Towards EU AI Act Compliance: Self-Certification and Fairness Alignment for Facial Emotion Recognition

Gregor Autischer, Kerstin Waxnegger, Dominik Kowald
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:453-459, 2026.

Abstract

The European Union’s AI Act establishes comprehensive requirements for high-risk AI systems, yet the harmonized standards granting presumption of conformity remain under development. We investigate the practical application of the Fraunhofer AI Assessment Catalogue as a self-certification framework by conducting a complete self-certification cycle of an AI-based facial emotion recognition system. Starting from a baseline model with deficiencies including inadequate fairness alignment and high prediction uncertainty, we document an enhancement process guided by certification requirements. The enhanced system achieves improved accuracy, higher prediction confidence and comprehensive fairness across demographic groups, successfully satisfying certification criteria for the reliability and fairness dimensions. We find that the certification framework provides substantial value as a proactive development tool, driving concrete technical improvements. However, fundamental gaps separate structured self-certification from legal compliance: harmonized European standards are not yet fully available and assessment catalogues cannot substitute for mandatory conformity assessment procedures. These findings establish the Fraunhofer AI Assessment Catalogue as a valuable preparatory tool that complements rather than replaces formal AI Act compliance requirements.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-autischer26a, title = {Towards EU AI Act Compliance: Self-Certification and Fairness Alignment for Facial Emotion Recognition}, author = {Autischer, Gregor and Waxnegger, Kerstin and Kowald, Dominik}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {453--459}, year = {2026}, editor = {De Bie, Tijl and Defrance, MaryBeth and Calders, Toon and Nguyen, Dennis}, volume = {350}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v350/main/assets/autischer26a/autischer26a.pdf}, url = {https://proceedings.mlr.press/v350/autischer26a.html}, abstract = {The European Union’s AI Act establishes comprehensive requirements for high-risk AI systems, yet the harmonized standards granting presumption of conformity remain under development. We investigate the practical application of the Fraunhofer AI Assessment Catalogue as a self-certification framework by conducting a complete self-certification cycle of an AI-based facial emotion recognition system. Starting from a baseline model with deficiencies including inadequate fairness alignment and high prediction uncertainty, we document an enhancement process guided by certification requirements. The enhanced system achieves improved accuracy, higher prediction confidence and comprehensive fairness across demographic groups, successfully satisfying certification criteria for the reliability and fairness dimensions. We find that the certification framework provides substantial value as a proactive development tool, driving concrete technical improvements. However, fundamental gaps separate structured self-certification from legal compliance: harmonized European standards are not yet fully available and assessment catalogues cannot substitute for mandatory conformity assessment procedures. These findings establish the Fraunhofer AI Assessment Catalogue as a valuable preparatory tool that complements rather than replaces formal AI Act compliance requirements.} }
Endnote
%0 Conference Paper %T Towards EU AI Act Compliance: Self-Certification and Fairness Alignment for Facial Emotion Recognition %A Gregor Autischer %A Kerstin Waxnegger %A Dominik Kowald %B Proceedings of Fifth European Conference on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2026 %E Tijl De Bie %E MaryBeth Defrance %E Toon Calders %E Dennis Nguyen %F pmlr-v350-autischer26a %I PMLR %P 453--459 %U https://proceedings.mlr.press/v350/autischer26a.html %V 350 %X The European Union’s AI Act establishes comprehensive requirements for high-risk AI systems, yet the harmonized standards granting presumption of conformity remain under development. We investigate the practical application of the Fraunhofer AI Assessment Catalogue as a self-certification framework by conducting a complete self-certification cycle of an AI-based facial emotion recognition system. Starting from a baseline model with deficiencies including inadequate fairness alignment and high prediction uncertainty, we document an enhancement process guided by certification requirements. The enhanced system achieves improved accuracy, higher prediction confidence and comprehensive fairness across demographic groups, successfully satisfying certification criteria for the reliability and fairness dimensions. We find that the certification framework provides substantial value as a proactive development tool, driving concrete technical improvements. However, fundamental gaps separate structured self-certification from legal compliance: harmonized European standards are not yet fully available and assessment catalogues cannot substitute for mandatory conformity assessment procedures. These findings establish the Fraunhofer AI Assessment Catalogue as a valuable preparatory tool that complements rather than replaces formal AI Act compliance requirements.
APA
Autischer, G., Waxnegger, K. & Kowald, D.. (2026). Towards EU AI Act Compliance: Self-Certification and Fairness Alignment for Facial Emotion Recognition. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:453-459 Available from https://proceedings.mlr.press/v350/autischer26a.html.

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