Cynical technical practice: From AI to APIs

Abstract

In this article, we examine how critical thinking, methods and design are used within the tech industry, using Philip Agre’s notion of critical technical practice (CTP) to consider the rise of ‘cynical’ technical practice. Arguments by tech firms that their AI systems are ethical, contextual, situated or fair, as well as APIs that are privacy-compliant and offer greater user control, are now commonplace. Yet, these justifications routinely disguise the organisational, and economic, reasons for the development of technical systems and features. The article considers how different forms of ‘technical critique’ are used by technical practitioners such as software engineers, applying Agre’s work on CTP, AI planning, grammars of action and empowerment to evaluate, and contextualise these justifications. As Agre understood, technical practitioners are not necessarily ‘a-critical’ or ‘uncritical’ in their approach to the design of technological systems or methods, but ordinarily compare the utility or performance of such according to a golden ethic: ‘does it work?’. Drawing on Agre’s studies of AI in the 1990s, the article considers how and what Agre considered to be the ‘Cartesian soul’ of AI research, on linguistic structuralism, and continues to frame much work within the wider tech industry today. Yet increasingly, as the article shows, ‘narrow’ and cynical forms of technical criticality are being used to legitimise, and publicise, corporate strategies of tech firms, whether through the development of AI systems by automotive start-ups such as Comma, or the management of relations with external developers through APIs, in the case of Facebook. Rather than judging the moral character of technical practitioners, however, the article offers an approach – via the work of Philip Agre – to examine how critical thinking is used, and often abused, within and beyond the tech industry.

Pluralising critical technical practice

“…Critique may be construed in terms of processes of problematisation: the surfacing of more fundamental tensions and issues, beyond bugs to be fixed and, amongst others industry-led incentives to solve social problems through technological innovation (cf. Hind and Seitz (2022) in this issue). Such problematisations have been proposed to lead to disenchantments and ‘awakenings’ (Malik and Malik, 2021) as well as bigger shifts in ways of thinking or relating to technologically mediated practices and systems (cf.…”

“…As the current critique of the risks of and biases in AI and related firings demonstrate, with Timnit Gebru as maybe the most prominent example, 4 formulation of critique from within tech industries can be fraught, to say the least. Some of the contributors to this special issue also discuss the possibility of 'ethics washing' through particular forms of technical criticality (ab)used by tech industry players for whom the organisational bottom line is corporate interests, which may limit critical engagement related to broader societal issues (see Hind and Seitz (2022) in this issue).…”

“…The section ‘Social Lives of Critique’ – hinting at Ruppert and colleagues’ (2013a and b) notable calls to acknowledge and inquire into the ‘social life of methods’ and devices – features papers that focus on criticality within different professional settings and situations in which digital, algorithmic, and data technologies play a central role. In ‘Cynical technical practice: From AI to APIs', Sam Hind and Tatjana Seitz (2022) detect and scrutinise a ‘narrow’ understanding of technical criticality used in the tech industry, ostensibly in service of social transformation, but in fact serving their corporate agendas. Drawing on the work of Agre, they argue that rather than a lack of technical criticality, tech industry workers are – still – too occupied with ‘work-ability’ focusing on only those theories that can be applied in a technical sense.…”

Simulation and the Reality Gap: Moments in a prehistory of synthetic data

“…Video footage captured for the purposes of training computer vision models intended for autonomous vehicles must be painstakingly segmented and labeled; each tree, person, vehicle, light post, traffic sign and so on (Kniazieva, 2022). Although end to end (E2E) approaches are increasingly common (Hind and Seitz, 2022) -removing the need for feature segmentationthey depend on larger, more diverse, datasets in order for such models to properly recognize objects. Finding or creating such datasets, offering a form of diversity of phenomena necessary for the task at hand is equally labour-and cost-intensive.…”

The ethics of AI business practices: a review of 47 AI ethics guidelines

“…However, accounting for these potential benefits within the contexts of AI value chains enables us to identify many concomitant harms: novel insights or gains to efficiency in some parts of an AI value chain may raise new risks in others (Cobbe, Veale, & Singh, 2023;Gansky & McDonald, 2022;Widder & Nafus, 2023); contributions to SDGs or "AI for good" initiatives may only be successful relative to a narrow set of measures (Aula & Bowles, 2023;Madianou, 2021;Moore, 2019); economic prosperity or environmental benefits may be inequitably distributed across different groups, communities, or geographies. While AI systems may produce beneficial outcomes for some value chain actors, pre-existing structural injustices in the social, political, and economic contexts of AI systems and their value chains warrant an assumption that the same systems will also produce harmful outcomes for other actors, particularly those who belong to historically marginalized communities (Birhane, 2021;Hind & Seitz, 2022).…”