A conceptual model for leaving the data-centric approach in machine learning
This work addresses the need for a unified approach to integrate external constraints in machine learning, potentially benefiting researchers across domains like fairness and engineering, though it is incremental as it builds on existing application-specific methods.
The paper tackles the problem of moving beyond purely data-centric machine learning by proposing a conceptual model that unifies methods for incorporating external constraints, such as fairness or physics, into a common framework to facilitate cross-field collaboration.
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.