Selected Readings on Algorithmic Scrutiny

Curated on Posted on 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

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 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.

Notable Privacy and Security Books from 2016

Curated on Posted on by Stefaan Verhulst

Daniel J. Solove at Technology, Academics, Policy: “Here are some notable books on privacy and security from 2016….

Chris Jay Hoofnagle, Federal Trade Commission Privacy Law and Policy

From my blurb: “Chris Hoofnagle has written the definitive book about the FTC’s involvement in privacy and security. This is a deep, thorough, erudite, clear, and insightful work – one of the very best books on privacy and security.”

My interview with Hoofnagle about his book: The 5 Things Every Privacy Lawyer Needs to Know about the FTC: An Interview with Chris Hoofnagle

My further thoughts on the book in my interview post above: “This is a book that all privacy and cybersecurity lawyers should have on their shelves. The book is the most comprehensive scholarly discussion of the FTC’s activities in these areas, and it also delves deep in the FTC’s history and activities in other areas to provide much-needed context to understand how it functions and reasons in privacy and security cases. There is simply no better resource on the FTC and privacy. This is a great book and a must-read. It is filled with countless fascinating things that will surprise you about the FTC, which has quite a rich and storied history. And it is an accessible and lively read too – Chris really makes the issues come alive.”

Gary T. Marx, Windows into the Soul: Surveillance and Society in an Age of High Technology

From Peter Grabosky: “The first word that came to mind while reading this book was cornucopia. After decades of research on surveillance, Gary Marx has delivered an abundant harvest indeed. The book is much more than a straightforward treatise. It borders on the encyclopedic, and is literally overflowing with ideas, observations, and analyses. Windows into the Soul commands the attention of anyone interested in surveillance, past, present, and future. The book’s website contains a rich abundance of complementary material. An additional chapter consists of an intellectual autobiography discussing the author’s interest in, and personal experience with, surveillance over the course of his career. Because of its extraordinary breadth, the book should appeal to a wide readership…. it will be of interest to scholars of deviance and social control, cultural studies, criminal justice and criminology. But the book should be read well beyond the towers of academe. The security industry, broadly defined to include private security and intelligence companies as well as state law enforcement and intelligence agencies, would benefit from the book’s insights. So too should it be read by those in the information technology industries, including the manufacturers of the devices and applications which are central to contemporary surveillance, and which are shaping our future.”

Susan C. Lawrence, Privacy and the Past: Research, Law, Archives, Ethics

From the book blurb: “When the new HIPAA privacy rules regarding the release of health information took effect, medical historians suddenly faced a raft of new ethical and legal challenges—even in cases where their subjects had died years, or even a century, earlier. In Privacy and the Past, medical historian Susan C. Lawrence explores the impact of these new privacy rules, offering insight into what historians should do when they research, write about, and name real people in their work.”

Ronald J. Krotoszynski, Privacy Revisited: A Global Perspective on the Right to Be Left Alone

From Mark Tushnet: “Professor Krotoszynski provides a valuable overview of how several constitutional systems accommodate competing interests in privacy, speech, and democracy. He shows how scholarship in comparative law can help one think about one’s own legal system while remaining sensitive to the different cultural and institutional settings of each nation’s law. A very useful contribution.”

Laura K. Donohue, The Future of Foreign Intelligence: Privacy and Surveillance in a Digital Age…

Gordon Corera, Cyberspies: The Secret History of Surveillance, Hacking, and Digital Espionage…

J. Macgregor Wise, Surveillance and Film…(More; See also Nonfiction Privacy + Security Books).

How to Hold Algorithms Accountable

Curated on Posted on by Stefaan Verhulst

Nicholas Diakopoulos and Sorelle Friedler at MIT Technology Review:  Algorithms are now used throughout the public and private sectors, informing decisions on everything from education and employment to criminal justice. But despite the potential for efficiency gains, algorithms fed by big data can also amplify structural discrimination, produce errors that deny services to individuals, or even seduce an electorate into a false sense of security. Indeed, there is growing awareness that the public should be wary of the societal risks posed by over-reliance on these systems and work to hold them accountable.

