Ethical Safeguards in Autonomous Data Collection: Protecting Cultural Context and Privacy in Indigenous Language NLP

The increasing dependence of Natural Language Processing (NLP) systems on large-scale textual datasets has intensified the use of automated data collection methods such as web scraping, crawling, and autonomous extraction pipelines. While these approaches improve scalability and support the development of Large Language Models (LLMs), they also raise important ethical concerns, particularly in indigenous and low-resource language contexts. Existing data collection systems primarily prioritize data availability and technical efficiency, often overlooking issues related to privacy, cultural sensitivity, consent, and community-centered governance. Consequently, automated pipelines may collect personally identifiable information (PII), culturally sensitive narratives, and indigenous knowledge without adequate contextual understanding or ethical safeguards. This paper proposes an ethical-by-design framework for autonomous indigenous language data collection using an LLM-based multi-agent architecture. The framework integrates ethical safeguards directly into system operations through context-aware filtering, hybrid PII detection, cultural sensitivity classification, and auditability mechanisms. A Design Science Research methodology was adopted to develop and evaluate the framework using scenario-based validation involving privacy-sensitive and culturally sensitive textual data. The evaluation demonstrates that the proposed approach supports transparent and accountable data collection while reducing ethical risks associated with privacy violations and inappropriate extraction of culturally protected information.  This study contributes to ongoing research in responsible AI, indigenous data governance, and ethical NLP by demonstrating how ethical principles can be operationalized within autonomous data collection infrastructures. The proposed framework provides a foundation for the development of transparent, culturally aware, and privacy-conscious NLP data collection systems for indigenous and low-resource language technologies.