Incorporating emergence in data-driven algorithms: the circularity pathway

Abstract

Today’s AI algorithms often use a data-first approach, where available data predicates the development of algorithms, following which the algorithms are evaluated using quantitative metrics. This institutes a lack of attention to sociomaterial aspects and broader contexts, ones that are highly relevant in times when AI is used across a variety of socially relevant sectors. Contemporary AI design practices, often characterised as ground-truthing, has entrenched a social science deficit, where qualitative aspects are largely ignored. In this commentary, we consider the subset of tasks involving data-driven estimation of emergent properties, properties which intrinsically emerge through processes underpinned by relationalities between objects. We posit that attention to emergence relationalities would enhance algorithmic estimation of emergent properties, while also making them more ethically aligned due to attempting to mirror actual phenomena. Yet, where do we start to operationalize this? We observe that modelling circularities within data-driven algorithms has led to hugely successful algorithms. It is also the case that circular formulations are underpinned by relationalities between objects. Consequently, we propose that circularity offers a substantive pathway to embed emergence relationalities within algorithms. We illustrate the new qualitative analysis capabilities we acquire through viewing algorithmic circularity through the prism of emergence relationalities. These include thought-frameworks to aid designing algorithms, and capabilities towards critiquing extant algorithms. Throughout the paper, we use popular data-driven estimation tasks to anchor the narrative.