Various industry efforts, including a consortium of Silicon Valley behemoths, are beginning to grapple with the ethics of deploying algorithms that can have unanticipated effects on society. Algorithm developers and product managers need new ways to think about, design, and implement algorithmic systems in publicly accountable ways. Over the past several months, we and some colleagues have been trying to address these goals by crafting a set of principles for accountable algorithms….

Accountability implies an obligation to report and justify algorithmic decision-making, and to mitigate any negative social impacts or potential harms. We’ll consider accountability through the lens of five core principles: responsibility, explainability, accuracy, auditability, and fairness.

Responsibility. For any algorithmic system, there needs to be a person with the authority to deal with its adverse individual or societal effects in a timely fashion. This is not a statement about legal responsibility but, rather, a focus on avenues for redress, public dialogue, and internal authority for change. This could be as straightforward as giving someone on your technical team the internal power and resources to change the system, making sure that person’s contact information is publicly available.

Explainability. Any decisions produced by an algorithmic system should be explainable to the people affected by those decisions. These explanations must be accessible and understandable to the target audience; purely technical descriptions are not appropriate for the general public. Explaining risk assessment scores to defendants and their legal counsel would promote greater understanding and help them challenge apparent mistakes or faulty data. Some machine-learning models are more explainable than others, but just because there’s a fancy neural net involved doesn’t mean that a meaningful explanationcan’t be produced.

Accuracy. Algorithms make mistakes, whether because of data errors in their inputs (garbage in, garbage out) or statistical uncertainty in their outputs. The principle of accuracy suggests that sources of error and uncertainty throughout an algorithm and its data sources need to be identified, logged, and benchmarked. Understanding the nature of errors produced by an algorithmic system can inform mitigation procedures.

Auditability. The principle of auditability states that algorithms should be developed to enable third parties to probe and review the behavior of an algorithm. Enabling algorithms to be monitored, checked, and criticized would lead to more conscious design and course correction in the event of failure. While there may be technical challenges in allowing public auditing while protecting proprietary information, private auditing (as in accounting) could provide some public assurance. Where possible, even limited access (e.g., via an API) would allow the public a valuable chance to audit these socially significant algorithms.

Fairness. As algorithms increasingly make decisions based on historical and societal data, existing biases and historically discriminatory human decisions risk being “baked in” to automated decisions. All algorithms making decisions about individuals should be evaluated for discriminatory effects. The results of the evaluation and the criteria used should be publicly released and explained….(More)”

New Data Portal to analyze governance in Africa

Curated on Posted on by Stefaan Verhulst

Press Release: “The Mo Ibrahim Foundation launched today the new IIAG Data Portal, which provides unprecedented access to 15 years of data, crucial to assessing the quality of governance in African countries. The Data Portal is freely available online. It will serve as an interactive platform for in-depth analysis and review of governance performance across Africa….

The IIAG Data Portal contains a number of innovative features, including:

  • enabling users to undertake bespoke analysis of governance ranks, scores and trends over the fifteen-year period since 2000, for each of Africa’s 54 countries
  • making possible, for the first time, to examine data at the indicator-level for all 95 IIAG indicators
  • allowing users to generate visualisations and graphics from the data that are shareable online
  • providing a user-friendly interface that facilitates navigation for anyone, from expert statisticians to the public.

….Mo Ibrahim, Chair of the Mo Ibrahim Foundation, said:

Strengthening the African national statistical offices and the robustness and availability of data is crucial to unlocking the continent’s potential. It is a matter of ownership, of identity and of sovereignty. Without sound national data, there is no way you can define adequate public policies nor measure their outcomes.Giving free access to data is about empowering people. It is less eye-catching than building a hospital or a school but it is a smart way to ensure that more hospitals and schools are delivered more effectively and efficiently. I’m delighted that the Foundation can play its part in taking forward Africa’s data revolution, and I hope that our new Data Portal will support efforts to improve governance on the continent.

