Toward Bridging the Data Divide

Curated on Posted on by Stefaan Verhulst

Blog by Randeep Sudan, Craig Hammer, and Yaroslav Eferin: “Developing countries face a data conundrum. Despite more data being available than ever in the world, low- and middle-income countries often lack adequate access to valuable data and struggle to fully use the data they have.

This seemingly paradoxical situation represents a data divide. The terms “digital divide” and “data divide” are often used interchangeably but differ. The digital divide is the gap between those with access to digital technologies and those without access. On the other hand, the data divide is the gap between those who have access to high-quality data and those who do not. The data divide can negatively skew development across countries and therefore is a serious issue that needs to be addressed…

The effects of the data divide are alarming, with low- and middle-income countries getting left behind. McKinsey estimates that 75% of the value that could be created through Generative AI (such as ChatGPT) would be in four areas of economic activity: customer operations, marketing and sales, software engineering, and research and development. They further estimate that Generative AI  could add between $2.6 trillion and $4.4 trillion in value in these four areas.

PWC estimates that approximately 70% of all economic value generated by AI will likely accrue to just two countries: the USA and China. These two countries account for nearly two-thirds of the world’s hyperscale data centers, high rates of 5G adoption, the highest number of AI researchers, and the most funding for AI startups. This situation creates serious concerns for growing global disparities in accessing benefits from data collection and processing, and the related generation of insights and opportunities. These disparities will only increase over time without deliberate efforts to counteract this imbalance…(More)”

Private sector access to public sector personal data: exploring data value and benefit sharing

Curated on Posted on by Stefaan Verhulst

Literature review for the Scottish Government: “The aim of this review is to enable the Scottish Government to explore the issues relevant to the access of public sector personal data (as defined by the European Union General Data Protection Regulation, GDPR) with or by the private sector in publicly trusted ways, to unlock the public benefit of this data. This literature review will specifically enable the Scottish Government to establish whether there are

(I) models/approaches of costs/benefits/data value/benefit-sharing, and

(II) intellectual property rights or royalties schemes regarding the use of public sector personal data with or by the private sector both in the UK and internationally.

In conducting this literature review, we used an adapted systematic review, and undertook thematic analysis of the included literature to answer several questions central to the aim of this research. Such questions included:

  • Are there any models of costs and/or benefits regarding the use of public sector personal data with or by the private sector?
  • Are there any models of valuing data regarding the use of public sector personal data with or by the private sector?
  • Are there any models for benefit-sharing in respect of the use of public sector personal data with or by the private sector?
  • Are there any models in respect of the use of intellectual property rights or royalties regarding the use of public sector personal data with or by the private sector?..(More)”.

Unlocking the value of supply chain data across industries

Curated on Posted on by Stefaan Verhulst

MIT Technology Review Insights: “The product shortages and supply-chain delays of the global covid-19 pandemic are still fresh memories. Consumers and industry are concerned that the next geopolitical climate event may have a similar impact. Against a backdrop of evolving regulations, these conditions mean manufacturers want to be prepared against short supplies, concerned customers, and weakened margins.

For supply chain professionals, achieving a “phygital” information flow—the blending of physical and digital data—is key to unlocking resilience and efficiency. As physical objects travel through supply chains, they generate a rich flow of data about the item and its journey—from its raw materials, its manufacturing conditions, even its expiration date—bringing new visibility and pinpointing bottlenecks.

This phygital information flow offers significant advantages, enhancing the ability to create rich customer experiences to satisfying environmental, social, and corporate governance (ESG) goals. In a 2022 EY global survey of executives, 70% of respondents agreed that a sustainable supply chain will increase their company’s revenue.

For disparate parties to exchange product information effectively, they require a common framework and universally understood language. Among supply chain players, data standards create a shared foundation. Standards help uniquely identify, accurately capture, and automatically share critical information about products, locations, and assets across trading communities…(More)”.

How to improve economic forecasting

Curated on Posted on by Stefaan Verhulst

Article by Nicholas Gruen: “Today’s four-day weather forecasts are as accurate as one-day forecasts were 30 years ago. Economic forecasts, on the other hand, aren’t noticeably better. Former Federal Reserve chair Ben Bernanke should ponder this in his forthcoming review of the Bank of England’s forecasting.

There’s growing evidence that we can improve. But myopia and complacency get in the way. Myopia is an issue because economists think technical expertise is the essence of good forecasting when, actually, two things matter more: forecasters’ understanding of the limits of their expertise and their judgment in handling those limits.

Enter Philip Tetlock, whose 2005 book on geopolitical forecasting showed how little experts added to forecasting done by informed non-experts. To compare forecasts between the two groups, he forced participants to drop their vague weasel words — “probably”, “can’t be ruled out” — and specify exactly what they were forecasting and with what probability. 

That started sorting the sheep from the goats. The simple “point forecasts” provided by economists — such as “growth will be 3.0 per cent” — are doubly unhelpful in this regard. They’re silent about what success looks like. If I have forecast 3.0 per cent growth and actual growth comes in at 3.2 per cent — did I succeed or fail? Such predictions also don’t tell us how confident the forecaster is.

