A conceptual model for leaving the data-centric approach in machine learning

TL;DR

This paper proposes a high-level conceptual model to unify various approaches for incorporating external constraints into machine learning, moving beyond purely data-centric methods and fostering cross-disciplinary exchange.

Contribution

It introduces a unifying conceptual framework that bridges application-specific methods for external constraints in machine learning.

Findings

A high-level model unifies diverse external constraint methods

Facilitates cross-disciplinary exchange in ML approaches

Encourages moving beyond purely data-centric ML models

Abstract

For a long time, machine learning (ML) has been seen as the abstract problem of learning relationships from data independent of the surrounding settings. This has recently been challenged, and methods have been proposed to include external constraints in the machine learning models. These methods usually come from application-specific fields, such as de-biasing algorithms in the field of fairness in ML or physical constraints in the fields of physics and engineering. In this paper, we present and discuss a conceptual high-level model that unifies these approaches in a common language. We hope that this will enable and foster exchange between the different fields and their different methods for including external constraints into ML models, and thus leaving purely data-centric approaches.