics should help settle arguments. They ought to provide stable reference points that everyone – no matter what their politics – can agree on. Yet in recent years, divergent levels of trust in statistics has become one of the key schisms that have opened up in western liberal democracies. Shortly before the November presidential election, a study in the US discovered that 68% of Trump supporters distrusted the economic data published by the federal government. In the UK, a research project by Cambridge University and YouGov looking at conspiracy theories discovered that 55% of the population believes that the government “is hiding the truth about the number of immigrants living here”.
Rather than diffusing controversy and polarisation, it seems as if statistics are actually stoking them. Antipathy to statistics has become one of the hallmarks of the populist right, with statisticians and economists chief among the various “experts” that were ostensibly rejected by voters in 2016. Not only are statistics viewed by many as untrustworthy, there appears to be something almost insulting or arrogant about them. Reducing social and economic issues to numerical aggregates and averages seems to violate some people’s sense of political decency.
Nowhere is this more vividly manifest than with immigration. The thinktank British Future has studied how best to win arguments in favour ofimmigration and multiculturalism. One of its main findings is that people often respond warmly to qualitative evidence, such as the stories of individual migrants and photographs of diverse communities. But statistics – especially regarding alleged benefits of migration to Britain’s economy – elicit quite the opposite reaction. People assume that the numbers are manipulated and dislike the elitism of resorting to quantitative evidence. Presented with official estimates of how many immigrants are in the country illegally, a common response is to scoff. Far from increasing support for immigration, British Future found, pointing to its positive effect on GDP can actually make people more hostile to it. GDP itself has come to seem like a Trojan horse for an elitist liberal agenda. Sensing this, politicians have now largely abandoned discussing immigration in economic terms.
All of this presents a serious challenge for liberal democracy. Put bluntly, the British government – its officials, experts, advisers and many of its politicians – does believe that immigration is on balance good for the economy. The British government did believe that Brexit was the wrong choice. The problem is that the government is now engaged in self-censorship, for fear of provoking people further.
This is an unwelcome dilemma. Either the state continues to make claims that it believes to be valid and is accused by sceptics of propaganda, or else, politicians and officials are confined to saying what feels plausible and intuitively true, but may ultimately be inaccurate. Either way, politics becomes mired in accusations of lies and cover-ups.
The declining authority of statistics – and the experts who analyse them – is at the heart of the crisis that has become known as “post-truth” politics. And in this uncertain new world, attitudes towards quantitative expertise have become increasingly divided. From one perspective, grounding politics in statistics is elitist, undemocratic and oblivious to people’s emotional investments in their community and nation. It is just one more way that privileged people in London, Washington DC or Brussels seek to impose their worldview on everybody else. From the opposite perspective, statistics are quite the opposite of elitist. They enable journalists, citizens and politicians to discuss society as a whole, not on the basis of anecdote, sentiment or prejudice, but in ways that can be validated. The alternative to quantitative expertise is less likely to be democracy than an unleashing of tabloid editors and demagogues to provide their own “truth” of what is going on across society.
Is there a way out of this polarisation? Must we simply choose between a politics of facts and one of emotions, or is there another way of looking at this situation?One way is to view statistics through the lens of their history. We need to try and see them for what they are: neither unquestionable truths nor elite conspiracies, but rather as tools designed to simplify the job of government, for better or worse. Viewed historically, we can see what a crucial role statistics have played in our understanding of nation states and their progress. This raises the alarming question of how – if at all – we will continue to have common ideas of society and collective progress, should statistics fall by the wayside….(More).”