Artificial intelligence (AI) systems are increasingly adopted to make decisions in domains such as business, education, health care, and criminal justice. However, such algorithmic decision systems can have prevalent biases against marginalized social groups and undermine social justice. Explainable artificial intelligence (XAI) is a recent development aiming to make an AI system’s decision processes less opaque and to expose its problematic biases. This paper argues against technical XAI, according to which the detection and interpretation of algorithmic bias can be handled (...) more or less independently by technical experts who specialize in XAI methods. Drawing on resources from feminist epistemology, we show why technical XAI is mistaken. Specifically, we demonstrate that the proper detection of algorithmic bias requires relevant interpretive resources, which can only be made available, in practice, by actively involving a diverse group of stakeholders. Finally, we suggest how feminist theories can help shape integrated XAI: an inclusive social-epistemic process that facilitates the amelioration of algorithmic bias. (shrink)

Reluctance to adopt mask-wearing as a preventive measure is widely observed in many Western societies since the beginning of the COVID-19 pandemics. This reluctance toward mask adoption, like any other complex social phenomena, will have multiple causes. Plausible explanations have been identified, including political polarization, skepticism about media reports and the authority of public health agencies, and concerns over liberty, amongst others. In this paper, we propose potential explanations hitherto unnoticed, based on the framework of epistemic injustice. We show how (...) testimonial injustice and hermeneutical injustice may be at work to shape the reluctant mask adoption at both the societal and individual levels. We end by suggesting how overcoming these epistemic injustices can benefit the global community in this challenging situation and in the future. (shrink)

This article explores the use of model organisms in studying the cognitive phenomenon of decision-making. Drawing on the framework of biological control to develop a skeletal conception of decision-making, we show that two core features of decision-making mechanisms can be identified by studying model organisms, such as E. coli, jellyfish, C. elegans, lamprey, and so on. First, decision mechanisms are distributed and heterarchically structured. Second, they depend heavily on chemical information processing, such as that involving neuromodulators. We end by discussing (...) the implications for studying distinctively human decision-making. (shrink)