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Search plays a unique role in modern online information systems.

Unlike with social media, where users primarily consume algorithmically curated feeds of information, the typical approach to a search engine begins with a query or question in an effort to seek new information.

However, not all search queries are equal. There are many search terms for which the available relevant data is limited,  non-existent, or deeply problematic.

We call these “data voids.”

Data Voids: Where Missing Data Can Easily Be Exploited explores different types of data voids; the challenges that search engines face when they encounter queries over spaces where data voids exist; and the ways data voids can be exploited by those with ideological, economic, or political agendas.

Authors

Michael Golebiewski, Microsoft Bing

danah boyd, Microsoft Research and Data & Society


primer | 04.18.18

Algorithmic Accountability: A Primer

Robyn Caplan, Joan Donovan, Lauren Hanson, and Jeanna Matthews

Algorithmic Accountability examines the process of assigning responsibility for harm when algorithmic decision-making results in discriminatory and inequitable outcomes.

The primer–originally prepared for the Progressive Congressional Caucus’ Tech Algorithm Briefing–explores the trade-offs debates about algorithms and accountability across several key ethical dimensions, including fairness and bias; opacity and transparency; and lack of standards for auditing.


report | 02.21.18

The Promises, Challenges, and Futures of Media Literacy

Monica Bulger and Patrick Davison

This report responds to the “fake news” problem by evaluating the successes and failures of recent media literacy efforts while pointing towards next steps for educators, legislators, technologists, and philanthropists.


report | 02.21.18

Dead Reckoning

Robyn Caplan, Lauren Hanson, and Joan Donovan

New Data & Society report clarifies current uses of “fake news” and analyzes four specific strategies for intervention.


In the era of big data, how do researchers ethically collect, analyze, and store data? danah boyd, Emily F. Keller, and Bonnie Tijerina explore this question and examine issues from how to achieve informed consent from research subjects in big data research to how to store data securely in case of breaches. The primer evolves into a discussion on how libraries can collaborate with computer scientists to examine ethical big data research issues.


primer | 09.07.16

Advertising in Schools

Maxwell Foxman, Alexandra Mateescu, Monica Bulger

Debates over digital advertising in schools have inherited the frames through which older, pre-digital forms of advertising have been conceptualized, and have also been characterized as departing from them in significant ways. But what, exactly, is new and how have older practices evolved?

D&S research assistant Maxwell Foxman, research analyst Alexandra Mateescu, and researcher Monica Bulger examine how digital advertising can impact students and call for more transparency on how data collection of students is being utilized in a new primer.


In this primer, D&S researcher Claire Fontaine examines the construct of accountability as it functions in discussions around education reform in the American public education system. The paper considers the historic precursors to accountability, as well as the set of political, economic, cultural, and social conditions that led to test scores becoming the main measure of a school’s success.

In addition to the historical context around accountability, the paper considers important questions about who accountability serves, what the incentive structures are, and how accountability is gamed and resisted. In short, accountability of what, to whom, for what ends, at what cost?

Abstract:

There is an ongoing tension in the American public education system between the values of excellence, equity, and a sustained commitment to efficiency. Accountability has emerged as a framework in education reform that promises to promote and balance all three values. Yet, this frame is often contested due to disagreements over the role of incentives and penalties in achieving desirable change, and concerns that the proposed mechanisms will have significant unintended consequences that outweigh potential benefits. More fundamentally, there is widespread disagreement over how to quantify excellence and equity, if it is even possible to do so. Accountability rhetoric echoes a broader turn toward data-driven decision-making and resource allocation across sectors. As a tool of power, accountability processes shift authority and control away from professional educators and toward policymakers, bureaucrats, and test makers.

The construct of accountability is predicated on several assumptions. First, it privileges quantification and statistical analysis as ways of knowing and is built on a long history of standardized testing and data collection. Second, it takes learning to be both measurable and the product of instruction, an empiricist perspective descended from John Locke and the doctrine that knowledge is derived primarily from experience. Third, it holds that schools, rather than families, neighborhoods, communities, or society at large, are fundamentally responsible for student performance. This premise lacks a solid evidentiary basis and is closely related to the ideology of meritocracy. Finally, efforts to achieve accountability presume that market-based solutions can effectively protect the interests of society’s most vulnerable, another controversial assumption.