The IIAG data portal can be accessed online via desktop and mobile at iiag.online.”

Artificial Intelligence can streamline public comment for federal agencies

Curated on Posted on by Stefaan Verhulst

John Davis at the Hill: “…What became immediately clear to me was that — although not impossible to overcome — the lack of consistency and shared best practices across all federal agencies in accepting and reviewing public comments was a serious impediment. The promise of Natural Language Processing and cognitive computing to make the public comment process light years faster and more transparent becomes that much more difficult without a consensus among federal agencies on what type of data is collected – and how.

“There is a whole bunch of work we have to do around getting government to be more customer friendly and making it at least as easy to file your taxes as it is to order a pizza or buy an airline ticket,” President Obama recently said in an interview with WIRED. “Whether it’s encouraging people to vote or dislodging Big Data so that people can use it more easily, or getting their forms processed online more simply — there’s a huge amount of work to drag the federal government and state governments and local governments into the 21st century.”

…expanding the discussion around Artificial Intelligence and regulatory processes to include how the technology should be leveraged to ensure fairness and responsiveness in the very basic processes of rulemaking – in particular public notices and comments. These technologies could also enable us to consider not just public comments formally submitted to an agency, but the entire universe of statements made through social media posts, blogs, chat boards — and conceivably every other electronic channel of public communication.

Obviously, an anonymous comment on the Internet should not carry the same credibility as a formally submitted, personally signed statement, just as sworn testimony in court holds far greater weight than a grapevine rumor. But so much public discussion today occurs on Facebook pages, in Tweets, on news website comment sections, etc. Anonymous speech enjoys explicit protection under the Constitution, based on a justified expectation that certain sincere statements of sentiment might result in unfair retribution from the government.

Should we simply ignore the valuable insights about actual public sentiment on specific issues made possible through the power of Artificial Intelligence, which can ascertain meaning from an otherwise unfathomable ocean of relevant public conversations? With certain qualifications, I believe Artificial Intelligence, or AI, should absolutely be employed in the critical effort to gain insights from public comments – signed or anonymous.

“In the criminal justice system, some of the biggest concerns with Big Data are the lack of data and the lack of quality data,” the NSTC report authors state. “AI needs good data. If the data is incomplete or biased, AI can exacerbate problems of bias.” As a former federal criminal prosecutor and defense attorney, I am well familiar with the absolute necessity to weigh the relative value of various forms of evidence – or in this case, data…(More)“

Make Algorithms Accountable

Curated on Posted on by Stefaan Verhulst

Julia Angwin in The New York Times: “Algorithms are ubiquitous in our lives. They map out the best route to our destination and help us find new music based on what we listen to now. But they are also being employed to inform fundamental decisions about our lives.

Companies use them to sort through stacks of résumés from job seekers. Credit agencies use them to determine our credit scores. And the criminal justice system is increasingly using algorithms to predict a defendant’s future criminality.
Those computer-generated criminal “risk scores” were at the center of a recent Wisconsin Supreme Court decision that set the first significant limits on the use of risk algorithms in sentencing.
The court ruled that while judges could use these risk scores, the scores could not be a “determinative” factor in whether a defendant was jailed or placed on probation. And, most important, the court stipulated that a pre sentence report submitted to the judge must include a warning about the limits of the algorithm’s accuracy.

This warning requirement is an important milestone in the debate over how our data-driven society should hold decision-making software accountable.But advocates for big data due process argue that much more must be done to assure the appropriateness and accuracy of algorithm results.

An algorithm is a procedure or set of instructions often used by a computer to solve a problem. Many algorithms are secret. In Wisconsin, for instance,the risk-score formula was developed by a private company and has never been publicly disclosed because it is considered proprietary. This secrecy has made it difficult for lawyers to challenge a result.

 The credit score is the lone algorithm in which consumers have a legal right to examine and challenge the underlying data used to generate it. In 1970,President Richard M. Nixon signed the Fair Credit Reporting Act. It gave people the right to see the data in their credit reports and to challenge and delete data that was inaccurate.

