As AI technologies are increasingly deployed in work, welfare, healthcare, and other domains, there is a growing realization not only of their power but of their problems. AI has the capacity to reinforce historical injustice, to amplify labor precarity, and to cement forms of racial and gendered inequality. An alternate set of values, paradigms, and priorities are urgently needed. How might we design and evaluate AI from an indigenous perspective? This article draws upon the five Tests developed by Māori scholar (...) Sir Hirini Moko Mead. This framework, informed by Māori knowledge and concepts, provides a method for assessing contentious issues and developing a Māori position. This paper takes up these tests, considers how each test might be applied to data-driven systems, and provides a number of concrete examples. This intervention challenges the priorities that currently underpin contemporary AI technologies but also offers a rubric for designing and evaluating AI according to an indigenous knowledge system. (shrink)

As AI technologies are rolled out into healthcare, academia, human resources, law, and a multitude of other domains, they become de-facto arbiters of truth. But truth is highly contested, with many different definitions and approaches. This article discusses the struggle for truth in AI systems and the general responses to date. It then investigates the production of truth in InstructGPT, a large language model, highlighting how data harvesting, model architectures, and social feedback mechanisms weave together disparate understandings of veracity. It (...) conceptualizes this performance as an operationalization of truth, where distinct, often-conflicting claims are smoothly synthesized and confidently presented into truth-statements. We argue that these same logics and inconsistencies play out in Instruct’s successor, ChatGPT, reiterating truth as a non-trivial problem. We suggest that enriching sociality and thickening “reality” are two promising vectors for enhancing the truth-evaluating capacities of future language models. We conclude, however, by stepping back to consider AI truth-telling as a social practice: what kind of “truth” do we as listeners desire? (shrink)

Personal data is highly vulnerable to security exploits, spurring moves to lock it down through encryption, to cryptographically ‘cloud’ it. But personal data is also highly valuable to corporations and states, triggering moves to unlock its insights by relocating it in the cloud. We characterise this twinned condition as ‘clouded data’. Clouded data constructs a political and technological notion of privacy that operates through the intersection of corporate power, computational resources and the ability to obfuscate, gain insights from and valorise (...) a dependency between public and private. First, we survey prominent clouded data approaches, suggesting their particular affordances produce distinctive versions of privacy. Next, we perform two notional code-based experiments using synthetic datasets. In the field of health, we submit a patient’s blood pressure to a notional cloud-based diagnostics service; in education, we construct a student survey that enables aggregate reporting without individual identification. We argue that these technical affordances legitimate new political claims to capture and commodify personal data. The final section broadens the discussion to consider the political force of clouded data and its reconstitution of traditional notions such as the public and the private. (shrink)

Abstract:AI technologies mine past data to anticipate future events, and yet our world of environmental and political crisis ushers in unprecedented conditions. Mixing examples of operational environments (AI in the oil and gas industry) with insights from media, cultural, and environmental studies, this article explores this grappling with uncertainty. To manage uncertainty, companies strive to internalize the complexity and contingency of the real world, collecting more data, designing more accurate sensors, and developing more exhaustive models. And yet prediction is a (...) fraught exercise that struggles with correlation versus causation, the epistemological outside (the unknown), and the ontological outside (the open-endedness of the future). In addition, technology’s role in accelerating and intensifying the destructive logics of capital contributes to more volatile planetary conditions, undermining the stability and continuity that prediction requires. The article thus argues that, at a fundamental level, a highly fluid future will increasingly frustrate any meaningful degree of prediction. Keywords: prediction, knowledge, AI, machine learning, uncertainty, climate change. (shrink)