The accountability movement reflects the application of free market economics to public education, a legacy of the Chicago School of Economics in the post-World War II era. As a set of policies it was instantiated in the Elementary and Secondary Education Act (ESEA) of 1965, reauthorized as the No Child Left Behind Act (NCLB) of 2002, and reinforced by the Every Student Succeeds Act (ESSA) of 2015. Teaching and learning are increasingly measured and quantified to enable analysis of the relationship between inputs (e.g., funding) and outputs (e.g., student performance).

As has been true in other sectors when data-driven surveillance and assessment practices are introduced, outcomes are not always as expected. It is unclear whether this data push will promote equality of opportunity, merely document inequality, or perhaps even increase racial and socioeconomic segregation. Furthermore, little is understood about the costs of increased assessment on the health and success of students and teachers, externalities that are rarely measured or considered in the march to accountability. States will need to generate stakeholder buy-in and think carefully about the metrics they include in their accountability formulas in order to balance mandates for accountability, the benefits that accrue to students from preserving teacher autonomy and professionalism, the social good of equal opportunity, and public calls for transparency and innovation.


In this primer, D&S researcher Monica Bulger defines the boundaries of “personalized learning,” explores the needs that various personalized learning systems aim to meet, and highlights the tensions between what is being promised with personalized learning and the practical realities of implementation. She also raises areas of concern, questions about unintended consequences, and potential risks that may come with the widespread adoption of personalized learning systems and platforms.

 


primer | 05.13.16

Mediation, Automation, Power

Robyn Caplan, danah boyd

A contemporary issues primer occasioned by Data & Society’s Who Controls the Public Sphere in an Era of Algorithms? workshop.

In this primer, D&S research analyst Robyn Caplan and D&S Founder danah boyd articulate emerging concerns and tensions coming to the fore as platforms like Google, Facebook, Twitter, and Weibo have overtaken traditional media forms, becoming the main way that news and information of cultural, economic, social and political significance is being produced, and disseminated. As social media and messaging apps enable the sharing of news information and serve as sites for public discussion and discourse about cultural and political events, the mechanisms and processes underlying this networked infrastructure, particularly big data, algorithms, and the companies controlling these information flows, are having a profound affect on the structure and formation of public and political life.

The authors raise and explore six concerns about the role of algorithms in shaping the public sphere:

  • Algorithms can be used to affect election outcomes and can be biased in favor of political parties.
  • Algorithms are editors that actively shape what content is made visible, but are not treated as such.
  • Algorithms can be used by states to achieve domestic and foreign policy aims.
  • Automation and bots are being used by state and non-state actors to game algorithms and sway public opinion.
  • The journalism industry and the role of the “fourth estate” have been affected by the logic of algorithms, and content is no longer serving reflexive, democratic aims.
  • Algorithms are being designed without consideration of how user feedback inserts biases into the system.

The authors also grapple with five different classes of tensions underpinning these various concerns and raise serious questions about what ideal we should be seeking:

  • Universality, Diversity, Personalization
  • A Change in Gatekeepers?
  • A Collapse/Re-emergence of Boundaries and Borders
  • Power and Accountability
  • Visibility, Accessibility, and Analysis

Finally, six proposed remedies and solutions to algorithmic shaping of the public sphere are considered and problematized. With each potential solution, the competing value systems and interests that feed into the design of technologies is highlighted:

  • Proactive Transparency
  • Reverse Engineering, Technical and Investigative Mechanisms
  • Design/Engineering Solutions
  • Computational/Algorithmic Literacy
  • Governance and Public Interest Frameworks
  • Decentralization in Markets and Technology

All systems of power are manipulated and there is little doubt that public spheres constructed through network technologies and algorithms can be manipulated, both by the architects of those systems and by those who find techniques to shape information flows. Yet, it is important to acknowledge that previous genres of media have been manipulated and that access to the public sphere has never been universal or even. As we seek to redress concerns raised by technical systems and work towards a more ideal form, it is essential to recognize the biases and assumptions that underpin any ideal and critically interrogate who benefits and who does not. No intervention is without externalities.

