explainer | 02.06.19
Technology enables employers to increasingly monitor their employees. This explainer by Alexandra Mateescu and Aiha Nguyen identifies four current trends in workplace monitoring and surveillance: prediction and flagging tools; biometrics and health data; remote monitoring and time-tracking; and gamification and algorithmic management.
Mateescu and Nguyen consider how each trend impacts workers and workplace dynamics. For instance, freelancers on Upwork can be tracked through their keystrokes, mouse clicks, and screenshots to measure work time for clients. Work that cannot be measured in this way (for example, group brainstorming or long-term planning) may be devalued or go uncompensated.
The authors observe that information asymmetries are deepening as the boundaries of workplace privacy are changing. Tracking metrics like health data, for instance, can make way for discrimination and raises concerns about consent. The type of data employers collect will determine which work is valued, how they evaluate performance, and how workers are classified and compensated.
This explainer from Data & Society provides a basic introductory overview to concepts and current issues around technology’s impact on the workplace. It is being co-released with an explainer on Algorithmic Management in the Workplace. For more coverage of emerging issues in labor and technology, visit Social Instabilities in Labor Futures.
explainer | 02.06.19
This explainer by Alexandra Mateescu and Aiha Nguyen defines algorithmic management and reviews how this concept challenges workers’ rights in sectors, including retail, the service industry, and delivery and logistics. The authors outline existing research on the ways that algorithmic management is manifesting across various labor industries, shifting workplace power dynamics, and putting workers at a disadvantage. It can enable increased surveillance and control while removing transparency.
Defined as “a diverse set of technology tools and techniques that structure the conditions of work and remotely manage workforces,” algorithmic management relies on data collection and worker surveillance to enable automated decision-making in real time. For example, an algorithm might decide and assign servers’ shifts.
Because companies aren’t “directly” managing their workers, algorithmic management makes it easier to classify workers as independent contractors, thus relieving companies of the pressure of providing standard worker benefits. Algorithmic management can provide avenues for bias and discrimination, while making it difficult to hold companies accountable. Companies ultimately benefit and continue to scale operations while cutting costs and labor.
This explainer from Data & Society provides a basic introductory overview to concepts and current issues around technology’s impact on the workplace. It is being co-released with an explainer on Workplace Monitoring & Surveillance. For more coverage of emerging issues in labor and technology, visit Social Instabilities in Labor Futures.
“AI in Context” shows how automated and AI technologies are reconfiguring work in small family-owned farms and grocery stores.
ACM Conference on Fairness, Accountability, and Transparency (FAT*) | 12.05.18
In this paper, authors identify the challenges to integrating fairness into machine learning based systems and suggest next steps.
“In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five “traps” that fair-ML work can fall into even as it attempts to be more context-aware in comparison to traditional data science. We draw on studies of sociotechnical systems in Science and Technology Studies to explain why such traps occur and how to avoid them. Finally, we suggest ways in which technical designers can mitigate the traps through a refocusing of design in terms of process rather than solutions, and by drawing abstraction boundaries to include social actors rather than purely technical ones.”
Data & Society Founder and President danah boyd and Researcher Madeleine Clare Elish lay the groundwork for questioning AI and ethics.
“Without comprehensively accounting for the strengths and weaknesses of technical practices, the work of ethics—which includes weighing the risks and benefits and potential consequences of an AI system—will be incomplete.”
Criminal Justice and Behavior | 11.23.18
Data & Society Fellow Cynthia Conti-Cook and co-authors assess the bias involved in risk assessment tools.
“In the top layer, we identify challenges to fairness within the risk-assessment models themselves. We explain types of statistical fairness and the tradeoffs between them. The second layer covers biases embedded in data. Using data from a racially biased criminal justice system can lead to unmeasurable biases in both risk scores and outcome measures. The final layer engages conceptual problems with risk models: Is it fair to make criminal justice decisions about individuals based on groups?”
Fast Company | 11.20.18
Data & Society 2015-2016 Fellow Wilneida Negron connects her past social work to her current work as a political scientist and technologist.
“We are at the cusp of a new wave of technological thinking, one defined by a new mantra that is the opposite of Zuckerberg’s: ‘Move carefully and purposely, and embrace complexity.’ As part of this wave, a new, inclusive, and intersectional generation of people are using technology for the public interest. This new wave will help us prepare for a future where technical expertise coexists with empathy, humility, and perseverance.”
The Cancer Letter | 11.16.18
As AI becomes integrated into different facets of our lives, Data & Society Researcher Kadija Ferryman joins Robert A. Winn in considering what this means for the field of health.
“How can we bring together the excitement for the possibilities of AI in medicine with the sobering reality of stubborn health disparities that remain despite technological advances?”
In Content or Context Moderation? by Robyn Caplan illustrates the organizational contexts of three types of content moderation strategies by drawing from interviews with 10 major digital platforms.
