Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing


Book by Ron Kohavi, Diane Tang, and Ya Xu: “Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions.

Learn how to use the scientific method to evaluate hypotheses using controlled experiments Define key metrics and ideally an Overall Evaluation Criterion Test for trustworthiness of the results and alert experimenters to violated assumptions. Build a scalable platform that lowers the marginal cost of experiments close to zero. Avoid pitfalls like carryover effects and Twyman’s law. Understand how statistical issues play out in practice….(More)”.

Corporate Capitalism's Use of Openness: Profit for Free?


Book by Arwid Lund and Mariano Zukerfeld: “This book tackles the concept of openness (as in open source software, open access and free culture), from a critical political economy perspective to consider its encroachment by capitalist corporations, but also how it advances radical alternatives to cognitive capitalism.

Drawing on four case studies, Corporate Capitalism’s Use of Openness will add to discussion on open source software, open access content platforms, open access publishing, and open university courses. These otherwise disparate cases share two fundamental features: informational capitalist corporations base their successful business models on unpaid productive activities, play, attention, knowledge and labour, and do so crucially by resorting to ideological uses of concepts such as “openness”, “communities” and “sharing”.

The authors present potential solutions and alternative regulations to counter these exploitative and alienating business models, and to foster digital knowledge commons, ranging from co-ops and commons-based peer production to state agencies’ platforms. Their research and findings will appeal to students, academics and activists around the world in fields such as sociology, economy, media and communication, library and information science, political sciences and technology studies….(More)”.

Tesco Grocery 1.0, a large-scale dataset of grocery purchases in London


Paper by Luca Maria Aiello, Daniele Quercia, Rossano Schifanella & Lucia Del Prete: “We present the Tesco Grocery 1.0 dataset: a record of 420 M food items purchased by 1.6 M fidelity card owners who shopped at the 411 Tesco stores in Greater London over the course of the entire year of 2015, aggregated at the level of census areas to preserve anonymity. For each area, we report the number of transactions and nutritional properties of the typical food item bought including the average caloric intake and the composition of nutrients.

The set of global trade international numbers (barcodes) for each food type is also included. To establish data validity we: i) compare food purchase volumes to population from census to assess representativeness, and ii) match nutrient and energy intake to official statistics of food-related illnesses to appraise the extent to which the dataset is ecologically valid. Given its unprecedented scale and geographic granularity, the data can be used to link food purchases to a number of geographically-salient indicators, which enables studies on health outcomes, cultural aspects, and economic factors….(More)”.

Who will benefit most from the data economy?


Special Report by The Economist: “The data economy is a work in progress. Its economics still have to be worked out; its infrastructure and its businesses need to be fully built; geopolitical arrangements must be found. But there is one final major tension: between the wealth the data economy will create and how it will be distributed. The data economy—or the “second economy”, as Brian Arthur of the Santa Fe Institute terms it—will make the world a more productive place no matter what, he predicts. But who gets what and how is less clear. “We will move from an economy where the main challenge is to produce more and more efficiently,” says Mr Arthur, “to one where distribution of the wealth produced becomes the biggest issue.”

The data economy as it exists today is already very unequal. It is dominated by a few big platforms. In the most recent quarter, Amazon, Apple, Alphabet, Microsoft and Facebook made a combined profit of $55bn, more than the next five most valuable American tech firms over the past 12 months. This corporate inequality is largely the result of network effects—economic forces that mean size begets size. A firm that can collect a lot of data, for instance, can make better use of artificial intelligence and attract more users, who in turn supply more data. Such firms can also recruit the best data scientists and have the cash to buy the best ai startups.

It is also becoming clear that, as the data economy expands, these sorts of dynamics will increasingly apply to non-tech companies and even countries. In many sectors, the race to become a dominant data platform is on. This is the mission of Compass, a startup, in residential property. It is one goal of Tesla in self-driving cars. And Apple and Google hope to repeat the trick in health care. As for countries, America and China account for 90% of the market capitalisation of the world’s 70 largest platforms (see chart), Africa and Latin America for just 1%. Economies on both continents risk “becoming mere providers of raw data…while having to pay for the digital intelligence produced,” the United Nations Conference on Trade and Development recently warned.