These varying tensions raise significant questions about who controls – and should control – the public sphere in an era of algorithms, but seeking solutions to existing concerns requires unpacking what values, peoples, and voices should have power.


In this background primer, D&S Research Analyst Laura Reed and D&S Founder danah boyd situate the current debate around the role of technology in the public sphere within a historical context. They identify and tease out some of the underlying values, biases, and assumptions present in the current debate surrounding the relationship between media and democracy, and connect them to existing scholarship within media history that is working to understand the organizational, institutional, social, political, and economic factors affecting the flow of news and information. They also identify a set of key questions to keep in mind as the conversation around technology and the public sphere evolves.

Algorithms play an increasingly significant role in shaping the digital news and information landscape, and there is growing concern about the potential negative impact that algorithms might have on public discourse. Examples of algorithmic biases and increasingly curated news feeds call into question the degree to which individuals have equal access to the means of producing, disseminating, and accessing information online. At the same time, these debates about the relationship between media, democracy, and publics are not new, and linking those debates to these emerging conversations about algorithms can help clarify the underlying assumptions and expectations. What do we want algorithms to do in an era of personalization? What does a successful algorithm look like? What form does an ideal public sphere take in the digital age? In asking these and other questions, we seek to highlight what’s at stake in the conversation about algorithms and publics moving forward.


D&S Research Analyst Laura Reed and D&S Researcher Robyn Caplan put together a set of case studies to complement the contemporary issues primer, Mediation, Automation, and Power, for the Algorithms and Publics project. These case studies explore situations in which algorithmic media is shaping the public sphere across a variety of dimensions, including the changing role of the journalism industry, the use of algorithms for censorship or international compliance, how algorithms are functioning within foreign policy aims, digital gerrymandering, the spread of misinformation, and more.


primer | 02.24.15

Police Body-Worn Cameras – Updated

Alexandra Mateescu, Alex Rosenblat, danah boyd (with support from Jenna Leventoff and David Robinson)

In the wake of the police shooting of Michael Brown in August 2014, as well as the subsequent protests in Ferguson, Missouri and around the country, there has been a call to mandate the use of body-worn cameras to promote accountability and transparency in police-civilian interactions. Both law enforcement and civil rights advocates are excited by the potential of body-worn cameras to improve community policing and safety, but there is no empirical research to conclusively suggest that these will reduce the deaths of black male civilians in encounters with police. There are some documented milder benefits evident from small pilot studies, such as more polite interactions between police and civilians when both parties are aware they are being recorded, and decreased fraudulent complaints made against officers. Many uncertainties about best practices of body-worn camera adoption and use remain, including when the cameras should record, what should be stored and retained, who should have access to the footage, and what policies should determine the release of footage to the public. As pilot and permanent body-worn camera programs are implemented, it is important to ask questions about how they can be best used to achieve their touted goals. How will the implementation of these programs be assessed for their efficacy in achieving accountability goals? What are the best policies to have in place to support those goals?

The primer on police body-worn cameras was written in February 2015. We provided an update on what has happened in the past year with regard to the use of body-worn cameras across the US (the update can be read here) for the 2015 Data & Civil Rights Conference, A New Era of Policing and Justice.


Public calls for data and transparency about police actions have increased in light of widely publicized incidents and patterns of police violence. Opening more data to the public about police actions is one reform recommended by the President’s Task Force on 21st Century Policing. It also has become a key component of the Police Data Initiative (PDI), a pilot program launched by President Obama in May 2015 that brings together federal government agencies, local police departments, community organizers, and industry stakeholders to increase transparency in policing and improve trust between communities and police departments. As of fall 2015, 26 police departments, a tiny fraction of the 18,000 state and local law enforcement agencies operating across the country, have signed on to participate in the PDI by pledging to release more than 100 previously unshared data sets on police-citizen interactions.

