How Big Data Analytics is Changing Legal Ethics

Curated on Posted on by Stefaan Verhulst

Renee Knake at Bloomberg Law: “Big data analytics are changing how lawyers find clients, conduct legal research and discovery, draft contracts and court papers, manage billing and performance, predict the outcome of a matter, select juries, and more. Ninety percent of corporate legal departments, law firms, and government lawyers note that data analytics are applied in their organizations, albeit in limited ways, according to a 2015 survey. The Legal Services Corporation, the largest funder of civil legal aid for low-income individuals in the United States, recommended in 2012 that all states collect and assess data on case progress/outcomes to improve the delivery of legal services. Lawyers across all sectors of the market increasingly recognize how big data tools can enhance their work.

A growing literature advocates for businesses and governmental bodies to adopt data ethics policies, and many have done so. It is not uncommon to find data-use policies prominently displayed on company or government websites, or required a part of a click-through consent before gaining access to a mobile app or webpage. Data ethics guidelines can help avoid controversies, especially when analytics are used in potentially manipulative or exploitive ways. Consider, for example, Target’s data analytics that uncovered a teen’s pregnancy before her father did, or Orbitz’s data analytics offered pricier hotels to Mac users. These are just two of numerous examples in recent years where companies faced criticism for how they used data analytics.

While some law firms and legal services organizations follow data-use policies or codes of conduct, many do not. Perhaps this is because the legal profession was not transformed as early or rapidly as other industries, or because until now, big data in legal was largely limited to e-discovery, where the data use is confined to the litigation and is subject to judicial oversight. Another reason may be that lawyers believe their rules of professional conduct provide sufficient guidance and protection. Unlike other industries, lawyers are governed by a special code of ethical obligations to clients, the justice system, and the public. In most states, this code is based in part upon the American Bar Association (ABA) Model Rules of Professional Conduct, though rules often vary from jurisdiction to jurisdiction. Several of the Model Rules are relevant to big data use. That said, the Model Rules are insufficient for addressing a number of fundamental ethical concerns.

At the moment, legal ethics for big data analytics is at best an incomplete mix of professional conduct rules and informal policies adopted by some, but not all law practices. Given the increasing prevalence of data analytics in legal services, lawyers and law students should be familiar not only with the relevant professional conduct rules, but also the ethical questions left unanswered. Listed below is a brief summary of both, followed by a proposed legal ethics agenda for data analytics. …

Questions Unanswered by Lawyer Ethics Rules 

Access/Ownership. Who owns the original data — the individual source or the holder of the pooled information? Who owns the insights drawn from its analysis? Who should receive access to the data compilation and the results?

Anonymity/Identity. Should all personally identifiable or sensitive information be removed from the data? What protections are necessary to respect individual autonomy? How should individuals be able to control and shape their electronic identity?

Consent. Should individuals affirmatively consent to use of their personal data? Or is it sufficient to provide notice, perhaps with an opt-out provision?

Privacy/Security. Should privacy be protected beyond the professional obligation of client confidentiality? How should data be secured? The ABA called upon private and public sector lawyers to implement cyber-security policies, including data use, in a 2012resolution and produced a cyber-security handbook in 2013.

Process. How involved should lawyers be in the process of data collection and analysis? In the context of e-discovery, for example, a lawyer is expected to understand how documents are collected, produced, and preserved, or to work with a specialist. Should a similar level of knowledge be required for all forms of data analytics use?

Purpose. Why was the data first collected from individuals? What is the purpose for the current use? Is there a significant divergence between the original and secondary purposes? If so, is it necessary for the individuals to consent to the secondary purpose? How will unintended consequences be addressed?

Source. What is the source of the data? Did the lawyer collect it directly from clients, or is the lawyer relying upon a third-party source? Client-based data is, of course, subject to the lawyer’s professional conduct rules. Data from any source should be trustworthy, reasonable, timely, complete, and verifiable….(More)”

Why Zika, Malaria and Ebola should fear analytics

Curated on Posted on by Stefaan Verhulst

Frédéric Pivetta at Real Impact Analytics: “Big data is a hot business topic. It turns out to be an equally hot topic for the non profit sector now that we know the vital role analytics can play in addressing public health issues and reaching sustainable development goals.