Yet it is the skewed distribution of income between capital and labour that may turn out to be the most pressing problem of the data economy. As it grows, more labour will migrate into the mirror worlds, just as other economic activity will. It is not only that people will do more digitally, but they will perform actual “data work”: generating the digital information needed to train and improve ai services. This can mean simply moving about online and providing feedback, as most people already do. But it will increasingly include more active tasks, such as labelling pictures, driving data-gathering vehicles and perhaps, one day, putting one’s digital twin through its paces. This is the reason why some say ai should actually be called “collective intelligence”: it takes in a lot of human input—something big tech firms hate to admit….(More)”.

Nudge Theory and Decision Making: Enabling People to Make Better Choices


Chapter by Vikramsinh Amarsinh Patil: “This chapter examines the theoretical underpinnings of nudge theory and makes a case for incorporating nudging into the decision-making process in corporate contexts. Nudging and more broadly behavioural economics have become buzzwords on account of the seminal work that has been done by economists and highly publicized interventions employed by governments to support national priorities. Firms are not to be left behind, however. What follows is extensive documentation of such firms that have successfully employed nudging techniques. The examples are segmented by the nudge recipient, namely – managers, employees, and consumers. Firms can guide managers to become better leaders, employees to become more productive, and consumers to stay loyal. However, nudging is not without its pitfalls. It can be used towards nefarious ends and be notoriously difficult to implement and execute. Therefore, nudges should be rigorously tested via experimentation and should be ethically sound….(More)”.

What if you ask and they say yes? Consumers' willingness to disclose personal data is stronger than you think


Grzegorz Mazurek and Karolina Małagocka at Business Horizons: “Technological progress—including the development of online channels and universal access to the internet via mobile devices—has advanced both the quantity and the quality of data that companies can acquire. Private information such as this may be considered a type of fuel to be processed through the use of technologies, and represents a competitive market advantage.

This article describes situations in which consumers tend to disclose personal information to companies and explores factors that encourage them to do so. The empirical studies and examples of market activities described herein illustrate to managers just how rewards work and how important contextual integrity is to customer digital privacy expectations. Companies’ success in obtaining client data depends largely on three Ts: transparency, type of data, and trust. These three Ts—which, combined, constitute a main T (i.e., the transfer of personal data)—deserve attention when seeking customer information that can be converted to competitive advantage and market success….(More)”.

The 2020 Edelman Trust Barometer


Edelman: “The 2020 Edelman Trust Barometer reveals that despite a strong global economy and near full employment, none of the four societal institutions that the study measures—government, business, NGOs and media—is trusted. The cause of this paradox can be found in people’s fears about the future and their role in it, which are a wake-up call for our institutions to embrace a new way of effectively building trust: balancing competence with ethical behavior…

Since Edelman began measuring trust 20 years ago, it has been spurred by economic growth. This continues in Asia and the Middle East, but not in developed markets, where income inequality is now the more important factor. A majority of respondents in every developed market do not believe they will be better off in five years’ time, and more than half of respondents globally believe that capitalism in its current form is now doing more harm than good in the world. The result is a world of two different trust realities. The informed public—wealthier, more educated, and frequent consumers of news—remain far more trusting of every institution than the mass population. In a majority of markets, less than half of the mass population trust their institutions to do what is right. There are now a record eight markets showing all-time-high gaps between the two audiences—an alarming trust inequality…

Distrust is being driven by a growing sense of inequity and unfairness in the system. The perception is that institutions increasingly serve the interests of the few over everyone. Government, more than any institution, is seen as least fair; 57 percent of the general population say government serves the interest of only the few, while 30 percent say government serves the interests of everyone….

Against the backdrop of growing cynicism around capitalism and the fairness of our current economic systems are deep-seated fears about the future. Specifically, 83 percent of employees say they fear losing their job, attributing it to the gig economy, a looming recession, a lack of skills, cheaper foreign competitors, immigrants who will work for less, automation, or jobs being moved to other countries….(More)”.

Tech groups cannot be allowed to hide from scrutiny


Marietje Schaake at the Financial Times: “Technology companies have governments over a barrel. Whether they are maximising traffic flow efficiency, matching pupils with their school preferences, trying to anticipate drought based on satellite and soil data, most governments heavily rely on critical infrastructure and artificial intelligence developed by the private sector. This growing dependence has profound implications for democracy.

