The need for fair and just AI is often related to the possibility of understanding AI itself, in other words, of turning an opaque box into a glass box, as inspectable as possible. Transparency and explainability, however, pertain to the technical domain and to philosophy of science, thus leaving the ethics and epistemology of AI largely disconnected. To remedy this, we propose an integrated approach premised on the idea that a glass-box epistemology should explicitly consider how to incorporate values and (...) other normative considerations, such as intersectoral vulnerabilities, at critical stages of the whole process from design and implementation to use and assessment. To connect ethics and epistemology of AI, we perform a double shift of focus. First, we move from trusting the output of an AI system to trusting the process that leads to the outcome. Second, we move from expert assessment to more inclusive assessment strategies, aiming to facilitate expert and non-expert assessment. Together, these two moves yield a framework usable for experts and non-experts when they inquire into relevant epistemological and ethical aspects of AI systems. We dub our framework ‘epistemology-cum-ethics’ to signal the equal importance of both aspects. We develop it from the vantage point of the designers: how to create the conditions to internalize values into the whole process of design, implementation, use, and assessment of an AI system, in which values (epistemic and non-epistemic) are explicitly considered at each stage and inspectable by every salient actor involved at any moment. (shrink)

Epistemic diversity is the ability or possibility of producing diverse and rich epistemic apparati to make sense of the world around us. In this paper we discuss whether, and to what extent, different conceptions of knowledge—notably as ‘justified true belief’ and as ‘distributed and embodied cognition’—hinder or foster epistemic diversity. We then link this discussion to the widespread move in science and philosophy towards monolingual disciplinary environments. We argue that English, despite all appearance, is no Lingua Franca, and we give (...) reasons why epistemic diversity is also deeply hindered is monolingual contexts. Finally, we sketch a proposal for multilingual academia where epistemic diversity is thereby fostered. (shrink)

In this chapter we address what we call “The-Everybody-Did-It” (TEDI) Syndrome, a symptom for collective negligence. Our main thesis is that the character of scientific communities can be evaluated morally and be found wanting in terms of moral responsibility. Even an epistemically successful scientific community can be morally responsible for consequences that were unforeseen by it and its members and that follow from policy advice given by its individual members. We motivate our account by a critical discussion of a recent (...) proposal by Heather Douglas. We offer three, related criticisms of Douglas’s account. First, she assumes that scientific fields are communicative communities. Second, in a system where the scientific community autonomously sets standards, there is a danger of self-affirming reasoning. Third, she ignores that the character of a scientific community is subject to moral evaluation. We argue that these omissions in Douglas’s theory leave it with no adequate response to TEDI Syndrome. Moreover, we deny that science ought to be characterized by unanimity of belief among its competent practitioners, this leads easily to the vices of close-mindedness and expert-overconfidence. If a scientific community wishes to avoid these vices it should create conditions for an active pluralism when it and its members aspire to the position of rational policy decision-making. (shrink)