Digital Government Revisited – II

Since the early 1980s, Governments have taken a bad rap. Menacing fingerpointing from most quarters ended up on a consensus that loudly declared them personas non-gratas. The 2009 Global Financial Crisis started to turn the tide. At the time, governments once again came to the rescue of capitalism, unveiling gigantic financial packages to prevent critical financial institutions’ failure. Once the recovery started a few years later, Governments took the back seat once more, backed by universal austerity policies that, in hindsight, did more damage than anything else – especially in terms of income and wealth inequality.

The ongoing pandemic has once again demanded the strong intervention of Governments. However, this time around, the crisis is impacting most, if not all, sectors, in addition

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Contentious Politics in the AI Age

Initially touted as revolutionary and progressive in the 1990s, the lightening evolution of digital technologies, running on the coattails of continuous innovation, has been accompanied by the rise of both extreme socio-economic inequalities and loud and widespread populism, nationalism and overt racism. Many countries are undergoing de-democratization processes undergirded by very resilient neoliberalism, while claim-making by conservative political actors has gained considerable ground in the always contentious political arena.

The unexpected and devastating pandemic triggered by the accelerated spread of the SARS-COV-2 virus has put into evidence the real constraints of a now aging and highly monopolistic digital sector. While information and communication tools and platforms are indeed

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Innovation in the Public Sector

I. Introduction

For the last 30 years, relentless technological innovation has seemingly conquered most, if not all, corners of the world. While in its early stages, the focus was on infrastructure and social networks, the latest phase has set its eyes on core productive and financial processes that will undoubtedly have profound socio-economic and environmental impact across the board. Rapidly adapting to the emerging global context is the clarion call for most countries if they want to remain relevant and competitive at the global level.

Many developing countries find themselves in a unique situation. For starters, most innovations and technologies hold a foreign passport and thus need to first travel and then be adopted and adapted to the national context. Having local capacities  –

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Technology and Earth Hacking

Hacking the Sky

Low, angry gray clouds, seemingly non-stop light rain and damp breathing air were hometown weather traits that most bothered me when I was growing up. Like most other children, I had a fascination with airplanes and could spend hours watching them. Going to the airport was one of the coolest things – nowadays not anymore. Planes, however, almost always managed to beat antagonistic weather. The opposite was my case. Bad weather automatically meant no outdoor play, parents reinforcing such terrible predicament. How could we change this, I started wondering.

My solution was simple. Equip a few small planes with some magical powder and get them to spray the menacing and sempiternal clouds. Viola! I could not understand why adults had not come up with such a brilliant idea.

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The Global Centralization of (Dis)Intermediation

Merchants are perhaps the most famous image of an intermediary, the not-so-loved “middleman” that buys cheap, sells dear, and becomes rich doing little work. Even in the supposedly dark Middle Ages, merchants were able to openly operate creating in the process Merchant Guilds that promoted regional trade while protecting members from potential abuses by powerful landlords and countervailing the staunch opposition of the Catholic Church. Merchants and traders are also part of the Greek and Roman empires.

Nevertheless, not every single intermediary is necessarily a merchant. In economics, an intermediary is defined as an agent or enterprise that sits between a product (or service) and the consumer. A supply chain for a given product might indeed have multiple intermediaries that handle the

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Uncertainty and Artificial Intelligence

In a world where perfect information supposedly rules across the board, uncertainty certainly poses a challenge to mainstream economists. While some of the tenets of such assumption have been already addressed – via the theory of information asymmetries and the development of the rational expectations school, for example, uncertainty still poses critical questions.

For starters, uncertainty should not be confused with risk. The latter in a nutshell can be quantified using probability theory. Based on existing data and previous behavior, we could say predict there is a 75 percent chance investments in the stock market can yield a 25 percent reward in say 5 years. This is not the case for uncertainty as here the outcome is entirely unknown. In other words, we have no idea what is going to

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Bitcoin Inequality

In the short and medium term, technology and inequality seemed to be positively correlated. In the long term, however, things are not as clear-cut. With the right policies and democratic institutions in place, technology could become a catalyst to reduce income and wealth inequality. Historical evidence from last century clearly supports this claim. Will digital technologies of the 21st Century follow the same path?

The long-term is still quite a few years away for digital technologies such as AI and blockchains. In this post, I will look at the world of Bitcoin and explore its links to income and wealth inequality. I will assume the Bitcoin network is a country on its own with defined financial ties to the rest of the world mostly via crypto exchanges and miners.

Last May, the total

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Blockchain Mining Revisited

Blockchain mining cannot catch a break when it comes to environmental sustainability. This is especially true for Bitcoin mining which seemingly has an insatiable appetite for electricity. A recent paper suggests that by 2020 Bitcoin mining will consume as much energy as Australia. While these estimates are not exempt from criticism, mining does not appear to be best friends with sustainable development, at least not for now.  An alternative way to look at this issue is to compare Bitcoin’s mining power use to that of cloud-based providers who have now become well-established tech corporations. Such comparison should be made not only in absolute terms (gigawatts) but also in relative fashion by considering, for example, the total population being served by these platforms and networks.

In

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Algorithmic Inequality

Disruptive. One of the attributes that most use to describe in minimalistic terms the potential impact of new and emerging information and communication technologies (ICTs) in society. While its actual meaning can vary from one person to another, disruption is usually linked to dramatic short-term change where old and obsolete technologies, processes and institutions -not to mention people – will be either replaced or purge altogether, all for the best.

Disruption is thus implicitly connected to the concept of progress, especially to its linear version. Here, progress is seen almost like time is in physics: it always goes forward, and it is impossible to go back and say edit the past. Recent research has challenged the linear conception of progress((See for example Amy Allen’s book, The

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Deconstructing the Gender-Equality Paradox in STEM, Part II

In the previous post, I detailed some issues that could help explain in part the gender-equality STEM paradox.

Recap

These can be summarized as follows:

  1.  The Global Gender Gap Index (GGGI) measures gaps not levels. It is thus a relative indicator that takes stock of the gender gap regardless of the level or depth of development.
  2. The four GGGI subindices cannot be larger than 1. Thus, the GGGI does not factor-in cases where women are ahead of men. This is related to the previous point: the aim is to measure gender gaps, not gender levels.
  3. As of 2015, UNESCO STEM data is only available for 59 of the 144 countries included in GGGI. That is, almost 60% of the states are missing in the analysis of the gender-equality STEM paradox. Many low-income and lower-middle income countries

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