An unprecedented information asymmetry is growing between companies and governments. We can see this in the long-running investigation into interference in the 2016 US presidential elections. Companies build voter registries, voting machines and tallying tools, while social media companies sell precisely targeted advertisements using information gleaned by linking data on friends, interests, location, shopping and search.

This has big privacy and competition implications, yet oversight is minimal. Governments, researchers and citizens risk being blindsided by the machine room that powers our lives and vital aspects of our democracies. Governments and companies have fundamentally different incentives on transparency and accountability.

While openness is the default and secrecy the exception for democratic governments, companies resist providing transparency about their algorithms and business models. Many of them actively prevent accountability, citing rules that protect trade secrets.

We must revisit these protections when they shield companies from oversight. There is a place for protecting proprietary information from commercial competitors, but the scope and context need to be clarified and balanced when they have an impact on democracy and the rule of law.

Regulators must act to ensure that those designing and running algorithmic processes do not abuse trade secret protections. Tech groups also use the EU’s General Data Protection Regulation to deny access to company information. Although the regulation was enacted to protect citizens against the mishandling of personal data, it is now being wielded cynically to deny scientists access to data sets for research. The European Data Protection Supervisor has intervened, but problems could recur. To mitigate concerns about the power of AI, provider companies routinely promise that the applications will be understandable, explainable, accountable, reliable, contestable, fair and — don’t forget — ethical.

Yet there is no way to test these subjective notions without access to the underlying data and information. Without clear benchmarks and information to match, proper scrutiny of the way vital data is processed and used will be impossible….(More)”.

One Nation Tracked: An investigation into the smartphone tracking industry


Stuart A. Thompson and Charlie Warzel at the New York Times: “…For brands, following someone’s precise movements is key to understanding the “customer journey” — every step of the process from seeing an ad to buying a product. It’s the Holy Grail of advertising, one marketer said, the complete picture that connects all of our interests and online activity with our real-world actions.

Pointillist location data also has some clear benefits to society. Researchers can use the raw data to provide key insights for transportation studies and government planners. The City Council of Portland, Ore., unanimously approved a deal to study traffic and transit by monitoring millions of cellphones. Unicef announced a plan to use aggregated mobile location data to study epidemics, natural disasters and demographics.

For individual consumers, the value of constant tracking is less tangible. And the lack of transparency from the advertising and tech industries raises still more concerns.

Does a coupon app need to sell second-by-second location data to other companies to be profitable? Does that really justify allowing companies to track millions and potentially expose our private lives?

Data companies say users consent to tracking when they agree to share their location. But those consent screens rarely make clear how the data is being packaged and sold. If companies were clearer about what they were doing with the data, would anyone agree to share it?

What about data collected years ago, before hacks and leaks made privacy a forefront issue? Should it still be used, or should it be deleted for good?

If it’s possible that data stored securely today can easily be hacked, leaked or stolen, is this kind of data worth that risk?

Is all of this surveillance and risk worth it merely so that we can be served slightly more relevant ads? Or so that hedge fund managers can get richer?

The companies profiting from our every move can’t be expected to voluntarily limit their practices. Congress has to step in to protect Americans’ needs as consumers and rights as citizens.

Until then, one thing is certain: We are living in the world’s most advanced surveillance system. This system wasn’t created deliberately. It was built through the interplay of technological advance and the profit motive. It was built to make money. The greatest trick technology companies ever played was persuading society to surveil itself….(More)”.

Between Truth and Power The Legal Constructions of Informational Capitalism


Book by Julie Cohen: “Our current legal system is to a great extent the product of an earlier period of social and economic transformation. From the late nineteenth century through the mid-twentieth century, as accountability for industrial-age harms became a pervasive source of conflict, the U.S. legal system underwent profound, tectonic shifts. Today, ownership of information-age resources and accountability for information-age harms have become pervasive sources of conflict, and different kinds of change are emerging.

In Between Truth and Power, Julie E. Cohen explores the relationships between legal institutions and political and economic transformation. Systematically examining struggles over the conditions of information flow and the design of information architectures and business models, she argues that as law is enlisted to help produce the profound economic and socio-technical shifts that have accompanied the emergence of the informational economy, it is too is transforming in fundamental ways. Drawing on elements from legal theory, science and technology studies, information studies, communication studies and organization studies to develop a complex theory of institutional change, Cohen develops an account of the gradual emergence of legal institutions adapted to the information age and of the power relationships that such institutions reflect and reproduce….(More)”.