Data Craft analyzes how bad actors manipulate metadata to create effective disinformation campaigns and provides tips for researchers and technology companies trying to spot this “data craft.”
MIT Technology Review | 10.23.18
Data & Society Health + Data Lead Mary Madden considers what patient privacy means in the current age of technology.
“In the era of data-driven medicine, systems for handling data need to avoid anything that feels like manipulation—whether it’s subtle or overt. At a minimum, the process of obtaining consent should be separated from the process of obtaining care.”
report | 10.17.18
Weaponizing the Digital Influence Machine: The Political Perils of Online Ad Tech identifies the technologies, conditions, and tactics that enable today’s digital advertising infrastructure to be weaponized by political and anti-democratic actors.
Building off the research for her book Uberland: How Algorithms are Rewriting the Rules of Work, Data & Society Researcher Alex Rosenblat explains algorithmic management in the gig-economy.
“Data and algorithms are presented as objective, neutral, even benevolent: Algorithms gave us super-convenient food delivery services and personalized movie recommendations. But Uber and other ride-hailing apps have taken the way Silicon Valley uses algorithms and applied it to work, and that’s not always a good thing.”
SSRN | 10.11.18
How are AI technologies being integrated in health care? What are the broader implications of this integration? Data & Society Researcher Madeleine Clare Elish investigates.
“This paper examines the development of a machine learning-driven sepsis risk detection tool in a hospital Emergency Department in order to interrogate the contingent and deeply contextual ways in which AI technologies are likely be adopted in healthcare.”
In Governing Artificial Intelligence: Upholding Human Rights & Dignity, Mark Latonero shows how human rights can serve as a “North Star” to guide the development and governance of artificial intelligence.
The report draws the connections between AI and human rights; reframes recent AI-related controversies through a human rights lens; and reviews current stakeholder efforts at the intersection of AI and human rights.
Alternative Influence: Broadcasting the Reactionary Right on YouTube presents data from approximately 65 political influencers across 81 channels to identify the “Alternative Influence Network (AIN)”; an alternative media system that adopts the techniques of brand influencers to build audiences and “sell” them political ideology.
points | 09.14.18
On September 13th, Data & Society Founder and President danah boyd gave the keynote speech at the Online News Association Conference. Read the transcript of her talk on Points.
“Now, more than ever, we need a press driven by ideals determined to amplify what is most important for enabling an informed citizenry.”
points | 09.10.18
Increasingly, technology’s impact on infrastructure is becoming a health concern. In this Points piece, Data & Society Researchers Mikaela Pitcan, Alex Rosenblat, Mary Madden, and Kadija Ferryman tease out why this intersection warrants further research.
“However, there is an urgent need to understand the interdependencies between technology, infrastructure and health, and how these relationships affect Americans’ ability to live the healthiest lives possible. How can we support design, decision-making, and governance of our infrastructures in order to ensure more equitable health outcomes for all Americans?”
Slate | 08.27.18
2017-2018 Fellow Jeanna Matthews and Research Analyst Kinjal Dave respond to Deji Olukotun’s story about an algorithmic tennis match.
“The answer can’t be derived from the past alone: It depends on what we collectively decide about the future, about what justice looks like, about leveling the playing field in sports and in life. As in Olukotun’s story, humans and computers will be working together to pick winners and losers. We need to collectively decide on and enforce the rules they will follow. We need the ability to understand, challenge, and audit the decisions. A level playing field won’t be the future unless we insist on it.”
Slate | 08.13.18
Drawing on conclusions from the Data & Society report Beyond Disruption, Researcher Alexandra Mateescu discusses surveillance of domestic care workers online.
“Online marketplaces may not be the root cause of individual employers’ biases, but their design is not neutral. They are built with a particular archetype of what an “entrepreneurial” domestic worker looks like—one who feels at home in the world of apps, social media, and online self-branding—and ultimately replicates and can even exacerbate many of the divisions that came with our predigital workplaces. As platform companies gain growing power over the hiring processes of a whole industry, they will need to actively work against the embedded inequalities in the markets they now mediate.”
Other | 08.09.17
In this article, Data & Society Founder and President danah boyd and Researcher Madeleine Clare Elish break down the “magic” narrative around AI systems.
“‘Big Data’ and ‘artificial intelligence’ have captured the public imagination and are profoundly shaping social, economic, and political spheres. Through an interrogation of the histories, perceptions, and practices that shape these technologies, we problematize the myths that animate the supposed “magic” of these systems.”
NY Daily News | 07.20.18
For the New York Daily News, Data & Society Media Manipulation Researcher Lead Joan Donovan and Research Analyst Brian Friedberg critique Facebook’s stance on hate speech on their platform.
