Citations & References

[1]

Nosek, B. A., Alter, G., Banks, G. C., et al. (2015). "Promoting an Open Research Culture."Science, 348(6242), 1422–1425.

Foundational work on open science practices and reproducibility. Establishes key principles: data transparency, method documentation, pre-registration.

[2]

Peng, R. D. (2011). "Reproducible Research in Computational Science."Science, 334(6060), 1226–1227.

Defines reproducibility in computational research. Core concept: "An article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship."

[3]

Baker, M. (2016). "1,500 Scientists Lift the Lid on Reproducibility."Nature, 533(7604), 452–454.

Large-scale survey of reproducibility crisis. 90% of researchers face reproducibility challenges; 50% have failed to reproduce their own results.

[4]

Lichterman, J. D. (2019). "How Journalists Can Better Verify Misinformation and Manipulated Media." Nieman Lab.

Practical framework for verification in newsrooms. Covers evidence hierarchies, source evaluation, and documentation standards.

[5]

Graves, L. (2018). Understanding the Promise and Limits of Automated Fact-Checking. Shorenstein Center, Harvard University.

Critical analysis of automated fact-checking: capabilities, limitations, and the role of human judgment. Key insight: automation should augment, not replace, verification.

[6]

Bounegru, L., Chambers, L., & Gray, J. (2012). The Data Journalism Handbook. European Journalism Centre.

Foundational handbook for evidence-driven journalism. Covers data sourcing, verification, and transparent documentation of analysis.

[7]

Wardle, C., & Derakhshan, H. (2017). "Information Disorder: Toward an Interdisciplinary Framework for Research and Policy Making." Shorenstein Center, Harvard University.

Defines three categories of information disorder (misinformation, disinformation, malformation) and verification strategies for each.

[8]

Vraga, E. K., & Bode, L. (2017). "Using Expert Sources to Correct Health Misinformation in Social Media." Science Communication, 39(1), 144–166.

Empirical study of fact-checking efficacy. Shows that transparent, detailed corrections outperform simple "true/false" labels.

[9]

Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (2018). "FEVER: a Large-scale Dataset for Fact Extraction and Verification." In Proceedings of NAACL-HLT 2018.

Introduces FEVER dataset and fact verification task. Establishes benchmarks for NLI-based verification. Central to AI transparency in evidence evaluation.

[10]

Dagan, I., Roth, D., Sammons, M., & Zanzotto, F. M. (2013). "Recognizing Textual Entailment: Models and Applications." Synthesis Lectures on Human Language Technologies, 6(4), 1–220.

Foundational textbook on Natural Language Inference. Covers formal semantics, inference strategies, and evaluation metrics.

[11]

Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015). "A Large Annotated Corpus for Learning Natural Language Inference." In Proceedings of EMNLP 2015.

Introduces SNLI dataset. Standard benchmark for NLI evaluation and model development.

[12]

Williams, A., Nangia, N., & Bowman, S. R. (2018). "A Broad-Coverage Challenge Corpus for Natural Language Inference." In Proceedings of ACL 2018.

MultiNLI dataset: cross-genre NLI. Addresses robustness and domain generalization in inference models.

[13]

Lipton, Z. C. (2016). "The Mythos of Model Interpretability."arXiv:1606.03490.

Critical analysis of interpretability claims in deep learning. Distinguishes between transparency, post-hoc explanation, and true understanding.

[14]

Buolamwini, B., & Gebru, T. (2018). "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification." In Conference on Fairness, Accountability and Transparency (FAccT).

Demonstrates how lack of transparency in AI systems can hide systematic failures. Case study: commercial face recognition systems.

[15]

Mitchell, M., Wu, S., Zaldivar, A., et al. (2019). "Model Cards for Model Reporting." InProceedings of FAccT 2019.

Introduces Model Cards: transparent documentation of model capabilities, limitations, and training data. Practical standard for AI accountability.

[16]

Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). "Design Science in Information Systems Research." MIS Quarterly, 28(1), 75–105.

Foundational paper on Design Science Research. Establishes the iterative cycle: problem identification, solution design, evaluation, and learning.

[17]

Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). "A Design Science Research Methodology for Information Systems Research." Journal of Management Information Systems, 24(3), 45–77.

DSRM framework: six-step process from problem definition through artifact evaluation and communication. Standard methodology in IS research.

[18]

Iivari, J., Hirschheim, R., & Klein, H. K. (1998). "Towards a Distinctive Body of Knowledge for Information Systems Experts: Coding ISD Research Literature." Information Systems Journal, 8(4), 313–342.

Categorizes research: behavioral science (predictions and explanations) vs design science (building and evaluating artifacts).

[19]

Thorne, J., Vlachos, A., Chrysostomou, C., et al. (2019). "Fact-Checking Meets Fauxtography: Verifying Claims About Images." In Proceedings of EMNLP 2019.

Multimodal fact-checking: combines NLI for text with image verification. Extends verification beyond text-only claims.

[20]

Augenstein, I., Parsons, S., Shadbolt, N., & Wyner, A. (2016). "Stance Extraction in Social Media Corpora and Stance Classification in News Corpora." In Proceedings of the 54th Annual Meeting of the ACL.

Stance detection: determining agreement/disagreement without explicit claims. Useful for detecting contradictory evidence.