This document is a workshop primer from Data & Civil Rights: A New Era of Policing and Justice.


primer | 10.27.15

Data & Civil Rights: Social Media Surveillance and Law Enforcement

Alexandra Mateescu, Douglas Brunton, Alex Rosenblat, Desmond Patton, Zachary Gold, danah boyd

According to a 2014 LexisNexis online survey, eighty percent of federal, state, and local law enforcement professionals use social media platforms as an intelligence gathering tool, but most lack policies governing the use of social media for investigations. Law enforcement agencies utilize social media for a wide range of reasons, including: discovering criminal activity, obtaining probable cause for search warrants, collecting evidence for court hearings, pinpointing the location of criminals, witness identification, as well as broadcasting information and soliciting tips from the public. Social media surveillance includes both manual and automated practices, and methods may be targeted or general.

This document is a workshop primer from Data & Civil Rights: A New Era of Policing and Justice.


primer | 10.27.15

Data & Civil Rights: Criminal Justice and Civil Rights Primer

The Leadership Conference on Civil and Human Rights

New technology has provided an increasingly ubiquitous tool with the potential to build trust between police and the communities they serve and help enhance accountability and transparency in policing and the justice system overall. At the same time, the arrival of new technology does not guarantee that a police agency will better protect the civil rights of the community it serves. Such technology could also be used to intensify disproportionate surveillance and disproportionate enforcement in heavily policed communities of color. Without the right safeguards, there is a real risk that these new tools could become instruments of injustice.

This document is a workshop primer from Data & Civil Rights: A New Era of Policing and Justice.


primer | 10.27.15

Data & Civil Rights: Courts and Predictive Algorithms

Angèle Christin, Alex Rosenblat, danah boyd

One of the most striking innovations in the criminal justice system during the past thirty years has been the introduction of actuarial methods – statistical models and software programs –designed to help judges and prosecutors assess the risk of criminal offenders. Predictive algorithms are currently used in four major areas of the U.S. criminal justice system: pretrial and bail, sentencing, probation and parole, and juvenile justice. These algorithms consider a small number of variables about a defendant – either connected to her or his criminal history (previous offenses, failure to appear in court, violent offenses, etc.) or socio-demographic characteristics (age, sex, employment status, drug history, etc.) – in an effort to predict a defendant’s risk of recidivism or their likelihood to fail to appear in court if they are let out on bail.

This document is a workshop primer from Data & Civil Rights: A New Era of Policing and Justice.


primer | 10.27.15

Data & Civil Rights: Biometric Technologies in Policing

Robyn Caplan, Ifeoma Ajunwa, Alex Rosenblat, danah boyd

Biometric technologies are rapidly finding use in a variety of policing contexts, and their use is expected to grow as these technologies become more accurate, cost-effective and accessible to law enforcement agencies. Since 2008, the FBI has been assembling a new biometrics database, the Next Generation Identification system (NGI), since 2008. This $1 billion program will combine fingerprints, iris scans, facial recognition, voice data and other biometrics into a multimodal database, greatly expanding the amount of data searchable by federal and state agencies. Other existing biometric databases such as the National DNA Index System may be interoperable with this system. At the same time, new technologies, as well as new laws and regulations, have widened the conditions under which law enforcement agencies can collect, store, and share biometric data.

This document is a workshop primer from Data & Civil Rights: A New Era of Policing and Justice.


primer | 10.27.15

Data & Civil Rights: Predictive Policing

Sarah Brayne, Alex Rosenblat, danah boyd

Predictive policing refers to the use of analytical techniques to make statistical predictions about potential criminal activity. The basic underlying assumption of predictive policing is that crime is not randomly distributed across people and places, holding that big data can be used to forecast when and where crimes may be more likely to occur, and which individuals are likely to be victims or perpetrators of crimes.

