CLEAR-NL: Language Complexity Feedback for Accessible Skills Languages

Eliza Hobo, Tom Brand, Maaike H.T. De Boer, Marieke van den Tooren
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:288-305, 2026.

Abstract

Skills-based hiring is often presented as a more inclusive alternative to credential-based recruitment, particularly for people whose competences are not well captured by formal qualifications. Central to such approaches are skills languages: standardized descriptions of skills that enable person-job matching and transferability of workers across occupations. However, when skill descriptions are written in abstract or complex language, they can themselves become a barrier, undermining the inclusive aims of skills-based labour market initiatives. In this paper, we conceptualise language complexity in skill descriptions as a fairness and accessibility issue and propose CLEAR-NL: a Dutch-specific, modular approach to support expert maintainers in writing skills in a more accessible way. CLEAR-NL is designed based on four criteria, ensuring that the tool 1) fits in the application context, 2) gives the user feedback that enables informed action, 3) ensures the user is in charge, and 4) is proportional in terms of the size, opacity and risks. We validate CLEAR-NL in a user study with expert maintainers of a skills language, which investigates whether this feedback supports writing and revision decisions. Our findings show that modular feedback can support experts while also highlighting tensions between the design criteria and the practical application context. We discuss the implications of these findings for the design of responsible AI systems that support, rather than replace, human decision-making in fairness-sensitive domains

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-hobo26a, title = {CLEAR-NL: Language Complexity Feedback for Accessible Skills Languages}, author = {Hobo, Eliza and Brand, Tom and De Boer, Maaike H.T. and Tooren, Marieke van den}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {288--305}, 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/hobo26a/hobo26a.pdf}, url = {https://proceedings.mlr.press/v350/hobo26a.html}, abstract = {Skills-based hiring is often presented as a more inclusive alternative to credential-based recruitment, particularly for people whose competences are not well captured by formal qualifications. Central to such approaches are skills languages: standardized descriptions of skills that enable person-job matching and transferability of workers across occupations. However, when skill descriptions are written in abstract or complex language, they can themselves become a barrier, undermining the inclusive aims of skills-based labour market initiatives. In this paper, we conceptualise language complexity in skill descriptions as a fairness and accessibility issue and propose CLEAR-NL: a Dutch-specific, modular approach to support expert maintainers in writing skills in a more accessible way. CLEAR-NL is designed based on four criteria, ensuring that the tool 1) fits in the application context, 2) gives the user feedback that enables informed action, 3) ensures the user is in charge, and 4) is proportional in terms of the size, opacity and risks. We validate CLEAR-NL in a user study with expert maintainers of a skills language, which investigates whether this feedback supports writing and revision decisions. Our findings show that modular feedback can support experts while also highlighting tensions between the design criteria and the practical application context. We discuss the implications of these findings for the design of responsible AI systems that support, rather than replace, human decision-making in fairness-sensitive domains} }
Endnote
%0 Conference Paper %T CLEAR-NL: Language Complexity Feedback for Accessible Skills Languages %A Eliza Hobo %A Tom Brand %A Maaike H.T. De Boer %A Marieke van den Tooren %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-hobo26a %I PMLR %P 288--305 %U https://proceedings.mlr.press/v350/hobo26a.html %V 350 %X Skills-based hiring is often presented as a more inclusive alternative to credential-based recruitment, particularly for people whose competences are not well captured by formal qualifications. Central to such approaches are skills languages: standardized descriptions of skills that enable person-job matching and transferability of workers across occupations. However, when skill descriptions are written in abstract or complex language, they can themselves become a barrier, undermining the inclusive aims of skills-based labour market initiatives. In this paper, we conceptualise language complexity in skill descriptions as a fairness and accessibility issue and propose CLEAR-NL: a Dutch-specific, modular approach to support expert maintainers in writing skills in a more accessible way. CLEAR-NL is designed based on four criteria, ensuring that the tool 1) fits in the application context, 2) gives the user feedback that enables informed action, 3) ensures the user is in charge, and 4) is proportional in terms of the size, opacity and risks. We validate CLEAR-NL in a user study with expert maintainers of a skills language, which investigates whether this feedback supports writing and revision decisions. Our findings show that modular feedback can support experts while also highlighting tensions between the design criteria and the practical application context. We discuss the implications of these findings for the design of responsible AI systems that support, rather than replace, human decision-making in fairness-sensitive domains
APA
Hobo, E., Brand, T., De Boer, M.H. & Tooren, M.v.d.. (2026). CLEAR-NL: Language Complexity Feedback for Accessible Skills Languages. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:288-305 Available from https://proceedings.mlr.press/v350/hobo26a.html.

Related Material