By contrast, “a 70 per cent chance of rain” specifies a clear event with a precise estimation of the weather forecaster’s confidence. Having rigorously specified the rules of the game, Tetlock has since shown how what he calls “superforecasting” is possible and how diverse teams of superforecasters do even better. 

What qualities does Tetlock see in superforecasters? As well as mastering necessary formal techniques, they’re open-minded, careful, curious and self-critical — in other words, they’re not complacent. Aware, like Socrates, of how little they know, they’re constantly seeking to learn — from unfolding events and from colleagues…(More)”.

Data Governance and Policy in Africa

Curated on Posted on by Stefaan Verhulst

This open access book edited by Bitange Ndemo, Njuguna Ndung’u, Scholastica Odhiambo and Abebe Shimeles: “…examines data governance and its implications for policymaking in Africa. Bringing together economists, lawyers, statisticians, and technology experts, it assesses gaps in both the availability and use of existing data across the continent, and argues that data creation, management and governance need to improve if private and public sectors are to reap the benefits of big data and digital technologies. It also considers lessons from across the globe to assess principles, norms and practices that can guide the development of data governance in Africa….(More)”.

Should Computers Decide How Much Things Cost?

Curated on Posted on by Stefaan Verhulst

Article by Colin Horgan: “In the summer of 2012, the Wall Street Journal reported that the travel booking website Orbitz had, in some cases, been suggesting to Apple users hotel rooms that cost more per night than those it was showing to Windows users. The company found that people who used Mac computers spent as much as 30 percent more a night on hotels. It was one of the first high-profile instances where the predictive capabilities of algorithms were shown to impact consumer-facing prices.

Since then, the pool of data available to corporations about each of us (the information we’ve either volunteered or that can be inferred from our web browsing and buying histories) has expanded significantly, helping companies build ever more precise purchaser profiles. Personalized pricing is now widespread, even if many consumers are only just realizing what it is. Recently, other algorithm-driven pricing models, like Uber’s surge or Ticketmaster’s dynamic pricing for concerts, have surprised users and fans. In the past few months, dynamic pricing—which is based on factors such as quantity—has pushed up prices of some concert tickets even before they hit the resale market, including for artists like Drake and Taylor Swift. And while personalized pricing is slightly different, these examples of computer-driven pricing have spawned headlines and social media posts that reflect a growing frustration with data’s role in how prices are dictated.

The marketplace is said to be a realm of assumed fairness, dictated by the rules of competition, an objective environment where one consumer is the same as any other. But this idea is being undermined by the same opaque and confusing programmatic data profiling that’s slowly encroaching on other parts of our lives—the algorithms. The Canadian government is currently considering new consumer-protection regulations, including what to do to control algorithm-based pricing. While strict market regulation is considered by some to be a political risk, another solution may exist—not at the point of sale but at the point where our data is gathered in the first place.

In theory, pricing algorithms aren’t necessarily bad…(More)”.

AI By the People, For the People

Curated on Posted on by Stefaan Verhulst

Article by Billy Perrigo/Karnataka: “…To create an effective English-speaking AI, it is enough to simply collect data from where it has already accumulated. But for languages like Kannada, you need to go out and find more.

This has created huge demand for datasets—collections of text or voice data—in languages spoken by some of the poorest people in the world. Part of that demand comes from tech companies seeking to build out their AI tools. Another big chunk comes from academia and governments, especially in India, where English and Hindi have long held outsize precedence in a nation of some 1.4 billion people with 22 official languages and at least 780 more indigenous ones. This rising demand means that hundreds of millions of Indians are suddenly in control of a scarce and newly-valuable asset: their mother tongue.

Data work—creating or refining the raw material at the heart of AI— is not new in India. The economy that did so much to turn call centers and garment factories into engines of productivity at the end of the 20th century has quietly been doing the same with data work in the 21st. And, like its predecessors, the industry is once again dominated by labor arbitrage companies, which pay wages close to the legal minimum even as they sell data to foreign clients for a hefty mark-up. The AI data sector, worth over $2 billion globally in 2022, is projected to rise in value to $17 billion by 2030. Little of that money has flowed down to data workers in India, Kenya, and the Philippines.

These conditions may cause harms far beyond the lives of individual workers. “We’re talking about systems that are impacting our whole society, and workers who make those systems more reliable and less biased,” says Jonas Valente, an expert in digital work platforms at Oxford University’s Internet Institute. “If you have workers with basic rights who are more empowered, I believe that the outcome—the technological system—will have a better quality as well.”

In the neighboring villages of Alahalli and Chilukavadi, one Indian startup is testing a new model. Chandrika works for Karya, a nonprofit launched in 2021 in Bengaluru (formerly Bangalore) that bills itself as “the world’s first ethical data company.” Like its competitors, it sells data to big tech companies and other clients at the market rate. But instead of keeping much of that cash as profit, it covers its costs and funnels the rest toward the rural poor in India. (Karya partners with local NGOs to ensure access to its jobs go first to the poorest of the poor, as well as historically marginalized communities.) In addition to its $5 hourly minimum, Karya gives workers de-facto ownership of the data they create on the job, so whenever it is resold, the workers receive the proceeds on top of their past wages. It’s a model that doesn’t exist anywhere else in the industry…(More)”.