This document is a workshop primer from Data & Civil Rights: A New Era of Policing and Justice.


primer | 07.01.15

Peer-to-Peer Lending

Alexandra Mateescu

“The collapse of the financial system starting in 2008 shattered public confidence in the traditional intermediaries of the financial system – the regulated banks. Not only did the mainstream financial system implode leaving millions of borrowers baring an extraordinary debt burden, the contraction that followed left individuals and small businesses cut off from fresh sources of credit. “Disintermediation,” the idea that we can have credit without banks, became a political rallying cry for those interested in reforming the financial system to better serve the interests of consumers. As the Financial Times has put it, peer-to-peer lending companies offered to “revolutionize credit by cutting out, or disintermediating, banks from the traditional lending process.” Although the amount of credit available through peer-to-peer lending is miniscule in comparison to traditional credit, the public attention given to this phenomenon is significant.”

This primer maps the peer-to-peer/marketplace lending ecosystem in order to ground the Data & Fairness initiative’s investigations into its benefits and challenges and potential for fairness and discrimination.


primer | 06.22.15

Data, Human Rights & Human Security

Mark Latonero, Zachary Gold

“In today’s global digital ecosystem, mobile phone cameras can document and distribute images of physical violence. Drones and satellites can assess disasters from afar. Big data collected from social media can provide real-time awareness about political protests. Yet practitioners, researchers, and policymakers face unique challenges and opportunities when assessing technological benefit, risk, and harm. How can these technologies be used responsibly to assist those in need, prevent abuse, and protect people from harm?”

Mark Latonero and Zachary Gold address the issues in this primer for technologists, academics, business, governments, NGOs, intergovernmental organizations — anyone interested in the future of human rights and human security in a data-saturated world.


primer | 10.30.14

Data & Civil Rights: Criminal Justice Primer

Alex Rosenblat, Kate Wikelius, danah boyd, Seeta Peña Gangadharan, Corrine Yu

Discrimination and racial disparities persist at every stage of the U.S. criminal justice system, from policing to trials to sentencing. The United States incarcerates a higher percentage of its population than any of its peer countries, with 2.2 million people behind bars. The criminal justice system disproportionately harms communities of color: while they make up 30 percent of the U.S. population, they represent 60 percent of the incarcerated population. There has been some discussion of how “big data” can be used to remedy inequalities in the criminal justice system; civil rights advocates recognize potential benefits but remained fundamentally concerned that data-oriented approaches are being designed and applied in ways that also disproportionately harms those who are already marginalized by criminal justice processes.

This document is a workshop primer from Data & Civil Rights: Why “Big Data” is a Civil Rights Issue.


primer | 10.30.14

Data & Civil Rights: Education Primer

Andrea Alarcon, Elana Zeide, Alex Rosenblat, Kate Wikelius, danah boyd, Seeta Peña Gangadharan, Corrine Yu

Many education reformers see the merging of student data, predictive analytics, processing tools, and technology-based instruction as the key to the future of education and a means to further opportunity and equity in education. However, despite widespread discussion of the potential benefits and costs of using data in educational reform, it is difficult to determine who benefits from reforms since there has been little assessment of these programs and few oversight mechanisms.

This document is a workshop primer from Data & Civil Rights: Why “Big Data” is a Civil Rights Issue.


primer | 10.30.14

Data & Civil Rights: Consumer Finance Primer

Alex Rosenblat, Rob Randhava, danah boyd, Seeta Peña Gangadharan, Corrine Yu

New data analytics tools, predictive technologies, and an increasingly available range of data sources have enabled new financial instruments and services to be developed, but access to high-quality services remains restricted, often along racial and socio-economic class lines. How data is used and how algorithms and scores are designed have the potential to minimize or maximize discrimination and inequity. Yet, because of the complexity of many of these systems, developing mechanisms of oversight and accountability is extremely challenging. Not only is there little transparency for those being assessed, but the very nature of the new types of algorithms being designed makes it difficult for those with technical acumen to truly understand what is unfolding and why. This raises significant questions for those invested in making certain that finance and pricing are fair.

