A data-centric approach for ethical and trustworthy AI in journalism

2024/09/28 by Laurence Dierickx, Andreas L. Opdahl, Sohail Ahmed Khan +2 · 1 voice
Computer Science · Decision Sciences · Social Sciences · #Big Data Technologies and Applications #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI)

paper · pdf · doi:10.1007/s10676-024-09801-6

openalex publication_date 2024/09/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

Abstract AI-driven journalism refers to various methods and tools for gathering, verifying, producing, and distributing news information. Their potential is to extend human capabilities and create new forms of augmented journalism. Although scholars agreed on the necessity to embed journalistic values in these systems to make AI systems accountable, less attention was paid to data quality, while the results’ accuracy and efficiency depend on high-quality data in any machine learning task. Assessing data quality in the context of AI-driven journalism requires a broader and interdisciplinary approach, relying on the challenges of data quality in machine learning and the ethical challenges of using machine learning in journalism. To better identify these, we propose a data quality assessment framework to support the collection and pre-processing stages in machine learning. It relies on three of the core principles of ethical journalism—accuracy, fairness, and transparency—and participates in the shift from model-centric to data-centric AI, by focusing on data quality to reduce reliance on large datasets with errors, making data labelling consistent, and better integrating journalistic knowledge.

Citations

• Beyond Accuracy: What Data Quality Means to Data Consumers
• The FAIR Guiding Principles for scientific data management and stewardship
• Fairness and Abstraction in Sociotechnical Systems
• A Survey of Methods for Explaining Black Box Models
• Introducing explainable supervised machine learning into interactive feedback loops for statistical production system
• Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy
• Data quality assessment
• At the crossroads of logics: Automating newswork with artificial intelligence—(Re)defining journalistic logics from the perspective of technologists

Discussions

• The paper is published in open access in Ethics and Information Technology, with a stellar Bergen’s team Dierickx, L., Opdahl, A.L., Khan, S.A. et al. A data-centric approach for ethical and trustwort [bsky, 0 points, 0 comments]