Diversity Reading List

Abstract:

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.

Abstract:

John Searle, in his paper on 'Minds, Brains, and Programs' (1980), argues that computational theories in psychology are essentially worthless. He makes two main claims: that computational theories, being purely formal in nature, cannot possibly help us to understand mental processes; and that computer hardware-unlike neuroprotein-obviously lacks the right causal powers to generate mental processes. I shall argue that both these claims are mistaken.

Abstract:
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).

Abstract:
During the 1970s and 1980s, a team of Automated Theorem Proving researchers at the Argonne National Laboratory near Chicago developed the Automated Reasoning Assistant, or AURA, to assist human users in the search for mathematical proofs. The resulting hybrid humans+AURA system developed the capacity to make novel contributions to pure mathematics by very untraditional means. This essay traces how these unconventional contributions were made and made possible through negotiations between the humans and the AURA at Argonne and the transformation in mathematical intuition they produced. At play in these negotiations were experimental practices, nonhumans, and nonmathematical modes of knowing. This story invites an earnest engagement between historians of mathematics and scholars in the history of science and science studies interested in experimental practice, material culture, and the roles of nonhumans in knowledge making.

Abstract:

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.

Abstract:

Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment (“AI spring”) and periods of disappointment, loss of confidence, and reduced funding (“AI winter”). Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense.

Introduction: Let’s set the stage. In 2016, ProPublica released a ground-breaking investigation called Machine Bias. You’ve probably heard of it. They examined a criminal risk prediction tool that’s used across the country. These are tools that claim to predict the likelihood that a defendant will reoffend if released, and they are used to inform bail and parole decisions.

Summary: How does one make a clever adaptive machine that can recognise speech, control an aircraft, and detect credit card fraud? Recent years have seen a revolution in the kinds of tasks computers can do. Underlying these advances is the burgeoning field of machine learning and computational neuroscience. The same methods that allow us to make clever machines also appear to hold the key to understanding ourselves: to explaining how our brain and mind work. This chapter explores this exciting new field and some of the philosophical questions that it raises.

Introduction: Mark Twain said that, for people whose only tool is a hammer, everything looks like a nail. In Thinking about Technology, Joe Pitt's main tools appear to be those of the philosopher of science, so it is not surprising that he claims most problems of philosophy of technology are epistemic problems. As he puts it: 'The strategy here is straightforward. Philosophers of science have examined in detail a number of concepts integral to our understanding of what makes science what it is. The bottom line is this: philosophical questions about technology are first and foremost questions about what we can know about a specific technology and its effects and in what that knowledge consists' . Although Pitt points out important disanalogies between scientific and technological knowledge, nevertheless he emphasizes that philosophy of technology is primarily epistemology. Pitt has stipulatively defined ethical and political analyses of technology as not part of philosophy and philosophy of technology. While claiming to assess the foundations of philosophy of technology, he has adopted a reductionist approach to his subject matter, one that ignores or denigrates the majority of work in philosophy of technology. Does Pitt's bold, reductionist move succeed?

Abstract: On a literal reading of `Computing Machinery and Intelligence'', Alan Turing presented not one, but two, practical tests to replace the question `Can machines think?'' He presented them as equivalent. I show here that the first test described in that much-discussed paper is in fact not equivalent to the second one, which has since become known as `the Turing Test''. The two tests can yield different results; it is the first, neglected test that provides the more appropriate indication of intelligence. This is because the features of intelligence upon which it relies are resourcefulness and a critical attitude to one''s habitual responses; thus the test''s applicablity is not restricted to any particular species, nor does it presume any particular capacities. This is more appropriate because the question under consideration is what would count as machine intelligence. The first test realizes a possibility that philosophers have overlooked: a test that uses a human''s linguistic performance in setting an empirical test of intelligence, but does not make behavioral similarity to that performance the criterion of intelligence. Consequently, the first test is immune to many of the philosophical criticisms on the basis of which the (so-called) `Turing Test'' has been dismissed.