For most other algorithms, people are expected to read fine-print privacy policies, in the hopes of determining whether their data might be used against them in a way that they wouldn’t expect.

 “We urgently need more due process with the algorithmic systems influencing our lives,” says Kate Crawford, a principal researcher atMicrosoft Research who has called for big data due process requirements.“If you are given a score that jeopardizes your ability to get a job, housing or education, you should have the right to see that data, know how it was generated, and be able to correct errors and contest the decision.”

The European Union has recently adopted a due process requirement for data-driven decisions based “solely on automated processing” that“significantly affect” citizens. The new rules, which are set to go into effect in May 2018, give European Union citizens the right to obtain an explanation of automated decisions and to challenge those decisions. However, since the European regulations apply only to situations that don’t involve human judgment “such as automatic refusal of an online credit application or e-recruiting practices without any human intervention,” they are likely to affect a narrow class of automated decisions. …More recently, the White House has suggested that algorithm makers police themselves. In a recent report, the administration called for automated decision-making tools to be tested for fairness, and for the development of“algorithmic auditing.”

But algorithmic auditing is not yet common. In 2014, Eric H. Holder Jr.,then the attorney general, called for the United States SentencingCommission to study whether risk assessments used in sentencing were reinforcing unjust disparities in the criminal justice system. No study was done….(More)”

Big data for government good: using analytics for policymaking

Curated on Posted on by Stefaan Verhulst

Kent Smetters in The Hill: ” Big Data and analytics are driving advancements that touch nearly every part of our lives. From improving disaster relief efforts following a storm, to enhancing patient response to specific medications to criminal justice reform and real-time traffic reporting, Big Data is saving lives, reducing costs and improving productivity across the private and the public sector.Yet when our elected officials draft policy they lack access to advanced data and analytics that would help them understand the economic implications of proposed legislation. Instead of using Big Data to inform and shape vital policy questions, Members of Congress typically don’t receive a detailed analysis of a bill until after it has been written, and after they have sought support for it. That’s when a policy typically undergoes a detailed budgetary analysis. And even then, these assessments often ignore the broader impact on jobs and the economy.

Yet when our elected officials draft policy they lack access to advanced data and analytics that would help them understand the economic implications of proposed legislation. Instead of using Big Data to inform and shape vital policy questions, Members of Congress typically don’t receive a detailed analysis of a bill until after it has been written, and after they have sought support for it. That’s when a policy typically undergoes a detailed budgetary analysis. And even then, these assessments often ignore the broader impact on jobs and the economy.

We must do better. Just as modern marketing firms use deep analytical tools to make smart business decisions, policymakers in Washington should similarly have access to modern tools for analyzing important policy questions.
Will Social Security be solvent for our grandchildren? How will changes to immigration policy influence the number of jobs and the GDP? How will tax reform impact the budget, economic growth and the income distribution? What is the impact of new investments in health care, education and roads? These are big questions that must be answered with reliable data and analysis while legislation is being written, not afterwards. The absence leaves us with ideology-driven partisanship.

Simply put, Washington needs better tools to evaluate these complex factors. Imagine the productive conversations we could have if we applied the kinds of tools that are commonplace in the business world to help Washington make more informed choices.

For example, with the help of a nonpartisan budget model from the Wharton School of the University of Pennsylvania, policymakers and the public can uncover some valuable—and even surprising—information about our choices surrounding Social Security, immigration and other issues.

By analyzing more than 4,000 different Social Security policy options, for example, the model projects that the Social Security Trust Fund will be depleted three years earlier than the Social Security Administration’s projections, barring any changes in current law. The tool’s projected shortfalls are larger than the SSA’s, in fact—because it takes into account how changes over time will affect the outcome. We also learn that many standard policy options fail to significantly move the Trust Fund exhaustion date, as these policies phase in too slowly or are too small. Securing Social Security, we now know, requires a range of policy combinations and potentially larger changes than we may have been considering.

