The Design of Digital Democracy


Book by Gianluca Sgueo: “Ever-stronger ties between technology, entertainment and design are transforming our relationship with democratic decision-making. When we are online, or when we use digital products and services, we tend to focus more on certain factors like speed of service and user-friendliness, and to overlook the costs – both for ourselves and others. As a result, a widening gap separates our expectations of everything related to digitalization – including government – and the actual practice of democratic governance. Democratic regulators, unable to meet citizens’ demands for tangible, fast and gratifying returns, are seeing the poorest results ever recorded in terms of interest, engagement and retention, despite using the most cutting-edge technologies.

This book explores various aspects of the relationship between democracy, technology and entertainment. These include, on the one hand, the role that digital technology has in strengthening our collective intelligence, nurturing empathic relations between citizens and democratic institutions, and supporting processes of political aggregation, deliberation and collaboration. On the other hand, they comprise the challenges accompanying digital technology for representation, transparency and inclusivity in democratic decision-making.

The book’s main argument is that digital democratic spaces should be redesigned to narrow the gap between the expectations and outcomes of democratic decision-making. It suggests abandoning the notion of digital participatory rights as being fast and easy to enjoy. It also refutes the notion that digital democratic decision-making can only be effective when it delivers rapid and successful responses to the issues of the day, regardless of their complexity.

Ultimately, the success or failure of digital democracy will depend on the ability of public regulators to design digital public spaces with a commitment to complexity, so as to make them appealing, but also effective at engaging citizens…(More)”.

The Legal Singularity


Book by Abdi Aidid and Benjamin Alarie: “…argue that the proliferation of artificial intelligence–enabled technology – and specifically the advent of legal prediction – is on the verge of radically reconfiguring the law, our institutions, and our society for the better.

Revealing the ways in which our legal institutions underperform and are expensive to administer, the book highlights the negative social consequences associated with our legal status quo. Given the infirmities of the current state of the law and our legal institutions, the silver lining is that there is ample room for improvement. With concerted action, technology can help us to ameliorate the problems of the law and improve our legal institutions. Inspired in part by the concept of the “technological singularity,” The Legal Singularity presents a future state in which technology facilitates the functional “completeness” of law, where the law is at once extraordinarily more complex in its specification than it is today, and yet operationally, the law is vastly more knowable, fairer, and clearer for its subjects. Aidid and Alarie describe the changes that will culminate in the legal singularity and explore the implications for the law and its institutions…(More)”.

Data Governance and Policy in Africa


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)”.

What if You Knew What You Were Missing on Social Media?


Article by Julia Angwin: “Social media can feel like a giant newsstand, with more choices than any newsstand ever. It contains news not only from journalism outlets, but also from your grandma, your friends, celebrities and people in countries you have never visited. It is a bountiful feast.

But so often you don’t get to pick from the buffet. On most social media platforms, algorithms use your behavior to narrow in on the posts you are shown. If you send a celebrity’s post to a friend but breeze past your grandma’s, it may display more posts like the celebrity’s in your feed. Even when you choose which accounts to follow, the algorithm still decides which posts to show you and which to bury.

There are a lot of problems with this model. There is the possibility of being trapped in filter bubbles, where we see only news that confirms our existing beliefs. There are rabbit holes, where algorithms can push people toward more extreme content. And there are engagement-driven algorithms that often reward content that is outrageous or horrifying.

Yet not one of those problems is as damaging as the problem of who controls the algorithms. Never has the power to control public discourse been so completely in the hands of a few profit-seeking corporations with no requirements to serve the public good.

Elon Musk’s takeover of Twitter, which he renamed X, has shown what can happen when an individual pushes a political agenda by controlling a social media company.

Since Mr. Musk bought the platform, he has repeatedly declared that he wants to defeat the “woke mind virus” — which he has struggled to define but largely seems to mean Democratic and progressive policies. He has reinstated accounts that were banned because of the white supremacist and antisemitic views they espoused. He has banned journalists and activists. He has promoted far-right figures such as Tucker Carlson and Andrew Tate, who were kicked off other platforms. He has changed the rules so that users can pay to have some posts boosted by the algorithm, and has purportedly changed the algorithm to boost his own posts. The result, as Charlie Warzel said in The Atlantic, is that the platform is now a “far-right social network” that “advances the interests, prejudices and conspiracy theories of the right wing of American politics.”

