A strong and widespread negative reaction to the growing power and influence of large technology companies, particularly those based in Silicon Valley (The Oxford English Dictionary).

Once promised to be society’s great equalizer, communications technology is now facing a backlash due to the role it has in spreading disinformation, privacy breaches, limiting pluralism, and undermining democracy. This phenomenon was predicted by Adrian Woolridge in 2013. In an article at The Economist, Woolridge argued that “the tech elite will join bankers and oilmen in public demonology.”

A 2020 survey conducted by the Knight Foundation and Gallup backs Woolridge’s prediction. The survey finds that there is a significant negative sentiment towards tech companies caused by concerns about how tech companies handle personal data, the spread of misinformation on social media platforms, and their growing influence and power in politics and the general life of their consumers, among other drivers. Some additional findings from the survey include:

  • 74% of Americans are very concerned about the spread of misinformation on the internet.
  • 68% are very concerned about the privacy of personal data stored by internet and technology companies, and 56% are very concerned about hate speech and other abusive or threatening language online.
  • 77% of Americans say major internet and technology companies like Facebook, Google, Amazon, and Apple have too much power.

A majority of tech experts, surveyed by Pew Research, predict “that humans’ use of technology will weaken democracy between now and 2030 due to the speed and scope of reality distortion, the decline of journalism, and the impact of surveillance capitalism.” What underpins this assertion is the argument that:

“The misuse of digital technology to manipulate and weaponize facts affects people’s trust in institutions and each other. That ebbing of trust affects people’s views about whether democratic processes and institutions designed to empower citizens are working.”

However, an overwhelming number of these experts also foresee “significant social and civic innovation between now and 2030 to try to address emerging issues.” One of the conclusions made through this study is that:

“The explosion of data generated by people, gadgetry and environmental sensors will affect the level of social and civic innovation in several potential directions. They argue that the existence of the growing trove of data – and people’s knowledge about its collection – will focus more attention on privacy issues and possibly affect people’s norms and behaviors. In addition, some say the way the data is analyzed will draw more scrutiny of the performance of algorithms and artificial intelligence systems, especially around issues related to whether the outcomes of data use are fair and explainable.”

Many would argue that technology is simply a tool. This tool has the ability to improve lives when utilized the right way. The task at hand is to address these problems in an effective and legitimate manner and to ensure that 21st-century tools deliver on their promise to help society progress.

Data Activism

/ˈdeɪtə ˈæktɪˌvɪzəm/

New social practices enabled by data and technology which aim to create political change (Milan and Gutiérrez).

The large-scale generation of data that has occurred over the past decade has given rise to data activism, defined by Stefania Milan and Miren Gutiérrez, scholars in technology and society at the University of Amsterdam and University of Deusto, as “new social practices rooted in technology and data.” These authors further discuss this term, arguing:

“Data activism indicates social practices that take a critical approach to big data. Examples include the collective mapping and geo-referencing of the messages of victims of natural disasters in order to facilitate disaster relief operations, or the elaboration of open government data for advocacy and campaigning. But data activism also embraces tactics of resistance to massive data collection by private companies and governments, such as the encryption of private communication, or obfuscation tactics that put sand into the data collection machine.

Milan and Gutiérrez further elaborate on these two forms of data activism in their paper “Technopolitics in the Age of Big Data.” Here, they argue all data activism is either proactive or reactive. They state:

“We identify two forms of data activism: proactive data activism, whereby citizens take advantage of the possibilities offered by big data infrastructure for advocacy and social change, and reactive data activism, namely grassroots efforts aimed at resisting massive data collection and protecting users from malicious snooping.”

An example of reactive data activism comes from Media Action Grassroots Network, a network of social justice organizations based in the United States. This network provides digital security training to grassroots activists working on racial justice issues.

An example of proactive data activism is discussed in “Data witnessing: attending to injustice with data in Amnesty International’s Decoders project.” There, author Jonathan Gray, a critical data scholar, examines “what digital data practices at Amnesty International’s Decoders initiative can add to the understanding of witnessing.” According to Gray, witnessing is a concept that has been used in law, religion, and media, among others, to explore the construction of evidence and experience. In this paper, Gray references four data witnessing projects, which are:

“(i) witnessing historical abuses with structured data from digitised documents; (ii) witnessing the destruction of villages with satellite imagery and machine learning; (iii) witnessing environmental injustice with company reports and photographs; and (iv) witnessing online abuse through the classification of Twitter data. These projects illustrate the configuration of experimental apparatuses for witnessing injustices with data.”

