A data science axiology: the nature, value, and risks of data science

Michael L. Brodie
7/19/2023
cs.AIcs.DBcs.LG

No confident code implementation found for this paper.

Abstract

Data science is not a science. It is a research paradigm with an unfathomed scope, scale, complexity, and power for knowledge discovery that is not otherwise possible and can be beyond human reasoning. It is changing our world practically and profoundly already widely deployed in tens of thousands of applications in every discipline in an AI Arms Race that, due to its inscrutability, can lead to unfathomed risks. This paper presents an axiology of data science, its purpose, nature, importance, risks, and value for problem solving, by exploring and evaluating its remarkable, definitive features. As data science is in its infancy, this initial, speculative axiology is intended to aid in understanding and defining data science to recognize its potential benefits, risks, and open research challenges. AI based data science is inherently about uncertainty that may be more realistic than our preference for the certainty of science. Data science will have impacts far beyond knowledge discovery and will take us into new ways of understanding the world.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{brodie2023data,
  title  = {A data science axiology: the nature, value, and risks of data science},
  author = {Michael L. Brodie},
  year   = {2023},
  eprint = {2307.10460},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.10460v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2307.10460.svg)](https://codeofpaper.com/paper/2307.10460)

Discussion