Autonomous mechanisms have been proposed to regulate certain aspects of society and are already being used to regulate business organisations. We take seriously recent proposals for algorithmic regulation of society, and we identify the existing technologies that can be used to implement them, most of them originally introduced in business contexts. We build on the notion of 'social machine' and we connect it to various ongoing trends and ideas, including crowdsourced task-work, social compiler, mechanism design, reputation management systems, and social scoring. After showing how all the building blocks of algorithmic regulation are already well in place, we discuss possible implications for human autonomy and social order. The main contribution of this paper is to identify convergent social and technical trends that are leading towards social regulation by algorithms, and to discuss the possible social, political, and ethical consequences of taking this path.

The cyberspace and the development of new technologies, especially intelligent systems using artificial intelligence, present enormous challenges to computer professionals, data scientists, managers and policy makers. There is a need to address professional responsibility, ethical, legal, societal, and policy issues. This paper presents problems and issues relevant to computer professionals and decision makers and suggests a curriculum for a course on ethics, law and policy. Such a course will create awareness of the ethics issues involved in building and using software and artificial intelligence.

Artificial intelligence can work wonders, but often it works in mysterious ways. Machine learning is based on the principle that a software program can analyze a huge set of data and fine-tune its algorithms to detect patterns and come up with solutions that humans may miss. That's how Google DeepMind's Alpha Go AI agent learned to play the ancient game of Go (and other games) well enough to beat expert players. But if programmers and users can't figure out how AI algorithms came up with their results, that black-box behavior can be a cause for concern. It may become impossible to judge whether AI agents have picked up unjustified biases or racial profiling from their data sets.

Mobile and ubiquitous sensing of urban air quality (AQ) has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. A necessary and value-added step towards data-driven sustainable urban management is fine-granular AQ inference, which estimates grid-level pollutant concentrations at every instance of time using AQ data collected from fixed-location and mobile sensors. We present the Mobile Air Pollution Sensing (MAPS) framework, which consists of data preprocessing, urban feature extraction, and AQ inference. This is applied to a case study in Beijing (3,025 square km, 19 June - 16 July 2018), where PM2.5 concentrations measured by 28 fixed monitoring stations and 15 vehicles are fused to infer hourly PM2.5 concentrations in 3,025 1km-by-1km grids. Two machine learning structures, namely Deep Feature Spatial-Temporal Tree (DFeaST-Tree) and Deep Feature Spatial-Temporal Network (DFeaST-Net), are proposed to infer PM2.5 concentrations supported by 62 types of urban data that encompass geography, land use, traffic, public, and meteorology. This allows us to infer fine-granular PM2.5 concentrations based on sparse AQ measurements (less than 5% coverage) with good accuracy (SMAPE<15%, R-square>0.9), while accounting for the regional transport of air pollutants outside the study area. In-depth discussions are provided on the heterogeneity of fixed and mobile data sources, spatial coverage of mobile sensing, and importance of urban features for inferring PM2.5 concentrations.

Artificial intelligence (AI) is proving to be a double-edged sword. While this can be said of most new technologies, both sides of the AI blade are far sharper, and neither is well understood. These technologies are starting to improve our lives in myriad ways, from simplifying our shopping to enhancing our healthcare experiences. Their value to businesses also has become undeniable: nearly 80 percent of executives at companies that are deploying AI recently told us that they're already seeing moderate value from it. Although the widespread use of AI in business is still in its infancy and questions remain open about the pace of progress, as well as the possibility of achieving the holy grail of "general intelligence," the potential is enormous.

Worker activists at Google held a "town hall" on Friday where they alleged that the company regularly retaliates against employees who speak out about workplace problems and announced plans for a "company-wide day of action" on 1 May. The meeting, livestreamed for Google employees in offices around the world, was announced after two of the organizers of the November 2018 global walkout circulated a letter internally alleging they were being punished for their activism. The two employees, Meredith Whittaker and Claire Stapleton, provided further details of their cases during the Friday event. Their statements, along with anonymous reports of retaliation of 11 other Google employees, were published in internal documents seen by the Guardian. "I didn't walk out because I'm against Google, I walked out because I'm for it – because I wanted to make it better," Stapleton said in her written statement.

The Artificial Intelligence paradigm (hereinafter referred to as "AI") builds on the analysis of data able, among other things, to snap pictures of the individuals' behaviors and preferences. Such data represent the most valuable currency in the digital ecosystem, where their value derives from their being a fundamental asset in order to train machines with a view to developing AI applications. In this environment, online providers attract users by offering them services for free and getting in exchange data generated right through the usage of such services. This swap, characterized by an implicit nature, constitutes the focus of the present paper, in the light of the disequilibria, as well as market failures, that it may bring about. We use mobile apps and the related permission system as an ideal environment to explore, via econometric tools, those issues. The results, stemming from a dataset of over one million observations, show that both buyers and sellers are aware that access to digital services implicitly implies an exchange of data, although this does not have a considerable impact neither on the level of downloads (demand), nor on the level of the prices (supply). In other words, the implicit nature of this exchange does not allow market indicators to work efficiently. We conclude that current policies (e.g. transparency rules) may be inherently biased and we put forward suggestions for a new approach.

In China, blockchain technology is increasingly employed to settle court cases, local news outlet Global Times reported on April 25. Speaking at the 2019 Forum on China Intellectual Property Protection, Zhang Wen, president of the Beijing Internet Court -- which was established in September 2018, and has since processed 14,904 cases -- reportedly said that the court employs technologies such as artificial intelligence (AI) and blockchain to render judgement. Zhang reportedly told the Global Times that "of the 41 cases concluded [with blockchain technology] so far, parties chose to settle out of court rather than litigate in 40 cases with compelling evidence from blockchain. He also noted that the court had deployed blockchain in 58 cases to collect and provide evidence. "In the current use of AI as an assistant to make rulings, efficiency is prioritized over accuracy.

We present ARCHANGEL; a novel distributed ledger based system for assuring the long-term integrity of digital video archives. First, we describe a novel deep network architecture for computing compact temporal content hashes (TCHs) from audio-visual streams with durations of minutes or hours. Our TCHs are sensitive to accidental or malicious content modification (tampering) but invariant to the codec used to encode the video. This is necessary due to the curatorial requirement for archives to format shift video over time to ensure future accessibility. Second, we describe how the TCHs (and the models used to derive them) are secured via a proof-of-authority blockchain distributed across multiple independent archives. We report on the efficacy of ARCHANGEL within the context of a trial deployment in which the national government archives of the United Kingdom, Estonia and Norway participated.

Samuel Greengard is an author and journalist based in West Linn, OR, USA.