In this paper I want to discuss the epistemological risk that we face by building representations of social phenomena that exclude contingency and unpredictability from the frame. Contingency or, to use Robert Musil’s “the sense of possibility” is what allows the adaptation and the dynamic transformation of subjectivity with respect to the environment and the mutating context. Datification is the new representation tool within social sciences. The algorithmic technology promise together with the huge amount of available information in form of data offers the perspective of creating new knowledge capable of allowing previsions on people’s behaviors, preferences and characteristics. The objectives of data science are the classification of what have happened and the prevision of what will happen. The anticipation of the future is not confined to irrelevant marketing areas, it includes very sensible situations such as risk assessment in recidivism, credit scoring, insurance premium evaluation, recruitment and selection of staff, university student candidate selection, predictive policing, welfare administration etc. However, data is always built, raw data is an epistemic invention. Data is created according to rules and mechanisms that reflect the interests and the perspectives of who is in charge of its interpretation. Instead data is sometimes wrongly regarded as the direct expression of phenomena, the object of knowledge that is organized by experience. The propensity to interpret the past following probabilistic inferences as a sort of straight anticipation of the future and the tendency to create clusters of people according to some of their common characters, behaviors or conditions is a normal human aspiration. The process of abstraction is one of the objectives of science. But we are in the process of transforming the understanding of what we are prepared to accept as an explanation. If we delegate a technological system to foresee the future without control, considering the normative effect of the prevision, and the uncertain conditions of the representation of the past on which the inferences are based, we are prone to technologically determine the future that we suppose to anticipate. There is a second risk of the automation of the interpretation of the future events, the inadequacy of the prevision to cope with unpredictability of our environment, due to the rigidity of calculations adopted by algorithmic technology. Human judgement is not perfect, but humans can assume the responsibility of their perspective, humans can change their mind, they can assess the variability and multiplicity of preferences and find negotiated political solutions to common problems. Humans possess the ‘sense of possibility’ introduced by Musil that according to Bernard Stiegler we can interpret as a “thought within entropy”. This algorithmic attitude towards future risks to reduce the adaptability of human beings. If we accept to base every judgement on a calculation, even when circumstances do not allow a complete and exhaustive measurement of all element of the situation, we risk misunderstanding the context, being incapable of reacting appropriately when facing external, unpredictable conditions.

Numerico, T. (2020). Tecnologia digitale, dati e algoritmi per riorganizzare gli schemi dell’esperienza cognitiva: alcune osservazioni critiche. POLEMOS, 2020(2).

Tecnologia digitale, dati e algoritmi per riorganizzare gli schemi dell’esperienza cognitiva: alcune osservazioni critiche

numerico T
2020

Abstract

In this paper I want to discuss the epistemological risk that we face by building representations of social phenomena that exclude contingency and unpredictability from the frame. Contingency or, to use Robert Musil’s “the sense of possibility” is what allows the adaptation and the dynamic transformation of subjectivity with respect to the environment and the mutating context. Datification is the new representation tool within social sciences. The algorithmic technology promise together with the huge amount of available information in form of data offers the perspective of creating new knowledge capable of allowing previsions on people’s behaviors, preferences and characteristics. The objectives of data science are the classification of what have happened and the prevision of what will happen. The anticipation of the future is not confined to irrelevant marketing areas, it includes very sensible situations such as risk assessment in recidivism, credit scoring, insurance premium evaluation, recruitment and selection of staff, university student candidate selection, predictive policing, welfare administration etc. However, data is always built, raw data is an epistemic invention. Data is created according to rules and mechanisms that reflect the interests and the perspectives of who is in charge of its interpretation. Instead data is sometimes wrongly regarded as the direct expression of phenomena, the object of knowledge that is organized by experience. The propensity to interpret the past following probabilistic inferences as a sort of straight anticipation of the future and the tendency to create clusters of people according to some of their common characters, behaviors or conditions is a normal human aspiration. The process of abstraction is one of the objectives of science. But we are in the process of transforming the understanding of what we are prepared to accept as an explanation. If we delegate a technological system to foresee the future without control, considering the normative effect of the prevision, and the uncertain conditions of the representation of the past on which the inferences are based, we are prone to technologically determine the future that we suppose to anticipate. There is a second risk of the automation of the interpretation of the future events, the inadequacy of the prevision to cope with unpredictability of our environment, due to the rigidity of calculations adopted by algorithmic technology. Human judgement is not perfect, but humans can assume the responsibility of their perspective, humans can change their mind, they can assess the variability and multiplicity of preferences and find negotiated political solutions to common problems. Humans possess the ‘sense of possibility’ introduced by Musil that according to Bernard Stiegler we can interpret as a “thought within entropy”. This algorithmic attitude towards future risks to reduce the adaptability of human beings. If we accept to base every judgement on a calculation, even when circumstances do not allow a complete and exhaustive measurement of all element of the situation, we risk misunderstanding the context, being incapable of reacting appropriately when facing external, unpredictable conditions.
Numerico, T. (2020). Tecnologia digitale, dati e algoritmi per riorganizzare gli schemi dell’esperienza cognitiva: alcune osservazioni critiche. POLEMOS, 2020(2).
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/372834
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact