The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good

A theoretical essay on socio-technical systems design thinking in the era of digital transformation

Digital technology is here to stay. Currently, digital technologies are unleashing the fourth industrial revolution. This so-called digital transformation is about the integration of digital technology into all areas of society. Within organisations, work is fundamentally changing which impacts how organisations will operate and deliver value to customers. Furthermore, but often forgotten, it is also about a cultural change that requires organisations to continually challenge their status quo, experiment, and get comfortable with failure.

Digital possibilities are emerging which cannot be viewed separately from social effects in organised (eco-)systems and for people in those systems. The challenge is to jointly optimise technical and social aspects for creating both added value in a sustainable manner and improve quality of working life. As we have an ‘organisational choice’, technical possibilities can be aligned with social needs and requirements, and vice versa. This alignment forms the basis of socio-technical systems (STS) thinking, which is necessary for developing sustainable organisational solutions. Sociotechnical theory and practice originally have a focus on optimising social and technical aspects in organisations. Therefore, we choose in this essay for an STS perspective, especially for the STS Design (STS-D) approach which is elaborated by the Lowlands STS school of thought. As digital technologies offer new affordances and constraints for organisational design, we aim, with this essay, to merge STS‑D with digital thinking.

We start with a brief sketch of the understanding of current digital technologies. After this, we discuss organisational design in terms of the division of labour and the penetration of digital technology into the nature of work. Then, the STS-D’s core design principles and design sequence, specifically from the Lowlands school of thought, are introduced and adapted for digital thinking. This is followed by a section on design routines for unlocking the potential for designing future, digital-receptive workplaces and organisations. We end the essay with some closing remarks and reflections.

Responsible Governance for a Food and Nutrition E-Infrastructure: Case Study of the Determinants and Intake Data Platform

The focus of the current paper is on a design of responsible governance of food consumer science e-infrastructure using the case study Determinants and Intake Data Platform (DI Data Platform). One of the key challenges for implementation of the DI Data Platform is how to develop responsible governance that observes the ethical and legal frameworks of big data research and innovation, whilst simultaneously capitalizing on huge opportunities offered by open science and the use of big data in food consumer science research. We address this challenge with a specific focus on four key governance considerations: data type and technology; data ownership and intellectual property; data privacy and security; and institutional arrangements for ethical governance. The paper concludes with a set of responsible research governance principles that can inform the implementation of DI Data Platform, and in particular: consider both individual and group privacy; monitor the power and control (e.g., between the scientist and the research participant) in the process of research; question the veracity of new knowledge based on big data analytics; understand the diverse interpretations of scientists' responsibility across different jurisdictions.

Research and Practice of AI Ethics: A Case Study Approach Juxtaposing Academic Discourse with Organisational Reality

This study investigates the ethical use of Big Data and Artificial Intelligence (AI) technologies (BD + AI)-using an empirical approach. The paper categorises the current literature and presents a multi-case study of 'on-the-ground' ethical issues that uses qualitative tools to analyse findings from ten targeted case-studies from a range of domains. The analysis coalesces identified singular ethical issues, (from the literature), into clusters to offer a comparison with the proposed classification in the literature. The results show that despite the variety of different social domains, fields, and applications of AI, there is overlap and correlation between the organisations' ethical concerns. This more detailed understanding of ethics in AI + BD is required to ensure that the multitude of suggested ways of addressing them can be targeted and succeed in mitigating the pertinent ethical issues that are often discussed in the literature.

Measuring objective and subjective well-being: dimensions and data sources

Well-being is an important value for people’s lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development.

bias goggles: Graph-Based Computation of the Bias of Web Domains Through the Eyes of Users

Ethical issues, along with transparency, disinformation, and bias, are in the focus of our information society. In this work, we propose the bias goggles model, for computing the bias characteristics of web domains to user-defined concepts based on the structure of the web graph. For supporting the model, we exploit well-known propagation models and the newly introduced Biased-PR PageRank algorithm, that models various behaviours of biased surfers. An implementation discussion, along with a preliminary evaluation over a subset of the greek web graph, shows the applicability of the model even in real-time for small graphs, and showcases rather promising and interesting results. Finally, we pinpoint important directions for future work. A constantly evolving prototype of the bias goggles system is readily available.

Responsible Data Governance of Neuroscience Big Data

Current discussions of the ethical aspects of big data are shaped by concerns regarding the social consequences of both the widespread adoption of machine learning and the ways in which biases in data can be replicated and perpetuated. We instead focus here on the ethical issues arising from the use of big data in international neuroscience collaborations. Neuroscience innovation relies upon neuroinformatics, large-scale data collection and analysis enabled by novel and emergent technologies. Each step of this work involves aspects of ethics, ranging from concerns for adherence to informed consent or animal protection principles and issues of data re-use at the stage of data collection, to data protection and privacy during data processing and analysis, and issues of attribution and intellectual property at the data-sharing and publication stages. Significant dilemmas and challenges with far-reaching implications are also inherent, including reconciling the ethical imperative for openness and validation with data protection compliance and considering future innovation trajectories or the potential for misuse of research results. Furthermore, these issues are subject to local interpretations within different ethical cultures applying diverse legal systems emphasising different aspects. Neuroscience big data require a concerted approach to research across boundaries, wherein ethical aspects are integrated within a transparent, dialogical data governance process. We address this by developing the concept of “responsible data governance,” applying the principles of Responsible Research and Innovation (RRI) to the challenges presented by the governance of neuroscience big data in the Human Brain Project (HBP).