Topics of interest include:

1. Legal Aspects of Language Data (LEGAL2026)

• Regulatory frameworks and global governance: Impact of the GDPR, EU Data Act, Data Governance Act, Digital Services Act, AI Act, and international regulations (e.g., China’s 2023 Draft Rules on Generative AI, U.S. AI Bill of Rights) on access, circulation, and reuse of language and speech data; statutory exceptions for text and data mining.

• Intellectual property, data protection, and LLM governance: Legal issues surrounding training data, derivative datasets, and model outputs; copyright, data governance, and data protection obligations in the development and deployment of Large Language Models.

• Ethics, fairness, trust, and transparency: Ethical considerations in personal data collection and reuse; ensuring fairness, transparency, and accountability in language and speech technologies.

• Compliance in practice: Legal metadata, provenance, consent documentation, usage rights, and machine-readable licensing; practical workflows for lawful data collection, annotation, and sharing.

• Operationalizing compliance: Tools and methods that support automated compliance checking, risk detection, consent tracking, and policy-aware data filtering; language technologies assisting in legal compliance.

• Emerging and grey areas: Legal uncertainties around synthetic or augmented data, LLM-generated content, and cross-modal leakage; evolving interpretations of anonymization thresholds.

• Interdisciplinary and cross-border coordination: Global harmonization of legal and technical approaches; collaboration models between researchers, legal experts, and infrastructure providers; navigating jurisdictional inconsistencies.

2. Pseudonymization, Anonymization, and De-identification: Theoretical, Methodological, and Technical Aspects (CALD-pseudo 2026)

• Detection and classification of personal information (PI): Automatic identification of PI in text, speech, and multimodal data; context-dependent and indirect indicators of identity.

• Replacement and transformation of PI: Context-sensitive pseudonymization and anonymization methods; substitution, masking, obfuscation; maintaining coherence across discourse and modalities.

• Utility and bias after de-identification: Effects of de-identification on downstream task performance, linguistic research validity, readability, and bias amplification or reduction.

• Approaches to evaluation and adversarial testing: Metrics and frameworks for assessing de-identification quality; adversarial re-identification attempts; robustness and failure-mode analysis.

• Dataset creation for de-identification research: Methodological, ethical, and annotation-related considerations in building corpora for training or evaluating de-identification systems.

• Low-resource scenarios: Techniques for de-identification in settings with limited data, scarce annotations, or underrepresented languages; transfer and multilingual approaches.

• Speech-specific challenges: Removing speaker identity cues in audio; voice anonymization; cross-modal leakage between text, transcripts, and acoustic features.

• Cross-disciplinary applications and challenges: Integrating de-identification techniques into real-world workflows in areas such as linguistics, social sciences, digital humanities, healthcare, and other private- or public-sector data environments.