Big players like IBM just announced they will help fight Zika by analyzing social media, transportation and weather data, among other indicators. Telecom data takes it further by helping to predict the spread of disease, identifying isolated and fragile communities and prioritizing the actions of aid workers.

The power of telecom data

Human mobility contributes significantly to epidemic transmission into new regions. However, there are gaps in understanding human mobility due to the limited and often outdated data available from travel records. In some countries, these are collected by health officials in the hospitals or in occasional surveys.

Telecom data, constantly updated and covering a large portion of the population, is rich in terms of mobility insights. But there are other benefits:

  • it’s recorded automatically (in the Call Detail Records, or CDRs), so that we avoid data collection and response bias.
  • it contains localization and time information, which is great for understanding human mobility.
  • it contains info on connectivity between people, which helps understanding social networks.
  • it contains info on phone spending, which allows tracking of socio-economic indicators.

Aggregated and anonymized, mobile telecom data fills the public data gap without questioning privacy issues. Mixing it with other public data sources results in a very precise and reliable view on human mobility patterns, which is key for preventing epidemic spreads.

Using telecom data to map epidemic risk flows

So how does it work? As in any other big data application, the challenge is to build the right predictive model, allowing decision-makers to take the most appropriate actions. In the case of epidemic transmission, the methodology typically includes five steps :

  • Identify mobility patterns relevant for each particular disease. For example, short-term trips for fast-spreading diseases like Ebola. Or overnight trips for diseases like Malaria, as it spreads by mosquitoes that are active only at night. Such patterns can be deduced from the CDRs: we can actually find the home location of each user by looking at the most active night tower, and then tracking calls to identify short or long-term trips. Aggregating data per origin-destination pairs is useful as we look at intercity or interregional transmission flows. And it protects the privacy of individuals, as no one can be singled out from the aggregated data.
  • Get data on epidemic incidence, typically from local organisations like national healthcare systems or, in case of emergency, from NGOs or dedicated emergency teams. This data should be aggregated on the same level of granularity than CDRs.
  • Knowing how many travelers go from one place to another, for how long, and the disease incidence at origin and destination, build an epidemiological model that can account for the way and speed of transmission of the particular disease.
  • With an import/export scoring model, map epidemic risk flows and flag areas that are at risk of becoming the new hotspots because of human travel.
  • On that base, prioritize and monitor public health measures, focusing on restraining mobility to and from hotspots. Mapping risk also allows launching prevention campaigns at the right places and setting up the necessary infrastructure on time. Eventually, the tool reduces public health risks and helps stem the epidemic.

That kind of application works in a variety of epidemiological contexts, including Zika, Ebola, Malaria, Influenza or Tuberculosis. No doubt the global boom of mobile data will proof extraordinarily helpful in fighting these fierce enemies….(More)”

The Ethics of Biomedical Big Data

Curated on Posted on by Stefaan Verhulst

Book edited by Mittelstadt, Brent Daniel, and Floridi, Luciano: “This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices. ‘Biomedical Big Data’ refers to the analysis of aggregated, very large datasets to improve medical knowledge and clinical care. The book describes the ethical problems posed by aggregation of biomedical datasets and re-use/re-purposing of data, in areas such as privacy, consent, professionalism, power relationships, and ethical governance of Big Data platforms. Approaches and methods are discussed that can be used to address these problems to achieve the appropriate balance between the social goods of biomedical Big Data research and the safety and privacy of individuals. Seventeen original contributions analyse the ethical, social and related policy implications of the analysis and curation of biomedical Big Data, written by leading experts in the areas of biomedical research, medical and technology ethics, privacy, governance and data protection. The book advances our understanding of the ethical conundrums posed by biomedical Big Data, and shows how practitioners and policy-makers can address these issues going forward….(More)”

5 Crowdsourced News Platforms Shaping The Future of Journalism and Reporting

Curated on Posted on by Stefaan Verhulst

 at Crowdsourcing Week: “We are exposed to a myriad of news and updates worldwide. As the crowd becomes moreinvolved in providing information, adopting that ‘upload mindset’ coined by Will Merritt ofZooppa, access to all kinds of data is a few taps and clicks away….

Google News Lab – Better reporting and insightful storytelling

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Last week, Google announced its own crowdsourced news platform dubbed News Lab as part of their efforts “to empower innovation at the intersection of technology and media.”

