I am a co-author on a new paper which appears in Minds and Machines (open access).This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.
Myself and Luciano Floridi have released a new paper on SSRN:
The paper discusses the opportunities and challenges of AI for society and reports the results of a meta analysis, which found that five principles – beneficence, non-maleficence, autonomy, justice, and explicability – undergird the emerging ethics of AI as expressed by leading multistakeholder organisations.
Cowls, Josh, and Schroeder, Ralph (2018). Tweeting All The Way to the White House. In Boczkowski, Pablo & Papacharissi, Zizi. (Eds.). (2018). Trump and the Media. MIT Press.
Myself and Evan Higgins have a new book review in Internet Histories:
The emergence of big data offers not only a potential boon for social scientific inquiry, but also raises distinct epistemological issues for this new area of research. Drawing on interviews conducted with researchers at the forefront of big data research, we offer insight into questions of causal versus correlational research, the use of inductive methods, and the utility of theory in the big data age. While our interviewees acknowledge challenges posed by the emergence of big data approaches, they reassert the importance of fundamental tenets of social science research such as establishing causality and drawing on existing theory. They also discussed more pragmatic issues, such as collaboration between researchers from different fields, and the utility of mixed methods. We conclude by putting the themes emerging from our interviews into the broader context of the role of data in social scientific inquiry, and draw lessons about the future role of big data in research.
Cowls, Josh and Schroeder, Ralph (2015) The Ethics of Given-off versus Captured Data in Digital Social Research. Workshop on Ethics for Studying Sociotechnical Systems in a Big Data World, CSCW 2015, March 2015, Vancouver, B.C., Canada.
This paper proposes new terminology to enhance understanding of how big data can be used for research, in both commercial and academic contexts. We distinguish between data as given-off and data as captured, and draw on insights from interviews conducted with researchers using such data to elaborate on this distinction. We conclude with a series of recommendations for research design and conduct, based on this re-conceptualization of ‘data’ and ‘capta’.
The increasing abundance of data creates new opportunities for communities of interest and communities of practice. We believe that interactive tabletops will allow users to explore data in familiar places such as living rooms, cafés, and public spaces. We propose informal, mobile possibilities for future generations of flexible and portable tabletops. In this paper, we build upon current advances in sensing and in organic user interfaces to propose how tabletops in the future could encourage collaboration and engage users in socially relevant data-oriented activities. Our work focuses on the socio-technical challenges of future democratic deliberation. As part of our vision, we suggest switching from fixed to mobile tabletops and provide two examples of hypothetical interface types: TableTiles and Moldable Displays. We consider how tabletops could foster future civic communities, expanding modes of participation originating in the Greek Agora and in European notions of cafés as locales of political deliberation.
This paper is the product of a workshop that brought together practitioners, researchers, and data experts to discuss how big data is becoming a resource for positive social change in low- and middle-income countries (LMICs). We include in our definition of big data sources such as social media data, mobile phone use records, digitally mediated transactions, online news media sources, and administrative records. We argue that there are four main areas where big data has potential for promoting positive social change: advocacy; analysis and prediction; facilitating information exchange; and promoting accountability and transparency. These areas all have particular challenges and possibilities, but there are also issues shared across them, such as open data and privacy concerns. Big data is shaping up to be one of the key battlefields of our time, and the paper argues that this is therefore an opportune moment for civil society groups in particular to become a larger part of the conversation about the use of big data, since questions about the asymmetries of power involved are especially urgent in these uses in LMICs. Civil society groups are also currently underrepresented in debates about privacy and the rights of technology users, which are dominated by corporations, governments and nongovernmental organizations in the Global North. We conclude by offering some lessons drawn from a number of case studies that represent the current state-of-the-art.
This position paper addresses current debates about data in general, and big data specifically, by examining the ethical issues arising from advances in knowledge production. Typically ethical issues such as privacy and data protection are discussed in the context of regulatory and policy debates. Here we argue that this overlooks a larger picture whereby human autonomy is undermined by the growth of scientific knowledge. To make this argument, we first offer definitions of data and big data, and then examine why the uses of data-driven analyses of human behaviour in particular have recently experienced rapid growth. Next, we distinguish between the contexts in which big data research is used, and argue that this research has quite different implications in the context of scientific as opposed to applied research. We conclude by pointing to the fact that big data analyses are both enabled and constrained by the nature of data sources available. Big data research will nevertheless inevitably become more pervasive, and this will require more awareness on the part of data scientists, policymakers and a wider public about its contexts and often unintended consequences.