In this sequel post, I will look at the various components of the UNDESA e-government index and then introduce the EIU democracy index to explore potential interlinks between the two,
The e-government development index (EGDI) comprises three distinct components 1. Online services. 2. Telecom infrastructure. And 3. Human capital. While the last two are obtained from external data sources (ITU, UNESCO, UNICEF), the first one is directly developed by the UN. A combination of website checking and a questionnaire sent to UN member states is used to generate the required data – albeit the data is not publicly available. The e-participation index comes from the same source.
The telecom index relies on user access to the Internet, mobiles, analog phones, and broadband. The human
A recent piece in MIT’s Technology Review nicely summarizes the issue of bias in AI/ML (AI) algorithms used in production to make decisions or predictions. The usual suspects make a cameo appearance including data, design and implicit fairness assumptions. But the article falls a bit short as it does not distinguish between bias in general and that which is unique to AI.
Indeed, I was surprised to see the issue of problem framing as the first potential source of AI bias. While this might occur in some cases, this is not an issue that only pertains AI projects and enterprises. For example, large multinational drug companies indeed face a similar challenge. Nowadays, almost none of them are investing in developing new antibiotics to stop the spread of the so-called superbugs nor have any interest
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
In the previous post, I provided a simple definition of an algorithm to then explore their use in the digital world. While algorithms live from the inputs they are feed, digital programs such as mobile apps and web platforms are comprised of a series of algorithms that, working in sync to, deliver the desired output(s). Algorithms sit between a given input and the expected output. They take the former, do their magic and yield the latter.
There is a direct relationship between the complexity of the planned output(s) and the coding effort required. The latter is usually measured by the number of coding lines in a given program. For example, Google is said to have over 2 billion coding lines (2×10^9) supporting its various services. You certainly need an army of programmers to create, manage
While the concept of algorithm has been around for centuries, the same cannot be said about algocracy. The latter has recently gained notoriety thanks in part to the renaissance of Artificial Intelligence and Machine Learning (AI/ML) and is frequently used to describe the increased use of algorithms in decision-making and governance processes. Indeed, the so-called Singularity could be seen as an extreme and seemingly irreversible algocracy case where humans lose the capacity to control superintelligent machines and might even face extinction. Not sure that will ever happen though.
A more plausible scenario takes place when humans and human institutions blindly rely on algorithms to make critical decisions. This is happening today in many sectors – the quasi-dictatorship of algorithms. In
Smart contracts are perhaps one of the most touted features of blockchain technology. While the idea itself dates from the end of last century, blockchains provided the platform for actual implementation in the Internet era. Undoubtedly, Ethereum was the real disruptive innovator by enhancing the original but limited Bitcoin architecture with a plethora of programmable new features, smart contracts being one of them.. This same development also opened the door for clearly distinguishing between blockchains and cryptocurrencies, the latter being just one application of the former, a general purpose technology of sorts.
Analysis of smart contracts can be undertaken from at least three different angles. These are 1. Finance; 2.
The post-WWII era can be arguably defined as the golden age of democratic capitalism – at least from the perspective of developed or industrialized countries. Rebuilding Europe and pumping capital into Japan triggered a long economic boom that lasted until the 1980s – notwithstanding the infamous 1973 oil crisis. The fall of the Berlin Wall in 1989 opened new markets to capital investment and recruited new members to the democracy club thus providing a much needed second wind to the then declining golden age. During that same period, democracy, defined narrowly, continuously expanded in developing countries, including those that became independent nations in the 1960/70s. By the end of the last Millennium analysts and observers were openly speaking about the third wave of democratization,
Open source is one of the core traits of blockchain technology propelling its rapid adoption and growth. The source code from the most popular platforms such as Bitcoin, Ethereum, and Hyperledger Fabric is freely available for download by anyone who wants to play with the technology. Granted, users wishing to deploy and use these platforms must have the required technical skills. While average Internet users might not have such capabilities, companies and startups can find internal capacity or hie external expertise to run and manage their preferred blockchain platform.
Free and Open Source Software (FOSS) has been around for almost three decades. Back in the late 1990s, a war of sorts between FOSS and proprietary software commenced, attracting lots of media attention and generating plenty
A paper on the subject published a couple of weeks ago in the academic journal Psychological Science attracted plenty of attention thanks to some of its surprising conclusions. Its main finding is that, contrary to all expectations, there is an inverse relation between gender equality and the number of women that graduate in Science, Technology, Engineering and Science (STEM). That is, higher gender-equality is correlated to lower female graduation rates in STEM. And vice-versa. How can this be?
In this post, I will explore the issue in more detail. First, I take a quick glance at the data used by the researchers. I then explore some of the nuances of the WEF’s Global Gender Gap Index (GGGI) used to measure gender equality. I conclude with some possible
The Sustainable Development Networking Programme (SDNP) was a UNDP global program that ran between 1992 and 2004. SDNP’s core goal was to enhance access to sustainable development information on a multi-stakeholder basis using new Information and Communication Technologies (ICTs). Its scope of work was driven by Agenda 21, the sustainable development agenda endorsed by UN member countries at the 1992 Earth Summit in Rio de Janeiro.
Agenda 21 was composed of forty chapters, organized under four separate headings. The very last chapter of the agenda called for increased access to information for decision-making as one of the means of implementation of the agenda. Adding to its approach the targets of chapters 27 (strengthening non-government organizations) and 37 (capacity building in developing