Scouting for real-time stories, updates, and breaking news is much easier and systematize for journalists worldwide. They can use Google’s tools for better reporting, data for insightful storytelling and programs to focus on the future of media, tackling this initiative in three ways.

“There’s a revolution in data journalism happening in newsrooms today, as more data sets and more tools for analysis are allowing journalists to create insights that were never before possible,” Google said.

Grasswire – first-hand information in real-time

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The design looks bleak and simple, but the site itself is rich with content—first-hand information crowdsourced from Twitter users in real-time and verified. Austen Allred, co-founder of Grasswire was inspired to develop the platform after his “minor slipup” as the American Journalism Review (AJR) puts it, when he missed his train out of Shanghai that actually saved his life.

“The bullet train Allred was supposed to be on collided with another train in the Wenzhou area ofChina’s Zhejiang province,” AJR wrote. “Of the 1,630 passengers, 40 died, and another 210 were injured.” The accident happened in 2011. Unfortunately, the Chinese government made some cover upon the incident, which frustrated Allred in finding first-hand information.

After almost four years, Grasswire was launched, a website that collects real-time information from users for breaking news infused with crowdsourcing model afterward. “It’s since grown into a more complex interface, allowing users to curate selected news tweets by voting and verifying information with a fact-checking system,” AJR wrote, which made the verification of data open and systematized.

Rappler – Project Agos: a technology for disaster risk reduction

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The Philippines is a favorite hub for typhoons. The aftermath of typhoon Haiyan was exceedingly disastrous. But the crowds were steadfast in uploading and sharing information and crowdsourcing became mainstream during the relief operations. Maria Ressa said that they had to educate netizens to use the appropriate hashtags for years (#nameoftyphoonPH, e.g. #YolandaPH) for typhoons to collect data on social media channels easily.

Education and preparation can mitigate the risks and save lives if we utilize the right technology and act accordingly. In her blog, After Haiyan: Crisis management and beyond, Maria wrote, “We need to educate not just the first responders and local government officials, but more importantly, the people in the path of the storms.” …

China’s CCDI app – Crowdsourcing political reports to crack down corruption practices

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In China, if you want to mitigate or possible, eradicate corrupt practices, then there’s an app for that.China launched its own anti-corruption app called, Central Commission for Discipline InspectionWebsite App, allowing the public to upload text messages, photos and videos of Chinese officials’ any corrupt practices.

The platform was released by the government agency, Central Committee for Discipline Inspection.Nervous in case you’ll be tracked as a whistleblower? Interestingly, anyone can report anonymously.China Daily said, “the anti-corruption authorities received more than 1,000 public reports, and nearly70 percent were communicated via snapshots, text messages or videos uploaded,” since its released.Kenya has its own version, too, called Ushahidi using crowdmapping, and India’s I Paid a Bribe.

Newzulu – share news, publish and get paid

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While journalists can get fresh insights from Google News Labs, the crowd can get real-time verified news from Grasswire, and CCDI is open for public, Newzulu crowdsourced news platforms doesn’t just invite the crowd to share news, they can also publish and get paid.

It’s “a community of over 150,000 professional and citizen journalists who share and break news to the world as it happens,” originally based in Sydney. Anyone can submit stories, photos, videos, and even stream live….(More)”

Open Data for Social Change and Sustainable Development

Curated on Posted on by Stefaan Verhulst

Special issue of the Journal of Community Informatics edited by Raed M. Sharif and Francois Van Schalkwyk: “As the second phase of the Emerging Impacts of Open Data in Developing Countries (ODDC) drew to a close, discussions started on a possible venue for publishing some of the papers that emerged from the research conducted by the project partners. In 2012 the Journal of Community Informatics published a special issue titled ‘Community Informatics and Open Government Data’. Given the journal’s previous interest in the field of open data, its established reputation and the fact that it is a peer-reviewed open access journal, the Journal of Community Informatics was approached and agreed to a second special issue with a focus on open data. A closed call for papers was sent out to the project research partners. Shortly afterwards, the first Open Data Research Symposium was held ahead of the International Open Data Conference 2015 in Ottawa, Canada. For the first time, a forum was provided to academics and researchers to present papers specifically on open data. Again there were discussions about an appropriate venue to publish selected papers from the Symposium. The decision was taken by the Symposium Programme Committee to invite the twenty plus presenters to submit full papers for consideration in the special issue.