This document is a workshop primer from Data & Civil Rights: Why “Big Data” is a Civil Rights Issue.


primer | 10.30.14

Data & Civil Rights: Housing Primer

Alex Rosenblat, Kate Wikelius, danah boyd, Seeta Peña Gangadharan, Corrine Yu

Data has always played an important role in housing policies, practices, and financing. Housing advocates worry that new sources of data are being used to extend longstanding discriminatory practices, particularly as it affects those who have access to credit for home ownership as well as the ways in which the rental market is unfolding. Open data practices, while potentially shedding light on housing inequities, are currently more theoretical than actionable. Far too little is known about the ways in which data analytics and other data-related practices may expand or relieve inequities in housing.

This document is a workshop primer from Data & Civil Rights: Why “Big Data” is a Civil Rights Issue.


primer | 10.30.04

Data & Civil Rights: Employment Primer

Alex Rosenblat, Kate Wikelius, danah boyd, Seeta Peña Gangadharan, Corrine Yu

The complexity of hiring algorithms which fold all kinds of data into scoring systems make it difficult to detect and therefore challenge hiring decisions, even when outputs appear to disadvantage particular groups within a protected class. When hiring algorithms weigh many factors to reach an unexplained decision, job applicants and outside observers are unable to detect and challenge factors that may have a disparate impact on protected groups.

This document is a workshop primer from Data & Civil Rights: Why “Big Data” is a Civil Rights Issue.


primer | 10.30.14

Data & Civil Rights: Technology Primer

Solon Barocas, Alex Rosenblat, danah boyd, Seeta Peña Gangadharan, Corrine Yu

Data have assumed a significant role in routine decisions about access, eligibility, and opportunity across a variety of domains. These are precisely the kinds of decisions that have long been the focus of civil rights campaigns. The results have been mixed. Companies draw on data in choosing how to focus their attention or distribute their resources, finding reason to cater to some of its customers while ignoring others. Governments use data to enhance service delivery and increase transparency, but also to decide whom to subject to special scrutiny, sanction, or punishment. The technologies that enable these applications are sometimes designed with a particular practice in mind, but more often are designed more abstractly, such that technologists are often unaware of and not testing for the ways in which they might benefit some and hurt others.

The technologies and practices that are driving these shifts are often described under the banner of “big data.” This concept is both vague and controversial, particularly to those engaged in the collection, cleaning, manipulation, use, and analysis of data. More often than not, the specific technical mechanisms that are being invoked fit under a different technical banner: “data mining.”

Data mining has a long history in many industries, including marketing and advertising, banking and finance, and insurance. As the technologies have become more affordable and the availability of data has increased, both public and private sectors—as well as civil society—are envisioning new ways of using these techniques to wrest actionable insights from once intractable datasets. The discussion of these practices has prompted fear and anxiety as well as hopes and dreams. There is a significant and increasing gap in understanding between those who are and are not technically fluent, making conversations about what’s happening with data challenging. That said, it’s important to understand that transparency and technical fluency is not always enough. For example, those who lack technical understanding are often frustrated because they are unable to provide oversight or determine the accuracy of what is produced while those who build these systems realize that even they cannot meaningfully assess the product of many algorithms.

This primer provides a basic overview to some of the core concepts underpinning the “big data” phenomenon and the practice of data mining. The purpose of this primer is to enable those who are unfamiliar with the relevant practices and technical tools to at least have an appreciation for different aspects of what’s involved.

