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Mapping open data governance models: Who makes decisions about government data and how?

Different countries have different models to govern and administer their open data activities.Ana Brandusescu, Danny Lämmerhirt and Stefaan Verhulst call for a systematic and comparative investigation of the different governance models for open data policy and publication.
(cross posted from Open Knowledge International Blog)

The Challenge

An important value proposition behind open data involves increased transparency and accountability of governance. Yet little is known about how open data itself is governed. Who decides and how? How accountable are data holders to both the demand side and policy makers? How do data producers and actors assure the quality of government data? Who, if any, are data stewards within government tasked to make its data open?
Getting a better understanding of open data governance is not only important from an accountability point of view. If there is a better insight of the diversity of decision-making models and structures across countries, the implementation of common open data principles, such as those advocated by the International Open Data Charter, can be accelerated across countries.

In what follows, we seek to develop the initial contours of a research agenda on open data governance models. We start from the premise that different countries have different models to govern and administer their activities – in short, different ‘governance models’. Some countries are more devolved in their decision making, while others seek to organize “public administration” activities more centrally. These governance models clearly impact how open data is governed – providing a broad patchwork of different open data governance across the world and making it difficult to identify who the open data decision makers and data gatekeepers or stewards are within a given country.
For example, if one wants to accelerate the opening up of education data across borders, in some countries this may fall under the authority of sub-national government (such as states, provinces, territories or even cities), while in other countries education is governed by central government or implemented through public-private partnership arrangements. Similarly, transportation or water data may be privatised, while in other cases it may be the responsibility of municipal or regional government. Responsibilities are therefore often distributed across administrative levels and agencies affecting how (open) government data is produced, and published.

Why does this research matter? Why now?

A systematic and comparative investigation of the different governance models for open data policy and publication has been missing till date. To steer the open data movement toward its next phase of maturity, there is an urgency to understand these governance models and their role in open data policy and implementation.
For instance, the International Open Data Charter states that government data should be “open by default” across entire nations. But the variety of governance systems makes it hard to understand the different levers that could be used to enable nationwide publication of open government data by default. Who holds effectively the power to decide what gets published and what not? By identifying the strengths and weaknesses of governance models, the global open data community (along with the Open Data Charter) and governments can work together and identify the most effective ways to implement open data strategies and to understand what works and what doesn’t.

In the next few months we will seek to increase our comparative understanding of the mechanisms of decision making as it relates to open data within and across government and map the relationships between data holders, decision makers, data producers, data quality assurance actors, data users and gatekeepers or intermediaries. This may provide for insights on how to improve the open data ecosystem by learning from others.
Additionally, our findings may identify the “levers” within governance models used to provide government data more openly. And finally, having more transparency about who is accountable for open data decisions could allow for a more informed dialogue with other stakeholders on performance of the publication of open government data.
We are interested in how different governance models affect open data policies and practices – including the implementations of global principles and commitments. We want to map the open data governance process and ecosystem by identifying the following key stakeholders, their roles and responsibilities in the administration of open data, and seeking how they are connected:

  • Decision makers – Who leads/asserts decision authority on open data in meetings, procedures, conduct, debate, voting and other issues?
  • Data holders – Which organizations / government bodies manage and administer data?
  • Data producers – Which organizations / government bodies produce what kind of public sector information?
  • Data quality assurance actors – Who are the actors ensuring that produced data adhere to certain quality standards and does this conflict with their publication as open data?
  • Data gatekeepers/stewards – Who controls open data publication?

We plan to research the governance approaches to the following types of data:

  • Health: mortality and survival rates, levels of vaccination, levels of access to health care, waiting times for medical treatment, spend per admission
  • Education: test scores for pupils in national examinations, school attendance rates, teacher attendance rates
  • National Statistics: population, GDP, unemployment
  • Transportation: times and stops of public transport services – buses, trains
  • Trade: import and export of specific commodities, balance of trade data against other countries
  • Company registers: list of registered companies in the country, shareholder and beneficial ownership information, lobbying register(s) with information on companies, associations representatives at parliamentary bodies
  • Legislation: national legal code, bills, transcripts of debates, finances of parties

Output of research

We will use different methods to get rapid insights. This includes interviews with stakeholders such as government officials, as well as open government initiatives from various sectors (e.g. public health services, public education, trade). Interviewees may be open data experts, as well as policymakers or open data champions within government.
The type of questions we will seek to answer beyond the broad topic of “who is doing what”

  • Who holds power to assert authority over open data publication? What roles do different actors within government play to design policies and to implement them?
  • What forms of governance models can be derived from these roles and responsibilities? Can we see a common pattern of how decision-making power is distributed? How do these governance models differ?
  • What are criteria to evaluate the “performance of the observed governance models? How do they for instance influence open data policy and implementation?

Call for contributions

We invite all interested in this topic to contribute their ideas and to participate in the design and execution of one or more case studies. Have you done research on this? If so, we would also like to hear from you!
Contact one or all of the authors at:
Ana Brandusescu: ana.brandusescu@webfoundation.org
Danny Lämmerhirt: danny.lammerhirt@okfn.org
Stefaan Verhulst: stefaan@thegovlab.org

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GovLab Blog GovLab Digest

Smarter Health: Boosting Analytical Capacity in Healthcare

Screen Shot 2017-02-08 at 6.08.18 PMReport by Beth Noveck, Stefaan Verhulst, Andrew Young, Maria Hermosilla, Anirudh Dinesh  and Juliet McMurren: “Public institutions such as the National Health Service in England increasingly want—and are expected—to base their actions on nationally agreed standards rather than anecdote. The collection and analysis of data, when done responsibly and in a trusted manner, has the potential to improve treatment and drive towards value, both social and economic, in healthcare.
However, the goal of using data to improve the NHS and social care is hampered by a “talent gap” – a lack of personnel with data analytical skills – that stands in the way of uncover- ing the rich insights expected to reside in the NHS’ own data. The NHS is not unique among public and even private sector institutions who are struggling to identify, hire and retain people with data science skills, and, above all, with the ability to apply new technological tactics to advancing the institution’s priorities….

