Artificial intelligence-based algorithms are, and will likely be, used extensively by public institutions. We argue that decisions made by AI algorithms cannot count as public decisions, namely, decisions that are made in the name of the public, and that this fact bears on the legitimacy and desirability of deploying AI in lieu of public officials. More specifically, the extensive use of AI in public institutions distances the public from decisions and, hence, undermines their authorship over those decisions.

The assumption underlying the analysis inandwas that there are cases where the value of a decision-making process hinges not only on the quality of the output, namely, the quality of the resulting decisions, but on who makes the decisions and, also, how they are being made. Inandit was argued that public decisions are characterized by the fact that they are being made in the name of all and, further, that the legitimacy and the value of such decisions hinge on the agent who makes them. This chapter identifies more concrete ramifications of this observation. More specifically, it argues that to count as being made in our name, decisions must be publicly discussed, openly debated, and, more concretely, satisfy three conditions: transparency, particitability, and challengeability. Decisions that fail to meet these conditions cannot be characterized as genuinely public decisions. The chapter argues that these conditions are often not met (or not sufficiently met) by AI algorithms. Further, it argues that the source of the failure at issue is structural, rather than contingent or transitory.

Introduction

A significant amount of research literature on different aspects of data, information and sustainable development has appeared in the recent past. A quick search on the Web of Science database in May 2023 using the search string (((SDG*) or (sustainability) or (sustainable development))) and (((data) or (information))) produced:

• ■ 32,919 results in the research area of Computer Science, out of which there were 24,608 articles and 565 books

• ■ 8,632 results within the discipline of Information and Library Science, out of which there were 8,353 articles and 252 books.

Although this is not a comprehensive search on the topic, it gives a sense of the volume of research activities around the SDGs in the computer science and library and information science disciplines.

Some publications date back to the 1990s, but research in this area within the library and information science discipline grew rapidly over the past decade – from 202 publications in 2011 to 1052 in 2021 and 1405 in 2022. In addition, there have also been professional research and development activities within the computer and information sciences area, but most of such research focused on sustainability and climate change in the form of the green ICT, green libraries, etc. This chapter discusses some of the academic as well as professional research, especially around data, information, people and society in the context of the SDGs. It tries to point out some key progress and trends, as well as some gaps in research, and highlights some key challenges. Research and development activities, and some of the associated challenges, within the library and information sector are discussed in Chapter 11.

The overall SDG research landscape

A review, based on the research literature appearing in the Web of Science database (Nakamura et al., 2019) shows the growth of research, in terms of publications, on health, and sustainability in which ecosystems and biodiversity appear top of the list. Bautista-Puig et al. (2021) report on a scientometrics study that aimed to investigate the trend of research related to the SDGs around the world. This study covered research between 2000 and 2017, and the topics included both the SDGs and MDGs (Millennium Development Goals), which were the predecessors of the SDGs.

This chapter presents the evolution of the main demographic variables: population growth and its sources, occupational structure, territorial distribution, and educational levels. Each of these four variables are taken separately to provide a long-run, quantitative description and an analysis based on bibliography. The overall image resulting from overlaps and interactions between the variables suggests three distinct “structural periods”. The first period, which covers most of the nineteenth century, featured remarkably stable structures. In contrast, the second period, from around 1890 to 1980, was an era of major transformations, such as the demographic transition, de-agrarianization, urbanization and mass literacy. After 1980 a third period is found in which demographic change has shifted to a different path involving low population growth, tertiarization, new trends in population geography, and longer schooling periods. The new challenges associated with this last period call for active public policies.