This document is a workshop primer from Data & Civil Rights: Why “Big Data” is a Civil Rights Issue.


primer | 10.30.14

Data & Civil Rights: Health Primer

Alex Rosenblat, Kate Wikelius, danah boyd, Seeta Peña Gangadharan, Corrine Yu

Data plays a central role in both medicine and insurance, enabling advances and creating new challenges. Although legislative efforts have attempted to protect the privacy of people’s health data, many other kinds of data can reveal sensitive health information about an individual. People’s medical conditions or health habits can be inferred from many sources, including their purchases, phone call patterns, fitness tracking apps, posts on social media, and browsing histories. Sometimes, medical information that reveals sensitive information about an individual can be linked to the medical state of a relative. However, accuracy of these inferences may be a problem, and inaccurate inference can result in social stigma and harmful reputational effects on the wrongly categorized individual. In addition, the kinds of inferences generated and used by marketers and insurance companies may not be useful when applied to the context of patient care. Not only does misuse of data have consequences for individuals seeking fair access to healthcare, but inappropriate practices also erode productive efforts to use data to empower people, personalize medicine, and develop innovations that can advance healthcare.

This document is a workshop primer from Data & Civil Rights: Why “Big Data” is a Civil Rights Issue.


primer | 10.08.14

Future of Labor: Networked Employment Discrimination

Alex Rosenblat, Tamara Kneese, danah boyd

As businesses begin implementing algorithms to sort through applicants and use third party services to assess the quality of candidates based on their networks, personality tests, and other scores, how do we minimize the potential discriminatory outcomes of such hiring processes?

This document was produced as a part of the Future of Work Project at Data & Society Research Institute. This effort is supported by the Open Society Foundations’ U.S. Programs Future of Work inquiry, which is bringing together a cross-disciplinary and diverse group of thinkers to address some of the biggest questions about how work is transforming and what working will look like 20-30 years from now. The inquiry is exploring how the transformation of work, jobs and income will affect the most vulnerable communities, and what can be done to alter the course of events for the better.


primer | 10.08.14

Future of Labor: Technologically Mediated Artisanal Production

Tamara Kneese, Alex Rosenblat, danah boyd

From 3D printing to maker culture, there’s a rise of technical practices that resist large industrial and corporate modes of production, similar to what is occurring in artisanal food and agriculture. While DIY practices are not new, the widespread availability and cheap cost of such tools has the potential to disrupt certain aspects of manufacturing. How do we better understand what is unfolding?

This document was produced as a part of the Future of Work Project at Data & Society Research Institute. This effort is supported by the Open Society Foundations’ U.S. Programs Future of Work inquiry, which is bringing together a cross-disciplinary and diverse group of thinkers to address some of the biggest questions about how work is transforming and what working will look like 20-30 years from now. The inquiry is exploring how the transformation of work, jobs and income will affect the most vulnerable communities, and what can be done to alter the course of events for the better.


Unionization emerged as a way of protecting labor rights when society shifted from an agricultural ecosystem to one shaped by manufacturing and industrial labor. New networked work complicates the organizing mechanisms that are inherent to unionization. How then do we protect laborers from abuse, poor work conditions, and discrimination?

This document was produced as a part of the Future of Work Project at Data & Society Research Institute. This effort is supported by the Open Society Foundations’ U.S. Programs Future of Work inquiry, which is bringing together a cross-disciplinary and diverse group of thinkers to address some of the biggest questions about how work is transforming and what working will look like 20-30 years from now. The inquiry is exploring how the transformation of work, jobs and income will affect the most vulnerable communities, and what can be done to alter the course of events for the better.


primer | 10.08.14

Future of Labor: Workplace Surveillance

Alex Rosenblat, Tamara Kneese, danah boyd

Employers have long devised techniques and used new technologies to surveil employees in order to increase efficiency, decrease theft, and otherwise assert power and control over subordinates. New and cheaper networked technologies make surveillance easier to implement, but what are the ramifications of widespread workplace surveillance?

This document was produced as a part of the Future of Work Project at Data & Society Research Institute. This effort is supported by the Open Society Foundations’ U.S. Programs Future of Work inquiry, which is bringing together a cross-disciplinary and diverse group of thinkers to address some of the biggest questions about how work is transforming and what working will look like 20-30 years from now. The inquiry is exploring how the transformation of work, jobs and income will affect the most vulnerable communities, and what can be done to alter the course of events for the better.


primer | 10.08.14

Future of Labor: Understanding Intelligent Systems

Alex Rosenblat, Tamara Kneese, danah boyd

Science fiction has long imagined a workforce reshaped by robots, but the increasingly common instantiation of intelligent systems in business is much more mundane. Beyond the utopian and dystopian hype of increased efficiencies and job displacement, how do we understand what disruptions intelligent systems will have on the workforce?