Informed by both a literature review and analysis as well as over fifty interviews with NHS and other experts, this paper offers a multiplicity of proposed recommendations for meeting the data analytic talent gap and achieving greater institutional readiness without full-time hiring. …
These recommendations fall into four categories:

  • Using new technology to coordinate distributed talent already present in the NHS, including project marketplaces.
  • Using new technology such as talent banks and skill finders, to find talent hiding in plain sight—namely those with the relevant skills but who are not classed as analysts and match them to projects.
  • Using expert networks to connect with empirical expertise outside the NHS.
  • Creating cost effective incentives to bring talent in from outside, including prize-backed challenges and foundation-funded fellowships…(More)”
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GovLab Blog

SEEKING: Talented Communications Professional

 
The Governance Lab (the GovLab) is excited to announce the search for a talented communications professional to play a central role in helping translate and make relevant the various activities of the GovLab to a diverse and broad audience of practitioners, policy makers, academics, and the general public.
Based at the NYU Tandon School of Engineering, the Governance Lab is a research, training and experimentation laboratory that focuses on improving people’s lives by addressing real world challenges through institutional redesign.
The ideal candidate for this position will be a clear thinker, speaker and writer who can work with the leadership team to develop and distribute materials supporting the organization’s strategic objectives. S/he will employ a wide range of tools and tactics to ensure information reaches target audiences with measurable impact.
The GovLab not only seeks to be a leader and innovator in how to conduct action-research, it also wants to innovate how to communicate effectively and meaningfully. This new position is therefore expected to help us experiment with new communications techniques and will help us “walk the talk” as we are advocating for data driven and collaborative ways of approaching challenges.

MORE INFO HERE

Job Description:
Reporting directly to Senior GovLab leadership, the position serves as a key GovLab ambassador, building strong relationships with intermediaries, including the media, and major stakeholders; will be tasked in developing and implementing a communications strategy for the GovLab’s data program, including projects such as Data CollaborativesOpen Data’s ImpactOpen Data for Developing Economies and Open Data 500; and will be responsible for the enhancement of the GovLab’s social media presence, developing content for website(s), initiating a targeted audience strategy, and managing a fully integrated dissemination plan for various projects of GovLab’s data program that includes speaking engagements, onsite and offsite events, leveraging networks and fundraising when needed.
Responsibilities:

  • Manages the development and production of all print and electronic media including communications materials, such as press releases, blogs, and newsletters;
  • Develops content for information brochures; reviews and edits drafts of correspondence, reports, and brochures for mass distribution; drafts articles and reports on the status of major initiatives for distribution to various audiences;
  • Develops and implements an annual communications plan – especially as it relates to GovLab’s data program;
  • Generates online content that engages readers; coordinates webpage maintenance to ensure that new and consistent information (article links, stories, events) is posted regularly;
  • Develops and manages a distribution list of relevant organizations/individuals;
  • Manages all media contacts;
  • Manage communications surrounding product launches and events promoting GovLab’s work.

MORE INFO HERE

Categories
GovLab Blog GovLab Digest

Data Justice Network

Press Release: “The Governance Lab (The GovLab) at the NYU Tandon School of Engineering has launched the Data Justice Network (datajustice.us).  The website fosters peer-to-peer learning among criminal justice practitioners and policymakers and helps officials get fast and comprehensive answers to their questions about how to make better use of data to reduce incarceration and crime.
Built by the GovLab with support from the Laura and John Arnold Foundation(LJAF) and in collaboration with The Justice Management Institute, the website was designed for practitioners by practitioners to ensure that the platform is both useful and simple. …
“Criminal justice data are collected by multiple agencies, stored in different formats, and maintained in various systems,” LJAF Vice President of Criminal Justice Matt Alsdorf explained. “The lack of data coordination makes it difficult for jurisdictions to analyze information and evaluate the effect of their local criminal justice policies. We are pleased to support the Data Justice Network and believe that it can help to address this issue and make it easier for communities to use data and predictive analytics to safely reduce their jail populations.”
On the website, criminal justice practitioners can search for colleagues with relevant experience, ask and answer questions, and track their own knowledge of innovative ways of using data at every stage of the criminal justice process….
With Data Justice Network, participants can easily get help and advice from those with experience to explore issues such as

  • How can data be used to create a better post-arrest diversion process for the mentally ill and reduce time spent in jail?
  • How can better data be collected about the number of mentally ill or substance abusers in county jails?
  • What is the best way to develop algorithms to predict super-utilizers of the criminal justice system?…(More)”
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Data and its uses for Governance GovLab Blog Selected Readings

The GovLab Selected Readings on Algorithmic Scrutiny

By Prianka Srinivasan, Andrew Young and Stefaan Verhulst
As part of an ongoing effort to build a knowledge base for the field of opening governance by organizing and disseminating its learnings, the GovLab Selected Readings series provides an annotated and curated collection of recommended works on key opening governance topics. In this edition, we explore the literature on Algorithmic Scrutiny. 
To suggest additional readings on this or any other topic, please email biblio@thegovlab.org. 

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

Governance

Consumer Finance

Justice

Tools & Process Toward Algorithmic Scrutiny

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 explanationarXiv 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.