Recently, there has been renewed interest in so-called evidence-based policy making. Enticed by the grand promises of Big Data, public officials seem increasingly inclined to experiment with more data-driven forms of governance. But while the rise of Big Data and related consequences has been a major issue of concern across different disciplines, attempts to develop a better understanding of the phenomenon's historical foundations have been rare. This short commentary addresses this gap by situating the current push for numerical evidence within (...) a broader socio-political context, demonstrating how the epistemological claims of Big Data science intersect with specific forms of trust, truth, and objectivity. We conclude by arguing that regulators' faith in numbers can be attributed to a distinct political culture, a representative democracy undermined by pervasive public distrust and uncertainty. (shrink)

Recently, amid growing awareness that computer algorithms are not neutral tools but can cause harm by reproducing and amplifying bias, attempts to detect and prevent such biases have intensified. An approach that has received considerable attention in this regard is the Value Sensitive Design (VSD) methodology, which aims to contribute to both the critical analysis of (dis)values in existing technologies and the construction of novel technologies that account for specific desired values. This article provides a brief overview of the key (...) features of the Value Sensitive Design approach, examines its contributions to understanding and addressing issues around bias in computer systems, outlines the current debates on algorithmic bias and fairness in machine learning, and discusses how such debates could profit from VSD-derived insights and recommendations. Relating these debates on values in design and algorithmic bias to research on cognitive biases, we conclude by stressing our collective duty to not only detect and counter biases in software systems, but to also address and remedy their societal origins. (shrink)

The notion of trustworthy AI has been proposed in response to mounting public criticism of AI systems, in particular with regard to the proliferation of such systems into ever more sensitive areas of human life without proper checks and balances. In Europe, the High-Level Expert Group on Artificial Intelligence has recently presented its Ethics Guidelines for Trustworthy AI. To some, the guidelines are an important step for the governance of AI. To others, the guidelines distract effort from genuine AI regulation. (...) In this chapter, we engage in a critical discussion of the concept of trustworthy AI by probing the concept both on theoretical and practical grounds, assessing its substance and the feasibility of its intent. We offer a concise overview of the guidelines and their vision for trustworthy AI and examine the conceptual underpinnings of trustworthy AI by considering how notions of 'trust' and 'trustworthiness' have been discussed in the philosophical literature. We then discuss several epistemic obstacles and moral requirements when striving to achieve trustworthy AI in practice before concluding with an argument in support of the establishment of a trustworthy AI culture that respects and protects foundational values. (shrink)