Within the more recent context, proactive data activism has several notable examples. Civil rights activists in Zanesville, Ohio used data to demonstrate the inequitable access to clean water between predominantly white communities and black communities. A collection of activists, organizers, and mathematicians formed Data 4 Black Lives to promote justice for Black communities through data and data science. Finally, in an effort to monitor government accountability in providing COVID-19 case data, Indonesian activists created a platform where citizens can independently report COVID-19 cases.



The pooling of expertise, funding, and political will to solve multiple problems with a single investment of time and money (Sawin, 2018).

Co-Director of Climate Interactive, a not-for-profit energy and environment think tank, Elizabeth Sawin wrote an article in Stanford Social Innovation Review (SSIR) on multisolving after a year-long study of the implementation of such approach for climate and health. Defined as a way of solving multiple problems with a single investment of time and money, the multisolving approach brings together stakeholders from different sectors and disciplines to tackle public issues in a cost-efficient manner.

In the article, Sawin provides examples of multisolving that have been implemented in countries across the globe:

In Japan, manufacturing facilities use “green curtains”—living panels of climbing plants—to clean the air, provide vegetables for company cafeterias, and reduce energy use for cooling. A walk-to-school program in the United Kingdom fights a decline in childhood physical activity while reducing traffic congestion and greenhouse gas emissions from transportation. A food-gleaning program staffed by young volunteers and families facing food insecurity in Spain addresses food waste, hunger, and a desire for sustainability.

A Climate Interactive report provides three principles and three practices that can help stakeholders develop multisolving strategy. In the SSIR article, Sawin summarizes those principles into three points. First, she argues that a solution must serve everyone in a system without an exception. Second, she suggests that multisolvers must recognize that problems are multifaceted and that multisolving provides solution to multiple facets of a big issue. Third, Sawin posits that experimentation and learning are key to measuring the success of multisolving.

Further in the article, Sawin also outlined three good multisolving practices. First, she identifies openness to collaboration with actors from different sectors or groups in a society as a critical ingredient in developing a multisolving strategy. Second, Sawin stresses the importance of learning, documenting, and improving to ensure optimal benefits of multisolving for the public. Finally, she argues that communicating the benefits of multisolving to various stakeholders can help generate buy-in for a multisolving project.

In concluding the article, Sawin wrote “[n]one of these multisolving principles or tools, on their own, are revolutionary. They need no new apps or state-of-the-art techniques to work. What makes multisolving unique is that it weaves together these principles and practices in a way that builds over time to create big results.”

Informational Autocrats

/ˌɪnfərˈmeɪʃənəl ˈɔtəˌkræts/

Rulers who control and manipulate information in order to maintain power (Guriev and Treisman, 2019).

Sergei Guriev (Professor of Economics, Sciences Po, Paris) and Daniel Treisman (Professor of Political Science, University of California, Los Angeles) detail in their paper, “Informational Autocrats,” a term for new, more surreptitious type of authoritarian leaders. The authors write:

“In this article, we document the changing characteristics of authoritarian states worldwide. Using newly collected data, we show that recent autocrats employ violent repression and impose official ideologies far less often than their predecessors. They also appear more prone to conceal rather than to publicize cases of state brutality. Analyzing texts of leaders’ speeches, we show that “informational autocrats” favor a rhetoric of economic performance and provision of public services that resembles that of democratic leaders far more than it does the discourse of threats and fear embraced by old-style dictators. Authoritarian leaders are increasingly mimicking democracy by holding elections and, where necessary, falsifying the results.