Public Policy and Technological Transformations in Africa

Curated on Posted on by Stefaan Verhulst

Book edited by Gedion Onyango: “This book examines the links between public policy and Fourth Industrial Revolution (4IR) technological developments in Africa. It broadly assesses three key areas – policy entrepreneurship, policy tools and citizen participation – in order to better understand the interfaces between public policy and technological transformations in African countries. The book presents incisive case studies on topics including AI policies, mobile money, e-budgeting, digital economy, digital agriculture and digital ethical dilemmas in order to illuminate technological proliferation in African policy systems. Its analysis considers the broader contexts of African state politics and governance. It will appeal to students, instructors, researchers and practitioners interested in governance and digital transformations in developing countries…(More)”.

Data Collaboratives: Enabling a Healthy Data Economy Through Partnerships

Curated on Posted on by Stefaan Verhulst

Paper by Stefaan Verhulst (as Part of the Digital Revolution and New Social Contract Program): “…Overcoming data silos is key to addressing these data asymmetries and promoting a healthy data economy. This is equally true of silos that exist within sectors as it is of those among sectors (e.g., between the public and private sectors). Today, there is a critical mismatch between data supply and demand. The data that could be most useful rarely gets applied to the social, economic, cultural, and political problems it could help solve. Data silos, driven in large part by deeply entrenched asymmetries and a growing sense of “ownership,” are stunting the public good potential of data.

This paper presents a framework for responsible data sharing and reuse that could increase sharing between the public and private sectors to address some of the most entrenched asymmetries. Drawing on theoretical and empirical material, we begin by outlining how a period of rapid datafication—the Era of the Zettabyte—has led to data asymmetries that are increasingly deleterious to the public good. Sections II and III are normative. Having outlined the nature and scope of the problem, we present a number of steps and recommendations that could help overcome or mitigate data asymmetries. In particular, we focus on one institutional structure that has proven particularly promising: data collaboratives, an emerging model for data sharing between sectors. We show how data collaboratives could ease the flow of data between the public and private sectors, helping break down silos and ease asymmetries. Section II offers a conceptual overview of data collaboratives, while Section III provides an approach to operationalizing data collaboratives. It presents a number of specific mechanisms to build a trusted sharing ecology….(More)”.

Revisiting the Behavioral Revolution in Economics 

Curated on Posted on by Stefaan Verhulst

Article by Antara Haldar: “But the impact of the behavioral revolution outside of microeconomics remains modest. Many scholars are still skeptical about incorporating psychological insights into economics, a field that often models itself after the natural sciences, particularly physics. This skepticism has been further compounded by the widely publicized crisis of replication in psychology.

Macroeconomists, who study the aggregate functioning of economies and explore the impact of factors such as output, inflation, exchange rates, and monetary and fiscal policy, have, in particular, largely ignored the behavioral trend. Their indifference seems to reflect the belief that individual idiosyncrasies balance out, and that the quirky departures from rationality identified by behavioral economists must offset each other. A direct implication of this approach is that quantitative analyses predicated on value-maximizing behavior, such as the dynamic stochastic general equilibrium models that dominate policymaking, need not be improved.

The validity of these assumptions, however, remains uncertain. During banking crises such as the Great Recession of 2008 or the ongoing crisis triggered by the recent collapse of Silicon Valley Bank, the reactions of economic actors – particularly financial institutions and investors – appear to be driven by herd mentality and what John Maynard Keynes referred to as “animal spirits.”…

The roots of economics’ resistance to the behavioral sciences run deep. Over the past few decades, the field has acknowledged exceptions to the prevailing neoclassical paradigm, such as Elinor Ostrom’s solutions to the tragedy of the commons and Akerlof, Michael Spence, and Joseph E. Stiglitz’s work on asymmetric information (all four won the Nobel Prize). At the same time, economists have refused to update the discipline’s core assumptions.

This state of affairs can be likened to an imperial government that claims to uphold the rule of law in its colonies. By allowing for a limited release of pressure at the periphery of the paradigm, economists have managed to prevent significant changes that might undermine the entire system. Meanwhile, the core principles of the prevailing economic model remain largely unchanged.

For economics to reflect human behavior, much less influence it, the discipline must actively engage with human psychology. But as the list of acknowledged exceptions to the neoclassical framework grows, each subsequent breakthrough becomes a potentially existential challenge to the field’s established paradigm, undermining the seductive parsimony that has been the source of its power.

By limiting their interventions to nudges, behavioral economists hoped to align themselves with the discipline. But in doing so, they delivered a ratings-conscious “made for TV” version of a revolution. As Gil Scott-Heron famously reminded us, the real thing will not be televised….(More)”.