The seven papers published in this special issue are those that were selected through a double-blind peer review process. Researchers are often given a rough ride by open data advocates – the research community is accused of taking too long, not being relevant enough and of speaking in tongues unintelligible to social movements and policy-makers. And yet nine years after the ground-breaking meeting in Sebastopol at which the eight principles of open government data were penned, seven after President Obama injected political legitimacy into a movement, and five after eleven nation states formed the global Open Government Partnership (OGP), which has grown six-fold in membership; an email crosses our path in which the authors of a high-level report commit to developing a comprehensive understanding of a continental open data ecosystem through an examination of open data supply. Needless to say, a single example is not necessarily representative of global trends in thinking about open data. Yet, the focus on government and on the supply of open data by open data advocates – with little consideration of open data use, the differentiation of users, intermediaries, power structures or the incentives that propel the evolution of ecosystems – is still all too common. Empirical research has already revealed the limitations of ‘supply it and they will use it’ open data practices, and has started to fill critical knowledge gaps to develop a more holistic understanding of the determinants of effective open data policy and practice. As open data policies and practices evolve, the need to capture the dynamics of this evolution and to trace unfolding outcomes becomes critical to advance a more efficient and progressive field of research and practice. The trajectory of the existing body of literature on open data and the role of public authorities, both local and national, in the provision of open data

As open data policies and practices evolve, the need to capture the dynamics of this evolution and to trace unfolding outcomes becomes critical to advance a more efficient and progressive field of research and practice. The trajectory of the existing body of literature on open data and the role of public authorities, both local and national, in the provision of open data is logical and needed in light of the central role of government in producing a wide range of types and volumes of data. At the same time, the complexity of open data ecosystem and the plethora of actors (local, regional and global suppliers, intermediaries and users) makes a compelling case for opening avenues for more diverse discussion and research beyond the supply of open data. The research presented in this special issue of the Journal of Community Informatics touches on many of these issues, sets the pace and contributes to the much-needed knowledge base required to promote the likelihood of open data living up to its promise. … (More)”

How Medical Crowdsourcing Empowers Patients & Doctors

Curated on Posted on by Stefaan Verhulst

Rob Stretch at Rendia: “Whether you’re a solo practitioner in a rural area, or a patient who’s bounced from doctor to doctor with adifficult–to-diagnose condition, there are many reasons why you might seek out expert medical advice from a larger group. Fortunately, in 2016, seeking feedback from other physicians or getting a second opinion is as easy as going online.

“Medical crowdsourcing” sites and apps are gathering steam, from provider-only forums likeSERMOsolves and Figure 1, to patient-focused sites like CrowdMed. They share the same mission of empowering doctors and patients, reducing misdiagnosis, and improving medicine. Is crowdsourcing the future of medicine? Read on to find out more.

Fixing misdiagnosis

An estimated 10 percent to 20 percent of medical cases are misdiagnosed, even more than drug errors and surgery on the wrong patient or body part, according to the National Center for Policy Analysis. And diagnostic errors are the leading cause of malpractice litigation. Doctors often report that with many of their patient cases, they would benefit from the support and advice of their peers.

The photo-sharing app for health professionals, Figure 1, is filling that need. Since we reported on it last year, the app has reached 1 million users and added a direct-messaging feature. The app is geared towards verified medical professionals, and goes to great lengths to protect patient privacy in keeping with HIPAAlaws. According to co-founder and CEO Gregory Levey, an average of 10,000 unique users check in toFigure 1 every hour, and medical professionals and students in 190 countries currently use the app.

Using Figure 1 to crowdsource advice from the medical community has saved at least one life. EmilyNayar, a physician assistant in rural Oklahoma and a self-proclaimed “Figure 1 addict,” told Wired magazine that because of photos she’d seen on the app, she was able to correctly diagnose a patient with shingles meningitis. Another doctor had misdiagnosed him previously, and the wrong medication could have killed him.

Collective knowledge at zero cost

In addition to serving as “virtual colleagues” for isolated medical providers, crowdsourcing forums can pool knowledge from an unprecedented number of doctors in different specialties and even countries,and can do so very quickly.

When we first reported on SERMO, the company billed itself as a “virtual doctors’ lounge.” Now, the global social network with 600,000 verified, credentialed physician members has pivoted to medical crowdsourcing with SERMOsolves, one of its most popular features, according to CEO Peter Kirk.