The Twitter takeover has been a public reckoning with algorithmic control, but any tech company could do something similar. To prevent those who would hijack algorithms for power, we need a pro-choice movement for algorithms. We, the users, should be able to decide what we read at the newsstand…(More)”.

An AI Model Tested In The Ukraine War Is Helping Assess Damage From The Hawaii Wildfires


Article by Irene Benedicto: “On August 7, 2023, the day before the Maui wildfires started in Hawaii, a constellation of earth-observing satellites took multiple pictures of the island at noon, local time. Everything was quiet, still. The next day, at the same, the same satellites captured images of fires consuming the island. Planet, a San Francisco-based company that owns the largest fleet of satellites taking pictures of the Earth daily, provided this raw imagery to Microsoft engineers, who used it to train an AI model designed to analyze the impact of disasters. Comparing before and after the fire photographs, the AI model created maps that highlighted the most devastated areas of the island.

With this information, the Red Cross rearranged its work on the field that same day to respond to the most urgent priorities first, helping evacuate thousands of people who’ve been affected by one of the deadliest fires in over a century. The Hawaii wildfires have already killed over a hundred people, a hundred more remain missing and at least 11,000 people have been displaced. The relief efforts are ongoing 10 days after the start of the fire, which burned over 3,200 acres. Hawaii Governor Josh Green estimated the recovery efforts could cost $6 billion.

Planet and Microsoft AI were able to pull and analyze the satellite imagery so quickly because they’d struggled to do so the last time they deployed their system: during the Ukraine war. The successful response in Maui is the result of a year and a half of building a new AI tool that corrected fundamental flaws in the previous system, which didn’t accurately recognize collapsed buildings in a background of concrete.

“When Ukraine happened, all the AI models failed miserably,” Juan Lavista, chief scientist at Microsoft AI, told Forbes.

The problem was that the company’s previous AI models were mainly trained with natural disasters in the U.S. and Africa. But devastation doesn’t look the same when it is caused by war and in an Eastern European city. “We learned that having one single model that would adapt to every single place on earth was likely impossible,” Lavista said…(More)”.

Driving Excellence in Official Statistics: Unleashing the Potential of Comprehensive Digital Data Governance


Paper by Hossein Hassani and Steve McFeely: “With the ubiquitous use of digital technologies and the consequent data deluge, official statistics faces new challenges and opportunities. In this context, strengthening official statistics through effective data governance will be crucial to ensure reliability, quality, and access to data. This paper presents a comprehensive framework for digital data governance for official statistics, addressing key components, such as data collection and management, processing and analysis, data sharing and dissemination, as well as privacy and ethical considerations. The framework integrates principles of data governance into digital statistical processes, enabling statistical organizations to navigate the complexities of the digital environment. Drawing on case studies and best practices, the paper highlights successful implementations of digital data governance in official statistics. The paper concludes by discussing future trends and directions, including emerging technologies and opportunities for advancing digital data governance…(More)”.

Should Computers Decide How Much Things Cost?


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


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


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)”.

Creating public sector value through the use of open data


Summary paper prepared as part of data.europa.eu: “This summary paper provides an overview of the different stakeholder activities undertaken, ranging from surveys to a focus group, and presents the key insights from this campaign regarding data reuse practices, barriers to data reuse in the public sector and suggestions to overcome these barriers. The following recommendations are made to help data.europa.eu support public administrations to boost open data value creation.

  • When it comes to raising awareness and communication, any action should also contain examples of data reuse by the public sector. Gathering and communicating such examples and use cases greatly helps in understanding the importance of the role of the public sector as a data reuser
  • When it comes to policy and regulation, it would be beneficial to align the ‘better regulation’ activities and roadmaps of the European Commission with the open data publication activities, in order to better explore the internal data needs. Furthermore, it would be helpful to facilitate a similar alignment and data needs analysis for all European public administrations. For example, this could be done by providing examples, best practices and methodologies on how to map data needs for policy and regulatory purposes.
  • Existing monitoring activities, such as surveys, should be revised to ensure that data reuse by the public sector is included. It would be useful to create a panel of users, based on the existing wide community, that could be used for further surveys.
  • The role of data stewards remains central to favouring reuse. Therefore, examples, best practices and methodologies on the role of data stewards should be included in the support activities – not specifically for public sector reusers, but in general…(More)”.