Looping Effects in Social Ontology

Does algorithmic political bias contribute to an entrenchment and polarization of political positions? Franke argues that it may do so because the bias involves classifications of people as liberals, conservatives, etc., and individuals often conform to the ways in which they are classified. I provide a novel example of this phenomenon in human–computer interactions and introduce a social psychological mechanism that has been overlooked in this context but should be experimentally explored. Furthermore, while Franke proposes that algorithmic political classifications entrench (...) political identities, I contend that they may often produce the opposite result. They can lead people to change in ways that disconfirm the classifications. Consequently and counterintuitively, algorithmic political bias can in fact decrease political entrenchment and polarization. (shrink)

Ideology is commonly defined along functional, epistemic, and genetic dimensions. This article advances a reasonably unified account that specifies how they connect and locates the mechanisms at work. I frame the account along a recent distinction between anchoring and grounding, endorse an etiological reading of functional explanations, and draw on current work about the epistemology of delusion, looping effects, and structuring causes to explain how ideologies originate, reproduce, and possibly collapse. This eventually allows articulating how the legitimating function of ideologies (...) relates to the constitutive and causal role they play when embedded into the facts they are originally designed to anchor. (shrink)

Winner of the 2020 Essay Competition of the International Social Ontology Society. -/- Ian Hacking uses the looping effect to describe how classificatory practices in the human sciences interact with the classified people. While arguably this interaction renders the affected human kinds unstable and hence different from natural kinds, realists argue that also some prototypical natural kinds are interactive and human kinds in general are stable enough to support explanations and predictions. I defend a more fine-grained realist interpretation of interactive (...) human kinds by arguing for an explanatory domain account of the looping effect. First, I argue that knowledge of the feedback mechanisms that mediate the looping effect can supplement, and help to identify, the applicability domain over which a kind and its property variations are stably explainable. Second, by applying this account to cross-cultural case studies of psychiatric disorders, I distinguish between congruent feedback mechanisms that explain matches between classifications and kinds, and incongruent feedback mecha- nisms that explain mismatches. For example, congruent mechanisms maintain Western auditory experiences in schizophrenia, whereas exporting diagnostic labels inflicts incongruence by influencing local experiences. Knowledge of the mechanisms can strengthen explanatory domains, and thereby facilitate classificatory adjustments and possible interventions on psychiatric disorders. (shrink)

This paper examines the phenomenon of ‘interactive kinds’ first identified by Ian Hacking. An interactive kind is one that is created or significantly modified once a concept of it has been formulated and acted upon in certain ways. Interactive kinds may also ‘loop back’ to influence our concepts and classifications. According to Hacking, interactive kinds are found exclusively in the human domain. After providing a general account of interactive kinds and outlining their philosophical significance, I argue that they are not (...) confined to the human realm, but that they can also occur elsewhere. Hence, I conclude by arguing that interactive kinds pose a challenge to scientific realism about kinds by making it difficult to make a distinction between real and non-real kinds. (shrink)