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Added by: Clotilde Torregrossa, Contributed by: Marilou NieddaAbstract:
This paper argues that AI follows classical versions of epistemology in assuming that the identity of the knowing subject is not important. In other words this serves to ‘delete the subject’. This disguises an implicit hierarchy of knowers involved in the representation of knowledge in AI which privileges the perspective of those who design and build the systems over alternative perspectives. The privileged position reflects Western, professional masculinity. Alternative perspectives, denied a voice, belong to less powerful groups including women. Feminist epistemology can be used to approach this from new directions, in particular, to show how women’s knowledge may be left out of consideration by AI’s focus on masculine subjects. The paper uncovers the tacitly assumed Western professional male subjects in two flagship AI systems, Cyc and Soar.
Comment: This text is very useful to offer a more socially situated account of AI to students: it is fairly easy to comprehend and it makes a very important point about the fact that symbolic language is not socially neutral.
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Abstract:
The recent proliferation of deepfakes, AI and other digitally produced deceptive representations has revived the debate on the epistemic robustness of photography and other mechanically produced images. Authors such as Rini (2020) and Fallis (2021) claim that the proliferation of deepfakes pose a serious threat to the reliability and the epistemic value of photographs and videos. In particular, Fallis adopts a Skyrmsian account of how signals carry information (Skyrms, 2010) to argue that the existence of deepfakes significantly reduces the information that images carry about the world, which undermines their reliability as a source of evidence. In this paper, we focus on Fallis’ version of the challenge, but our results can be generalized to address similar pessimistic views such as Rini’s. More generally, we offer an account of the epistemic robustness of photography and videos that allows us to understand these systems of representation as continuous with other means of information transmission we find in nature. This account will then give us the necessary tools to put Fallis’ claims into perspective: using a richer approach to animal signaling based on the signaling model of communication (Maynard-Smith and Harper, 2003), we will claim that, while it might be true that deepfake technology increases the probability of obtaining false positives, the dimension of the epistemic threat involved might still be negligible.
Comment: It'd be a good reading for a class that touches on the epistemic challenges of AI and digital images. What is fun and interesting about it is that (i) it gives a more positive view on the issue: it argues that the epistemic threat of AI and manipulated images is not as terrible as many suggest and (ii) in order to argue for this position it relies on the animal world.
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, Contributed by: Marilou NieddaAbstract:
In this chapter, I draw on the seminal work of feminist theorist Iris Marion Young to consider how political responses to structural injustice, exacerbated by AI,¹ might be developed. My interpretation of Young’s work is centred on what I see as an overlooked feature of her account of structural injustice - ‘untraceability’. Drawing on Arendt’s concept of ‘thoughtlessness’ and the work of other scholars such as Benjamin, I explore the relationship between structural injustice and algorithmic decision making,² which, itself, is becoming increasingly untraceable. I argue that while fault-based regulation of AI is vital for combating injustice exacerbated by AI, too little thought goes into addressing the structural dynamics of AI’s impact on society which requires a different sort of approach. I conclude by offering some suggestions on how we might think about mitigating the exacerbation of structural injustice posed by AI. In particular, I advocate adopting several elements of the mini-public approach within the regulatory public-body landscape of democratic governance systems to form a new ‘AI Public Body’ (using the UK as my example).Comment: On the one hand, this book chapter is useful for introducing the concept of structural injustice to students by examining the (un)traceable responsibility that individuals bear when they unconsciously (re)produce structural inequities. The text is fairly easy to read, especially to apprehend such a central concept in feminist theory of justice. On the other hand, Browne applies her understanding of traceable liability to AI technologies, which is an application that students can understand quite well. It also brings about a social perspective in the philosophy of AI.