EU Company Data: State of the Union 2020


Report by OpenCorporates: “… on access to company data in the EU. It’s completely revised, with more detail on the impact that the lack of access to this critical dataset has – on business, on innovation, on democracy, and society.

The results are still not great however:

  • Average score is low
    The average score across the EU in terms of access to company data is just 40 out of 100. This is better than the average score 8 years ago, which was just 23 out of 100, but still very low nevertheless.
  • Some major economies score badly
    Some of the EU’s major economies continue to score very badly indeed, with Germany, for example, scoring just 15/100, Italy 10/100, and Spain 0/100.
  • EU policies undermined
    The report identifies 15 areas where the lack of open company data frustrates, impedes or otherwise has a negative impact on EU policy.
  • Inequalities widened
    The report also identifies how inequalities are further widened by poor access to this critical dataset, and how the recovery from COVID-19 will be hampered by it too.

On the plus side, the report also identifies the EU Open Data & PSI Directive passed last year as potentially game changing – but only if it is implemented fully, and there are significant doubts whether this will happen….(More)”

How data privacy leader Apple found itself in a data ethics catastrophe


Article by Daniel Wu and Mike Loukides: “…Apple learned a critical lesson from this experience. User buy-in cannot end with compliance with rules. It requires ethics, constantly asking how to protect, fight for, and empower users, regardless of what the law says. These strategies contribute to perceptions of trust.

Trust has to be earned, is easily lost, and is difficult to regain….

In our more global, diverse, and rapidly- changing world, ethics may be embodied by the “platinum rule”: Do unto others as they would want done to them. One established field of ethics—bioethics—offers four principles that are related to the platinum rule: nonmaleficence, justice, autonomy, and beneficence.

For organizations that want to be guided by ethics, regardless of what the law says, these principles as essential tools for a purpose-driven mission: protecting (nonmaleficence), fighting for (justice), and empowering users and employees (autonomy and beneficence).

An ethics leader protects users and workers in its operations by using governance best practices. 

Before creating the product, it understands both the qualitative and quantitative contexts of key stakeholders, especially those who will be most impacted, identifying their needs and fears. When creating the product, it uses data protection by design, working with cross-functional roles like legal and privacy engineers to embed ethical principles into the lifecycle of the product and formalize data-sharing agreements. Before launching, it audits the product thoroughly and conducts scenario planning to understand potential ethical mishaps, such as perceived or real gender bias or human rights violations in its supply chain. After launching, its terms of service and collection methods are highly readable and enables even disaffected users to resolve issues delightfully.

Ethics leaders also fight for users and workers, who can be forgotten. These leaders may champion enforceable consumer protections in the first place, before a crisis erupts. With social movements, leaders fight powerful actors preying on vulnerable communities or the public at large—and critically examines and ameliorates its own participation in systemic violence. As a result, instead of last-minute heroic efforts to change compromised operations, it’s been iterating all along.

Finally, ethics leaders empower their users and workers. With diverse communities and employees, they co-create new products that help improve basic needs and enable more, including the vulnerable, to increase their autonomy and their economic mobility. These entrepreneurial efforts validate new revenue streams and relationships while incubating next-generation workers who self-govern and push the company’s mission forward. Employees voice their values and diversify their relationships. Alison Taylor, the Executive Director of Ethical Systems, argues that internal processes should “improve [workers’] reasoning and creativity, instead of short-circuiting them.” Enabling this is a culture of psychological safety and training to engage kindly with divergent ideas.

These purpose-led strategies boost employee performance and retention, drive deep customer loyalty, and carve legacies.

To be clear, Apple may be implementing at least some of these strategies already—but perhaps not uniformly or transparently. For instance, Apple has implemented some provisions of the European Union’s General Data Protection Regulation for all US residents—not just EU and CA residents—including the ability to access and edit data. This expensive move, which goes beyond strict legal requirements, was implemented even without public pressure.

But ethics strategies have major limitations leaders must address

As demonstrated by the waves of ethical “principles” released by Fortune 500 companies and commissions, ethics programs can be murky, dominated by a white, male, and Western interpretation.

Furthermore, focusing purely on ethics gives companies an easy way to “free ride” off social goodwill, but ultimately stay unaccountable, given the lack of external oversight over ethics programs. When companies substitute unaccountable data ethics principles for thoughtful engagement with the enforceable data regulation principles, users will be harmed.

Long-term, without the ability to wave a $100 million fine with clear-cut requirements and lawyers trained to advocate for them internally, ethics leaders may face barriers to buy-in. Unlike their sales, marketing, or compliance counterparts, ethics programs do not directly add revenue or reduce costs. In recessions, these “soft” programs may be the first on the chopping block.

As a result of these factors, we will likely see a surge in ethics-washing: well-intentioned companies that talk ethics, but don’t walk it. More will view these efforts as PR-driven ethics stunts, which don’t deeply engage with actual ethical issues. If harmful business models do not change, ethics leaders will be fighting a losing battle….(More)”.

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)”.