2021
DOI: 10.1162/artl_a_00336
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The Impossibility of Automating Ambiguity

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 fundamen… Show more

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Cited by 80 publications
(50 citation statements)
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References 42 publications
(58 reference statements)
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“…Resultantly, people have grown to degrade first-hand, direct experiences [ 13 ], in favour of a strict compliance with ever-increasing specification, rules, regulations, and conventions established to commodify prescribed experiences and products, centralising market certainty, deskilling people in the process (note, this can be detected in phrases, like ‘the computer won’t let me do that’ or ‘the system is down’). Thus, while current technological evolution has improved many aspects of our lives, it is an evolutionary work in progress, rooted in a mechanistic, Cartesian worldview, founded on a fear of uncertainty [ 13 , 14 ].…”
Section: A (Brief) Philosophical Excursionmentioning
confidence: 99%
“…Resultantly, people have grown to degrade first-hand, direct experiences [ 13 ], in favour of a strict compliance with ever-increasing specification, rules, regulations, and conventions established to commodify prescribed experiences and products, centralising market certainty, deskilling people in the process (note, this can be detected in phrases, like ‘the computer won’t let me do that’ or ‘the system is down’). Thus, while current technological evolution has improved many aspects of our lives, it is an evolutionary work in progress, rooted in a mechanistic, Cartesian worldview, founded on a fear of uncertainty [ 13 , 14 ].…”
Section: A (Brief) Philosophical Excursionmentioning
confidence: 99%
“…The case study expanded the state of the art by including the detection of abstract concepts that are very subjective and difficult to quantify. We are aware that there are significant omissions of exceptional cases by the AI and ML algorithms [ 63 ] and tried to reduce the bias by incorporating confidence scores. We are also aware that the kappa inter-annotator agreement may cause an issue in the interpretation of the agreements as poor, slight, fair, moderate, substantial, and strong [ 64 , 65 ].…”
Section: Discussionmentioning
confidence: 99%
“…A path dependency of seventeenth-century scientific ideas (i.e. the Newtonian/Cartesian paradigm, see Birhane, 2021;Montuori, 2011) has arguably led sports science and pedagogy to downplay the role of environmental (sociocultural, historical, political) constraints, creating an organismic asymmetry . This biased preference for organism-centered explanatory mechanisms has had an influence in shaping applied research and practical interventions, arguably inhibiting our understanding of the myriad of complex interactions that typify an athlete's world .…”
Section: The Learning In Development Research Frameworkmentioning
confidence: 99%