This document was produced as a part of the Future of Work Project at Data & Society Research Institute. This effort is supported by the Open Society Foundations’ U.S. Programs Future of Work inquiry, which is bringing together a cross-disciplinary and diverse group of thinkers to address some of the biggest questions about how work is transforming and what working will look like 20-30 years from now. The inquiry is exploring how the transformation of work, jobs and income will affect the most vulnerable communities, and what can be done to alter the course of events for the better.


Data-oriented systems are inferring relationships between people based on genetic material, behavioral patterns (e.g., shared geography imputed by phone carriers), and performed associations (e.g., “friends” online or shared photographs). What responsibilities do entities who collect data that imputes connections have to those who are implicated by association? For example, as DNA and other biological materials are collected outside of medicine (e.g., at point of arrest, by informatics services like 23andme, for scientific inquiry), what rights do relatives (living, dead, and not-yet-born) have? In what contexts is it acceptable to act based on inferred associations and in which contexts is it not?

This document is a workshop primer from The Social, Cultural & Ethical Dimensions of “Big Data”.


primer | 03.17.14

Primer: Data Supply Chains

Data & Society

As data moves between actors and organizations, what emerges is a data supply chain. Unlike manufacturing supply chains, transferred data is often duplicated in the process, challenging the essence of ownership. What does ethical data labor look like? How are the various stakeholders held accountable for being good data guardians? What does clean data transfer look like? What kinds of best practices can business and government put into place? What upstream rights to data providers have over downstream commercialization of their data?

This document is a workshop primer from The Social, Cultural & Ethical Dimensions of “Big Data”.


The availability of data is not evenly distributed. Some organizations, agencies, and sectors are better equipped to gather, use, and analyze data than others. If data is transformative, what are the consequences of defense and security agencies having greater capacity to leverage data than, say, education or social services? Financial wherewithal, technical capacity, and political determinants all affect where data is employed. As data and analytics emerge, who benefits and who doesn’t, both at the individual level and the institutional level? What about the asymmetries between those who provide the data and those who collect it? How does uneven data access affect broader issues of inequality? In what ways does data magnify or combat asymmetries in power?

This document is a workshop primer from The Social, Cultural & Ethical Dimensions of “Big Data”.


Accountability is fundamentally about checks and balances to power. In theory, both government and corporations are kept accountable through social, economic, and political mechanisms. Journalism and public advocates serve as an additional tool to hold powerful institutions and individuals accountable. But in a world of data and algorithms, accountability is often murky. Beyond questions about whether the market is sufficient or governmental regulation is necessary, how should algorithms be held accountable? For example what is the role of the fourth estate in holding data-oriented practices accountable?

This document is a workshop primer from The Social, Cultural & Ethical Dimensions of “Big Data”.


Countless highly accurate predictions can be made from trace data, with varying degrees of personal or societal consequence (e.g., search engines predict hospital admission, gaming companies can predict compulsive gambling problems, government agencies predict criminal activity). Predicting human behavior can be both hugely beneficial and deeply problematic depending on the context. What kinds of predictive privacy harms are emerging? And what are the implications for systems of oversight and due process protections? For example, what are the implications for employment, health care and policing when predictive models are involved? How should varied organizations address what they can predict?

This document is a workshop primer from The Social, Cultural & Ethical Dimensions of “Big Data”.


Just because data can be made more accessible to broader audiences does not mean that those people are equipped to interpret what they see. Limited topical knowledge, statistical skills, and contextual awareness can prompt people to read inferences into, be afraid of, and otherwise misinterpret the data they are given. As more data is made more available, what other structures and procedures need to be in place to help people interpret what’s available?

This document is a workshop primer from The Social, Cultural & Ethical Dimensions of “Big Data”.


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