Napoletani, Domenico and Panza, Marco and Struppa, Daniele (2021) The Agnostic Structure of Data Science Methods. Lato Sensu, revue de la Société de philosophie des sciences, 8 (2). pp. 44-57. ISSN 2295-8029

Preview

Text 15573-Article Text-105693-2-10-20210406.pdf - Published Version Available under License Creative Commons Attribution Non-commercial Share Alike. Download (448kB) | Preview

Abstract

In this paper we argue that data science is a coherent and novel approach to empirical problems that, in its most general form, does not build understanding about phenomena. Within the new type of mathematization at work in data science, mathematical methods are not selected because of any relevance for a problem at hand; mathematical methods are applied to a specific problem only by `forcing’, i.e. on the basis of their ability to reorganize the data for further analysis and the intrinsic richness of their mathematical structure. In particular, we argue that deep learning neural networks are best understood within the context of forcing optimization methods. We finally explore the broader question of the appropriateness of data science methods in solving problems. We argue that this question should not be interpreted as a search for a correspondence between phenomena and specific solutions found by data science methods; rather, it is the internal structure of data science methods that is open to precise forms of understanding.

---

Export/Citation:

EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL

Social Networking:

Share |

---

Item Type:	Published Article or Volume
Creators:	Creators Email ORCID Napoletani, Domenico Panza, Marco panzam10@gmail.com 0000-0003-4131-7103 Struppa, Daniele
Keywords:	Big data, philosophy of science, philosophy of mathematics
Subjects:	General Issues > Data Specific Sciences > Mathematics > Applicability Specific Sciences > Mathematics > Epistemology
Depositing User:	Lato Sensu
Date Deposited:	25 Apr 2021 03:55
Last Modified:	25 Apr 2021 03:55
Item ID:	18942
Journal or Publication Title:	Lato Sensu, revue de la Société de philosophie des sciences
Publisher:	Société de philosophie des sciences
Official URL:	https://ojs.uclouvain.be/index.php/latosensu/artic...
DOI or Unique Handle:	10.20416/LSRSPS.V8I2.5
Subjects:	General Issues > Data Specific Sciences > Mathematics > Applicability Specific Sciences > Mathematics > Epistemology
Date:	6 April 2021
Page Range:	pp. 44-57
Volume:	8
Number:	2
ISSN:	2295-8029
URI:	https://philsci-archive.pitt.edu/id/eprint/18942

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Altmetric.com

Actions (login required)

View Item