Today, informational autocrats often employ “cyber troops” to spread disinformation. They specifically target and take advantage of the “uninformed masses”  in order to advance their interests. Guriev and Treisman further argue:

“A key element in our theory of informational autocracy is the gap in political knowledge between the “informed elite” and the general public. While the elite accurately observes the limitations of an incompetent incumbent, the public is susceptible to the ruler’s propaganda. Using individual-level data from the Gallup World Poll, we show that such a gap does indeed exist in many authoritarian states today. Unlike in democracies, where the highly educated are more likely than others to approve of their government, in authoritarian states the highly educated tend to be more critical. The highly educated are also more aware of media censorship than their less-schooled compatriots.”

Separately, Andrea Kendall-Taylor, Erica Frantz, and Joseph Wright, in Foreign Affairs, echo the above suggestion, in that: 

“Dictatorships can also use new technologies to shape public perception of the regime and its legitimacy. Automated accounts (or “bots”) on social media can amplify influence campaigns and produce a flurry of distracting or misleading posts that crowd out opponents’ messaging.”


“Digital tools might even help regimes make themselves appear less repressive and more responsive to their citizens. In some cases, authoritarian regimes have deployed new technologies to mimic components of democracy, such as participation and deliberation.”

Globalization of ideas and technological advances have contributed to creating a hostile environment for traditional and overt dictatorship. At the same time, this combination has also been misused by informational autocrats to advance their own interests. Promoting accountability across all sectors through open government data and algorithmic transparency, for example, can prevent such efforts to control and manipulate information.



A clumsy but temporarily effective solution to a particular problem (Oxford English Dictionary).

The term kludge is often used in the world of computer programming to refer to an inelegant temporary patch intended to solve a problem.

In an article for the Washington Post, Mike Konczal—a fellow at the Roosevelt Institute—discusses how kludges are also found in policymaking. Konczal argues that in a well-intentioned effort to make governing simpler, policymakers tend to adopt simple fixes, instead of policies that would make decision-making process actually simple.

Policies that make decision-making process simple can involve “nudges”—a behavioral economics concept proposed by Richard Thaler and Cass Sunstein. In the article, Konczal writes:

“A simple policy is one that simply “nudges” people into one choice or another using a variety of default rules, disclosure requirements, and other market structures. Think, for instance, of rules that require fast-food restaurants to post calories on their menus, or a mortgage that has certain terms clearly marked in disclosures.

“These sorts of regulations are deemed “choice preserving.” Consumers are still allowed to buy unhealthy fast-food meals or sign up for mortgages they can’t reasonably afford. The regulations are just there to inform people about their choices. These rules are designed to keep the market “free,” where all possibilities are ultimately possible, although there are rules to encourage certain outcomes.”

On the other hand, there are policy “kludges”, which according to Steve Teles—professor of political science at Johns Hopkins University—illustrate the current public policy situation in the United States, reflected in the complexity of the healthcare, education, and environmental protection system, to which Teles further arguesAmerica has chosen to govern itself through more indirect and incoherent policy mechanisms than can be found in any comparable country.” 

According to Teles, these kludges can accumulate to be costly and complex with no clear principles. Continued iteration of policy kludges has increased the transaction costs for individuals to access services, the compliance costs for government and business, and created unequal opportunity for individuals and institutions to benefit from democracy. In Teles’ words, the costs of kludges are outlined as follows:

“The most insidious feature of kludgeocracy is the hidden, indirect, and frequently corrupt distribution of its costs. Those costs can be put into three categories — costs borne by individual citizens, costs borne by the government that must implement the complex policies, and costs to the character of our democracy.”



The belief that technology is always the solution (Broussard, 2018).

Since the beginning of its rise in the late 20th century, digital and computer technology promised to improve many ways in which society operates. Personal computers, mobile phones, and the internet are some of the most ubiquitous examples of technology that have demonstrable capabilities to make lives easier to a certain extent.

However, recent years have shown increasing techlash—defined by The Oxford English Dictionary as “a strong and widespread negative reaction to the growing power and influence of large technology companies, particularly those based in Silicon Valley”—as a response to the harm that technology has helped create. Misinformation, privacy violation, and algorithmic bias are phrases that can often be found in the same sentence as one or more tech companies.