Immigration policy, too, is an area where we could all benefit from greater understanding. The political left argues that legalizing undocumented workers will have a positive impact on jobs and the economy. The political right argues for just the opposite—deportation of undocumented workers—for many of the same reasons. But, it turns out, the numbers don’t offer much support to either side.

On one hand, legalization actually slightly reduces the number of jobs. The reason is simple: legal immigrants have better access to school and college, and they can spend more time looking for the best job match. However, because legal immigrants can gain more skills, the actual impact on GDP from legalization alone is basically a wash.

The other option being discussed, deportation, also reduces jobs, in this case because the number of native-born workers can’t rise enough to absorb the job losses caused by deportation. GDP also declines. Calculations based on 125 different immigration policy combinations show that increasing the total amount of legal immigrants—especially those with higher skills—is the most effective policy for increasing employment rates and GDP….(More)”

Data-Driven Justice Initiative, Disrupting Cycle of Incarceration

Curated on Posted on by Stefaan Verhulst

The White House: “Every year, more than 11 million people move through America’s 3,100 local jails, many on low-level, non-violent misdemeanors, costing local governments approximately $22 billion a year. In local jails, 64 percent of people suffer from mental illness, 68 percent have a substance abuse disorder, and 44 percent suffer from chronic health problems. Communities across the country have recognized that a relatively small number of these highly vulnerable people cycle repeatedly not just through local jails, but also hospital emergency rooms, shelters, and other public systems, receiving fragmented and uncoordinated care at great cost to American taxpayers, with poor outcomes.

For example, in Miami-Dade, Florida found that 97 people with serious mental illness accounted for $13.7 million in services over four years, spending more than 39,000 days in either jail, emergency rooms, state hospitals or psychiatric facilities in their county. In response, the county provided key mental health de-escalation training to their police officers and 911 dispatchers and, over the past five years, Miami-Dade police have responded to nearly 50,000 calls for service for people in mental health crisis, but have made only 109 arrests, diverting more than 10,000 people to services or safely stabilizing situations without arrest. The jail population fell from over 7000 to just over 4700 and the county was able to close an entire jail facility, saving nearly $12 million a year.

In addition, on any given day, more than 450,000 people are held in jail before trial, nearly 63 percent of the local jail population, even though they have not been convicted of a crime. A 2014 study of New York’s Riker’s Island jail found more than 86% percent of detained individuals were held on a bond of $500 or less. To tackle the challenges of bail, in 2014 Charlotte-Mecklenburg, NC began using a data-based risk assessment tool to identify low risk people in jail and find ways to release them safely. Since they began using the tool, the jail population has gone down 20 percent, significantly more low-risk individuals have been released from jail, and there has been no increase in reported crime.

To break this cycle of incarceration, the Administration has launched the Data-Driven Justice Initiative with a bipartisan coalition of city, county, and state governments who have committed to using data-driven strategies to divert low-level offenders with mental illness out of the criminal system and to change approaches to pre-trial incarceration so that low risk offenders no longer stay in jail simply because they cannot afford a bond. These innovative strategies, which have measurably reduced jail populations in several communities, help stabilize individuals and families, better serve communities, and, often, saves money in the process. DDJ communities commit to:

  1. combining data from across criminal justice and health systems to identify the individuals with the highest number of contacts with police, ambulance, emergency departments, and other services, and, leverage existing resources to link them to health, behavioral health, and social services in the community;
  2. equipping law enforcement and first responders to enable more rapid deployment of tools, approaches, and other innovations they need to safely and more effectively respond to people in mental health crisis and divert people with high needs to identified service providers instead of arrest; and
  3. working towards using objective, data-driven, validated risk assessment tools to inform the safe release of low-risk defendants from jails in order to reduce the jail population held pretrial….(More: FactSheet)”

The Billions We’re Wasting in Our Jails

Curated on Posted on by Stefaan Verhulst

Stephen Goldsmith  and Jane Wiseman in Governing: “By using data analytics to make decisions about pretrial detention, local governments could find substantial savings while making their communities safer….