“Through coordination and exploitation, a vocal minority posits itself as a majority, amplifying their messaging with fake accounts and bots to hide their identities. In his implicit defense of conspiracy content on Facebook, Zuckerberg misunderstands the relationship between words and actions. He suggests that it is impossible to assess intent in online conversation.”
report | 07.18.18
This report by Data & Society Researcher Bonnie Tijerina and Michael Zimmer is the culmination of gatherings that brought together different privacy practitioners to discuss digital privacy for libraries.
“While the recent surge in privacy-related activities within the library community is welcome, we see a gap in the conversations we are having about privacy and our digital presence – a knowledge gap, a lack of shared vocabulary, disparate skill sets, and varied understanding. This gap prevents inclusion across the profession and lacks clarity for those responsible for building tools and licensing products.”
On Friday, June 8, the second-annual Future Perfect gathering at Data & Society brought together individuals from a variety of world-building disciplines—from art and fiction to architecture and science—to explore the uses, abuses, and paradoxes of speculative futures.
How can we trace the spread of disinformation by tracking metadata? Data & Society Research Affiliate Amelia Acker explains.
“One way of more fully understanding the data craftwork of disinformation on social media platforms is by reading the metadata just as closely as the algorithms do.”
In an op-ed for The New York Times, Data & Society Researcher Alex Rosenblat shatters the narrative that Uber encapsulates the entire gig-economy.
“But this industry has, until recently, operated largely informally, with jobs secured by word-of-mouth. That’s changing, as employers are increasingly turning to Uber-like services to find nannies, housecleaners and other care workers. These new gig economy companies, while making it easier for some people to find short-term work, have created hardships for others, and may leave many experienced care workers behind.”
In this reading list, Data & Society Researcher Alexandra Mateescu and Postdoctoral Scholar Julia Ticona provide a pathway for deeper investigations into themes such as gender inequality and algorithmic visibility in the gig economy.
“This list is meant for readers of Beyond Disruption who want to dig more deeply into some of the key areas explored in its pages. It isn’t meant to be exhaustive, but rather give readers a jumping off point for their own investigations.”
Drawn from the experiences of U.S. ridehail, care, and cleaning platform workers, “Beyond Disruption” demonstrates how technology reshapes the future of labor.
Data & Society INFRA Lead Ingrid Burrington traces the history of Silicon Valley and its residents.
“Now San Jose has an opportunity to lift up these workers placed at the bottom of the tech industry as much as the wealthy heroes at its top. If Google makes good on the “deep listening” it has promised, and if San Jose residents continue to challenge the company’s vague promises, the Diridon project might stand a chance of putting forth a genuinely visionary alternative to the current way of life in the Santa Clara Valley and the founder-centric, organized-labor-allergic ideology of Silicon Valley. If it does, San Jose might yet justify its claim to be the center of Silicon Valley—if not as its capital, at least as its heart.”
For Points, Data & Society Postdoctoral Scholar Caroline Jack reviews the history of advertising imaginaries.
“The question of what protections ads themselves deserve, and to what degree people deserve to be protected from ads, is ripe for reconsideration.”
In this Medium post, Founder and President danah boyd reflects on the current state of journalism and offers next steps.
“Contemporary propaganda isn’t about convincing someone to believe something, but convincing them to doubt what they think they know.”
points | 06.19.18
Data & Society Research Analyst Melanie Penagos summarizes three blogposts that came as a result of Data & Society’s AI & Human Rights Workshop in April 2018.
“Following Data & Society’s AI & Human Rights Workshop in April, several participants continued to reflect on the convening and comment on the key issues that were discussed. The following is a summary of articles written by workshop attendees Bendert Zevenbergen, Elizabeth Eagen, and Aubra Anthony.”
How will the introduction of AI into the field of medicine affect the doctor-patient relationship? Data & Society Fellow Claudia Haupt identifies some legal questions we should be asking.
“I contend that AI will not entirely replace human doctors (for now) due to unresolved issues in transposing diagnostics to a non-human context, including both limits on the technical capability of existing AI and open questions regarding legal frameworks such as professional duty and informed consent.”
How do people decide what to trust? Data & Society Postdoctoral Scholar Francesca Tripodi shares insights from her research into conservative news practices.
“While not all Christians are conservative nor all conservatives religious, there is a clear connection between how the process of scriptural inference trickles down into conservative methods of inquiry. Favoring the original text of the Constitution is closely tied to the practices of ‘constitutional conservatism,’ and currently members in all three branches of the U.S. government rely on practices of scriptural inference to make important political decisions.”
Washington Journal of Law, Technology & Arts | 06.07.18
Data & Society Data & Human Rights Research Lead Mark Latonero and Zachary Gold look at the history of web crawlers usage and legal issues surrounding that usage.
“This paper discusses the history of web crawlers in courts as well as the uses of such programs by a wide array of actors. It addresses ethical and legal issues surrounding the crawling and scraping of data posted online for uses not intended by the original poster or by the website on which the information is hosted. The article further suggests that stronger rules are necessary to protect the users’ initial expectations about how their data would be used, as well as their privacy.”