Computer scientist and data journalist Meredith Boussard, who is a professor at New York University, argues that these problems stem from technochauvinism—the belief that technology is always the solution. The summary of her book, Artificial Unintelligence, writes:

“… it’s just not true that social problems would inevitably retreat before a digitally enabled Utopia. To prove her point, she undertakes a series of adventures in computer programming. She goes for an alarming ride in a driverless car, concluding “the cyborg future is not coming any time soon”; uses artificial intelligence to investigate why students can’t pass standardized tests; deploys machine learning to predict which passengers survived the Titanic disaster; and attempts to repair the U.S. campaign finance system by building AI software. If we understand the limits of what we can do with technology, Broussard tells us, we can make better choices about what we should do with it to make the world better for everyone.”

The term technochauvinism is similar to technosolutionism. In that, they both describe the belief that most, if not all, complex issues can be solved with the right computation and engineering. However, the use of “chauvinism” is intentional because part of the criticism is about the rampant gender inequality in the tech industry, which manifest in many ways including algorithmic sexism.

“In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally—hiring, driving, paying bills, even choosing romantic partners—that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology. With this book, she offers a guide to understanding the inner workings and outer limits of technology—and issues a warning that we should never assume that computers always get things right.”



A method of describing the present or the near future by analyzing datasets that are not traditionally included in the analysis (e.g. web searches, reviews, social media data, etc.)

Nowcasting is a term that originates in meteorology, which refers to “the detailed description of the current weather along with forecasts obtained by extrapolation for a period of 0 to 6 hours ahead.” Today, nowcasting is also used in other fields, such as macroeconomics and health, to provide more up-to-date statistics.

Traditionally, macroeconomic statistics are collected on a quarterly basis and released with a substantial lag. For example, GDP data for the euro area “is only available at quarterly frequency and is released six weeks after the close of the quarter.” Further, economic datasets from government agencies such as the US Census Bureau “typically appear only after multi-year lags, and the public-facing versions are aggregated to the county or ZIP code level.

The arrival of the big data era has shown some promise to improve nowcasting. A paper by Edward L. Glaeser, Hyunjin Kim, and Michael Luca presents “evidence that Yelp data can complement government surveys by measuring economic activity in close to real-time, at a granular level, and at almost any geographic scale.” In the paper, the authors concluded:

“Our analyses of one possible data source, Yelp, suggests that these new data sources can be a useful complement to official government data. Yelp can help predict contemporaneous changes in the local economy. It can also provide a snapshot of economic change at the local level. It is a useful addition to the data tools that local policy-makers can access.

“Yet our analysis also highlights the challenges with the idea of replacing the Census altogether at any point in the near future. Government statistical agencies invest heavily in developing relatively complete coverage, for a wide set of metrics. The variation in coverage inherent in data from online platforms make it difficult to replace the role of providing official statistics that government data sources play.

“Ultimately, data from platforms like Yelp –combined with official government statistics – can provide valuable complementary datasets that will ultimately allow for more timely and granular forecasts and policy analyses, with a wider set of variables and more complete view of the local economy.”

Another example comes from the United States Federal Reserve (The Fed), which used data from payroll-processing company ADP to payroll employment. This data is traditionally provided by Current Employment Statistics (CES) survey. Despite being “one of the most carefully conducted measures of labor market activity and uses an extremely large sample, it is still subject to significant sampling error and nonsampling errors.” The Fed sought to improve the reliability of this survey by including data provided by ADP. The study found that combining CES and ADP data “reduces the error inherent in both data sources.”

However, nowcasting using big data comes with some limitations. Several researchers evaluated the accuracy of Google Flu Trends (GFT) in the 2012-2013 and 2013-2014 seasons. GFT uses flu-related google searches to make its prediction. The study found that GFT data showed significant overestimation compared to Centers for Disease Control and Prevention (CDC) flu trends prediction.

Jesse Dunietz wrote in Nautilus describing how to address the limitations of big data and make nowcasting efforts more accurate: 

“But when big data isn’t seen as a panacea, it can be transformative. Several groups, like Columbia University researcher Jeffrey Shaman’s, for example, have outperformed the flu predictions of both the CDC and GFT by using the former to compensate for the skew of the latter. “Shaman’s team tested their model against actual flu activity that had already occurred during the season,” according to the CDC. By taking the immediate past into consideration, Shaman and his team fine-tuned their mathematical model to better predict the future. All it takes is for teams to critically assess their assumptions about their data.”