Few areas of local government spending present better opportunities for dramatic savings than those that surround pretrial detention. Cities and counties are wasting more than $3 billion a year, and often inducing crime and job loss, by holding the wrong people while they await trial. The problem: Only 10 percent of jurisdictions use risk data analytics when deciding which defendants should be detained.

As a result, dangerous people are out in our communities, while many who could be safely in the community are behind bars. Vast numbers of people accused of petty offenses spend their pretrial detention time jailed alongside hardened convicts, learning from them how to be better criminals….

In this era of big data, analytics not only can predict and prevent crime but also can discern who should be diverted from jail to treatment for underlying mental health or substance abuse issues. Avoided costs aggregating in the billions could be better spent on detaining high-risk individuals, more mental health and substance abuse treatment, more police officers and other public safety services.

Jurisdictions that do use data to make pretrial decisions have achieved not only lower costs but also greater fairness and lower crime rates. Washington, D.C., releases 85 percent of defendants awaiting trial. Compared to the national average, those released in D.C. are two and a half times more likely to remain arrest-free and one and a half times as likely to show up for court.

Louisville, Ky., implemented risk-based decision-making using a tool developed by the Laura and John Arnold Foundation and now releases 70 percent of defendants before trial. Those released have turned out to be twice as likely to return to court and to stay arrest-free as those in other jurisdictions. Mesa County, Colo., and Allegheny County, Pa., both have achieved significant savings from reduced jail populations due to data-driven release of low-risk defendants.

Data-driven approaches are beginning to produce benefits not only in the area of pretrial detention but throughout the criminal justice process. Dashboards now in use in a handful of jurisdictions allow not only administrators but also the public to see court waiting times by offender type and to identify and address processing bottlenecks….(More)”

White House Challenges Artificial Intelligence Experts to Reduce Incarceration Rates

Curated on Posted on by Stefaan Verhulst

Jason Shueh at GovTech: “The U.S. spends $270 billion on incarceration each year, has a prison population of about 2.2 million and an incarceration rate that’s spiked 220 percent since the 1980s. But with the advent of data science, White House officials are asking experts for help.

On Tuesday, June 7, the White House Office of Science and Technology Policy’s Lynn Overmann, who also leads the White House Police Data Initiative, stressed the severity of the nation’s incarceration crisis while asking a crowd of data scientists and artificial intelligence specialists for aid.

“We have built a system that is too large, and too unfair and too costly — in every sense of the word — and we need to start to change it,” Overmann said, speaking at a Computing Community Consortium public workshop.

She argued that the U.S., a country that has the highest amount incarcerated citizens in the world, is in need of systematic reforms with both data tools to process alleged offenders and at the policy level to ensure fair and measured sentences. As a longtime counselor, advisor and analyst for the Justice Department and at the city and state levels, Overman said she has studied and witnessed an alarming number of issues in terms of bias and unwarranted punishments.

For instance, she said that statistically, while drug use is about equal between African-Americans and Caucasians, African-Americans are more likely to be arrested and convicted. They also receive longer prison sentences compared to Caucasian inmates convicted of the same crimes….

Data and digital tools can help curb such pitfalls by increasing efficiency, transparency and accountability, she said.

“We think these types of data exchanges [between officials and technologists] can actually be hugely impactful if we can figure out how to take this information and operationalize it for the folks who run these systems,” Obermann noted.

The opportunities to apply artificial intelligence and data analytics, she said, might include using it to improve questions on parole screenings, using it to analyze police body camera footage, and applying it to criminal justice data for legislators and policy workers….

If the private sector is any indication, artificial intelligence and machine learning techniques could be used to interpret this new and vast supply of law enforcement data. In an earlier presentation by Eric Horvitz, the managing director at Microsoft Research, Horvitz showcased how the company has applied artificial intelligence to vision and language to interpret live video content for the blind. The app, titled SeeingAI, can translate live video footage, captured from an iPhone or a pair of smart glasses, into instant audio messages for the seeing impaired. Twitter’s live-streaming app Periscope has employed similar technology to guide users to the right content….(More)”