Data Snack - Responsible Text Mining: A Framework for Fair, Transparent, and Ethical AI in Research

Responsible text mining is essential for ensuring that natural language processing systems are fair, transparent, and accountable. As large language models become increasingly important in research and decision-making, they can also reinforce societal biases, misclassify marginalized groups, and generate harmful content. This Data Snack presents a practical approach to integrating ethical evaluation into text mining workflows. By using methods such as CheckList and HateCheck, researchers can assess how models perform in realistic scenarios, beyond simple accuracy metrics. These evaluations reveal key limitations, including misclassification of harmful language and unintended blocking of valid or supportive content. The framework also highlights the importance of evaluating model performance across different groups, including women, people with disabilities, and racial and ethnic minorities. Even with balanced data, models often perform less effectively for these groups due to hidden biases. In addition, the high energy consumption required to train large models raises important environmental concerns. Overall, responsible text mining should be treated as a core competency in data science, supporting ethical reasoning and transparent research practices. This Data Snack was developed and delivered within the framework of the DataNord project at the Data Science Center of the University of Bremen. The DataNord project is funded under the grant number 16DKZ2026A by the Federal Ministry of Research, Technology and Space (BMFTR) and financed by the European Union – NextGenerationEU. It is part of a broader initiative to strengthen data literacy among researchers by offering practical, hands-on training in core areas of 1) data science, 2) research data management (RDM), 3) critical thinking, and 4) ethical, legal and social aspects (ELSA).