“Crowdsourcing patient cases through SERMOsolves is an ideal way for physicians to gain valuable information from the collective knowledge of hundreds of physicians instantly,” he said in a press release.According to SERMO, 3,500 challenging patient cases were posted in 2014, viewed 700,000 times, and received 50,000 comments. Most posted cases received responses within 1.5 hours and were resolved within a day. “We have physicians from more than 96 specialties and subspecialties posting on the platform, working together to share their valuable insights at zero cost to the healthcare system.”

While one early user of SERMO wrote on KevinMD.com that he felt the site’s potential was overshadowed by the anonymous rants and complaining, other users have noted that the medical crowdsourcing site has,like Figure 1, directly benefitted patients.

In an article on PhysiciansPractice.com, Richard Armstrong, M.D., cites the example of a family physician in Canada who posted a case of a young girl with an E. coli infection. “Physicians from around the world immediately began to comment and the recommendations resulted in a positive outcome for the patient.This instance offered cross-border learning experiences for the participating doctors, not only regarding the specific medical issue but also about how things are managed in different health systems,” wrote Dr.Armstrong.

Patients get proactive

While patients have long turned to social media to (questionably) crowdsource their medical queries, there are now more reputable sources than Facebook.

Tech entrepreneur Jared Heyman launched the health startup CrowdMed in 2013 after his sister endured a “terrible, undiagnosed medical condition that could have killed her,” he told the Wall Street Journal. She saw about 20 doctors over three years, racking up six-figure medical bills. The NIH Undiagnosed DiseaseProgram finally gave her a diagnosis: fragile X-associated primary ovarian insufficiency, a rare disease that affects just 1 in 15,000 women. A hormone patch resolved her debilitating symptoms….(More)”

How Technology Can Restore Our Trust in Democracy

Curated on Posted on by Stefaan Verhulst

Cenk Sidar in Foreign Policy: “The travails of the Arab Spring, the rise of the Islamic State, and the upsurge of right-wing populism throughout the countries of West all demonstrate a rising frustration with the liberal democratic order in the years since the 2008 financial crisis. There is a growing intellectual consensus that the world is sailing into uncharted territory: a realm marked by authoritarianism, shallow populism, and extremism.

One way to overcome this global resentment is to use the best tools we have to build a more inclusive and direct democracy. Could new technologies such as Augmented Reality (AR), Virtual Reality (VR), data analytics, crowdsourcing, and Blockchain help to restore meaningful dialogue and win back people’s hearts and minds?

Underpinning our unsettling current environment is an irony: Thanks to modern communication technology, the world is more connected than ever — but average people feel more disconnected. In the United States, polls show that trust in government is at a 50-year low. Frustrated Trump supporters and the Britons who voted for Brexit both have a sense of having “lost out” as the global elite consolidates its power and becomes less responsive to the rest of society. This is not an irrational belief: Branko Milanovic, a leading inequality scholar, has found that people in the lower and middle parts of rich countries’ income distributions have been the losers of the last 15 years of globalization.

The same 15 years have also brought astounding advances in technology, from the rise of the Internet to the growing ubiquity of smartphones. And Western society has, to some extent, struggled to find its bearings amid this transition. Militant groups seduce young people through social media. The Internet enables consumers to choose only the news that matches their preconceived beliefs, offering a bottomless well of partisan fury and conspiracy theories. Cable news airing 24/7 keeps viewers in a state of agitation. In short, communication technologies that are meant to bring us together end up dividing us instead (and not least because our politicians have chosen to game these tools for their own advantage).

It is time to make technology part of the solution. More urgently than ever, leaders, innovators, and activists need to open up the political marketplace to allow technology to realize its potential for enabling direct citizen participation. This is an ideal way to restore trust in the democratic process.

As the London School of Economics’ Mary Kaldor put it recently: “The task of global governance has to be reconceptualized to make it possible for citizens to influence the decisions that affect their lives — to reclaim substantive democracy.” One notable exception to the technological disconnect has been fundraising, as candidates have tapped into the Internet to enable millions of average voters to donate small sums. With the right vision, however, technological innovation in politics could go well beyond asking people for money….(More)”

Through the looking glass: Harnessing big data to respond to violent extremism

Curated on Posted on by Stefaan Verhulst

Michele Piercey, Carolyn Forbes, and Hasan Davulcu at Devex:”People think and say all sorts of things that they would never actually do. One of the biggest challenges in countering violent extremism is not only figuring out which people hold radical views, but who is most likely to join and act on behalf of violent extremist organizations. Determining who is likely to become violent is key to designing and evaluating more targeted interventions, but it has proven to be extremely difficult.

