Work

Inherently Interpretable Machine Learning: A Contrasting Paradigm to Post-hoc Explainable AI

Year: 2025

Type: article

Abstract: A decade ago, data scientists devoted roughly 60–80% of their effort to manual feature engineering, aiming to create features with high predictive power and meaningful properties (Press 2016 ). However, with the rise of deep learning and highly flexible models capable of processing raw input features and learning intricate feature representations, this focus has shifted dramatically (Janiesch et al. 2021 ). Today, data scientists spend much of their time explaining opaque machine learning (ML) models because users struggle to understand their complex decision logic (Bauer et al. 2021 ).

Source: Business & Information Systems Engineering

DOI: 10.1007/s12599-025-00964-0

Authors: Patrick Zschech, Sven Weinzierl, Mathias Kraus

Institutions: Technische Universität Dresden, Friedrich-Alexander-Universität Erlangen-Nürnberg, University of Regensburg

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,090

Funder: Technische Universität Dresden