Literature DB >> 34529757

The Impossibility of Automating Ambiguity.

Abeba Birhane1,2.   

Abstract

On the one hand, complexity science and enactive and embodied cognitive science approaches emphasize that people, as complex adaptive systems, are ambiguous, indeterminable, and inherently unpredictable. On the other, Machine Learning (ML) systems that claim to predict human behaviour are becoming ubiquitous in all spheres of social life. I contend that ubiquitous Artificial Intelligence (AI) and ML systems are close descendants of the Cartesian and Newtonian worldview in so far as they are tools that fundamentally sort, categorize, and classify the world, and forecast the future. Through the practice of clustering, sorting, and predicting human behaviour and action, these systems impose order, equilibrium, and stability to the active, fluid, messy, and unpredictable nature of human behaviour and the social world at large. Grounded in complexity science and enactive and embodied cognitive science approaches, this article emphasizes why people, embedded in social systems, are indeterminable and unpredictable. When ML systems "pick up" patterns and clusters, this often amounts to identifying historically and socially held norms, conventions, and stereotypes. Machine prediction of social behaviour, I argue, is not only erroneous but also presents real harm to those at the margins of society.
© 2021 Massachusetts Institute of Technology.

Entities:  

Keywords:  Complexity; artificial intelligence; embodiment; equity; machine learning; racial justice

Mesh:

Year:  2021        PMID: 34529757     DOI: 10.1162/artl_a_00336

Source DB:  PubMed          Journal:  Artif Life        ISSN: 1064-5462            Impact factor:   0.667


  3 in total

Review 1.  Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches.

Authors:  Sara E Berger; Alexis T Baria
Journal:  Front Pain Res (Lausanne)       Date:  2022-06-02

2.  The unseen Black faces of AI algorithms.

Authors:  Abeba Birhane
Journal:  Nature       Date:  2022-10       Impact factor: 69.504

3.  Intersectionality and reflexivity-decolonizing methodologies for the data science process.

Authors:  A E Boyd
Journal:  Patterns (N Y)       Date:  2021-12-10
  3 in total

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