BibTeX

@misc{volodina-etal-2024-proceedings-336386,
	title        = {Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024)},
	author       = {Volodina, Elena and Alfter, David and Dobnik, Simon and Lindström Tiedemann, Therese and Muñoz Sánchez, Ricardo and Szawerna, Maria Irena and  Vu, Xuan-Son},
	year         = {2024},
	publisher    = {Association for Computational Linguistics},
}

@inProceedings{szawerna-etal-2024-detecting-336385,
	title        = {Detecting Personal Identifiable Information in Swedish Learner Essays},
	abstract     = {Linguistic data can — and often does — contain PII (Personal Identifiable Information). Both from a legal and ethical standpoint, the sharing of such data is not permissible. According to the GDPR, pseudonymization, i.e. the replacement of sensitive information with surrogates, is an acceptable strategy for privacy preservation. While research has been conducted on the detection and replacement of sensitive data in Swedish medical data using Large Language Models (LLMs), it is unclear whether these models handle PII in less structured and more thematically varied texts equally well. In this paper, we present and discuss the performance of an LLM-based PII-detection system for Swedish learner essays.},
	booktitle    = {Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024)},
	author       = {Szawerna, Maria Irena and Dobnik, Simon and Muñoz Sánchez, Ricardo and Lindström Tiedemann, Therese and Volodina, Elena},
	year         = {2024},
	publisher    = {Association for Computational Linguistics},
}

@inProceedings{munozsanchez-etal-2024-names-336384,
	title        = {Did the Names I Used within My Essay Affect My Score? Diagnosing Name Biases in Automated Essay Scoring},
	abstract     = {Automated essay scoring (AES) of second-language learner essays is a high-stakes task as it can affect the job and educational opportunities a student may have access to. Thus, it becomes imperative to make sure that the essays are graded based on the students’ language proficiency as opposed to other reasons, such as personal names used in the text of the essay. Moreover, most of the research data for AES tends to contain personal identifiable information. Because of that, pseudonymization becomes an important tool to make sure that this data can be freely shared. Thus, our systems should not grade students based on which given names were used in the text of the essay, both for fairness and for privacy reasons. In this paper we explore how given names affect the CEFR level classification of essays of second language learners of Swedish. We use essays containing just one personal name and substitute it for names from lists of given names from four different ethnic origins, namely Swedish, Finnish, Anglo-American, and Arabic. We find that changing the names within the essays has no apparent effect on the classification task, regardless of whether a feature-based or a transformer-based model is used.},
	booktitle    = {Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024)},
	author       = {Muñoz Sánchez, Ricardo and Dobnik, Simon and Szawerna, Maria Irena and Lindström Tiedemann, Therese and Volodina, Elena},
	year         = {2024},
	publisher    = {Association for Computational Linguistics},
}