There are few recognized tools for assessing perceptions and beliefs, such as whether community sentiment about violent extremist organizations is more or less favorable, or which narratives and counternarratives resonate with vulnerable populations.

Program designers and monitoring and evaluation staff often rely on perception surveying to assess attitudinal changes that CVE programs try to achieve, but there are limitations to this method. Security and logistical challenges to collecting perception data in a conflict-affected community can make it difficult to get a representative sample, while ensuring the safety of enumerators and respondents. And given the sensitivity of the subject matter, respondents may be reluctant to express their actual beliefs to an outsider (that is, social desirability bias can affect data reliability).

The rise of smartphone technology and social media uptake among the burgeoning youth populations of many conflict-affected countries presents a new opportunity to understand what people believe from a safer distance, lessening the associated risks and data defects. Seeing an opportunity in the growing mass of online public data, the marketing industry has pioneered tools to “scrape” and aggregate the data to help companies paint a clearer picture of consumer behavior and perceptions of brands and products.

These developments present a critical question for CVE programs: Could similar tools be developed that would analyze online public data to identify who is being influenced by which extremist narratives and influences, learn which messages go viral, and distinguish groups and individuals who simply hold radical views from those who support or carry out violence?

Using data to track radicalization

Seeking to answer this question, researchers at Arizona State University’s Center for the Study of Religion and Conflict, Cornell University’s Social Dynamics Laboratory, and Carnegie Mellon’s Center for Computational Analysis of Social and Organizational systems have been innovating a wide variety of data analytics tools. ASU’s LookingGlass tool, for example, maps networks of perception, belief, and influence online. ASU and Chemonics International are now piloting the tool on a CVE program in Libya.

Drawn from the humanities and social and computational sciences, LookingGlass retrieves, categorizes, and analyzes vast amounts of data from across the internet to map the spread of extremist and counter-extremist influence online. The tool displays what people think about their political situation, governments and extremist groups, and tracks changes in these perceptions over time and in response to events. It also lets users visualize how groups emerge, interact, coalesce, and fragment in relation to emerging issues and events and evaluates “information cascades” to assess what causes extremist messages to go viral on social media and what causes them to die out.

By assessing the relative influence and expressed beliefs of diverse groups over time and in critical locations, LookingGlass represents an advanced capability for providing real-time contextual information about the ideological drivers of violent and counter-violent extremist movements online. Click here to view a larger version.

For CVE planners, LookingGlass can map social movements in relation to specific countries and regions. Indonesia, for example, has been the site of numerous violent movements and events. A relatively young democracy, the country’s complex political environment encompasses numerous groups seeking radical change across a wide spectrum of social and political issues….(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)”

Open Data for Developing Economies

Curated on Posted on by Stefaan Verhulst

Scan of the literature by Andrew Young, Stefaan Verhulst, and Juliet McMurren: This edition of the GovLab Selected Readings was developed as part of the Open Data for Developing Economies research project (in collaboration with WebFoundation, USAID and fhi360). Special thanks to Maurice McNaughton, Francois van Schalkwyk, Fernando Perini, Michael Canares and David Opoku for their input on an early draft. Please contact Stefaan Verhulst ([email protected]) for any additional input or suggestions.

Open data is increasingly seen as a tool for economic and social development. Across sectors and regions, policymakers, NGOs, researchers and practitioners are exploring the potential of open data to improve government effectiveness, create new economic opportunity, empower citizens and solve public problems in developing economies. Open data for development does not exist in a vacuum – rather it is a phenomenon that is relevant to and studied from different vantage points including Data4Development (D4D), Open Government, the United Nations’ Sustainable Development Goals (SDGs), and Open Development. The below-selected readings provide a view of the current research and practice on the use of open data for development and its relationship to related interventions.

Selected Reading List (in alphabetical order)

  • Open Data and Open Development…
  • Open Data and Developing Countries (National Case Studies)….(More)”