| Zugriffsnummer | 52814 |
| Dokumenttyp | Zeitschriftenartikel |
| Peer Review | mit Peer Review |
| Sprache | Englisch |
| Titel | XAI-TRIS: non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance |
| Autor(in); Institution |
Wilming, Rick; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin; Computer Science Department, Technische Universität Berlin, Berlin, GERMANY
|
| Quelle/Jahr | Machine Learning: 113 (2024), 6871 - 6910 |
| ISSN | 0885-6125 (print) ; 1573-0565 (online) |
| DOI | |
| Verlag | Dordrecht [u.a.]: Springer Nature |
| Freie Schlagworte | Explainable AI ; Benchmark ; Explanation performance ; Non-linear problems ; Deep learning ; Suppressor variables ; dataset ; classification |
| Zusammenfassung | The field of ‘explainable’ artificial intelligence (XAI) has produced highly acclaimed methods that seek to make the decisions of complex machine learning (ML) methods ‘understandable’ to humans, for example by attributing ‘importance’ scores to input features. Yet, a lack of formal underpinning leaves it unclear as to what conclusions can safely be drawn from the results of a given XAI method and has also so far hindered the theoretical verification and empirical validation of XAI methods. This means that challenging non-linear problems, typically solved by deep neural networks, presently lack appropriate remedies. Here, we craft benchmark datasets for one linear and three different non-linear classification scenarios, in which the important class-conditional features are known by design, serving as ground truth explanations. Using novel quantitative metrics, we benchmark the explanation performance of a wide set of XAI methods across three deep learning model architectures. We show that popular XAI methods are often unable to significantly outperform random performance baselines and edge detection methods, attributing false-positive importance to features with no statistical relationship to the prediction target rather than truly important features. Moreover, we demonstrate that explanations derived from different model architectures can be vastly different; thus, prone to misinterpretation even under controlled conditions. |
| Kostenfreier Zugang | Open Access Hybrid |
| Rechteinformation | CC BY 4.0 ; Creative Commons Attribution 4.0 License |
| Themenbereich der Metrologie | Metrologie in der Medizin |
| Forschungsprojekt | internes Projekt: Heidenhain Institution "Nachwuchsforschergruppe im Bereich Maschinelles Lernen" |
| Förderinformationen (1) |
Förderername: intern
Förderprogramm: Heidenhain Titel der Förderung: Nachwuchsforschergruppe im Bereich Maschinelles Lernen Förderungsnummer: 1W-84027 |