Michael Saxon


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Modeling Disclosive Transparency in NLP Application Descriptions
Michael Saxon | Sharon Levy | Xinyi Wang | Alon Albalak | William Yang Wang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Broader disclosive transparency—truth and clarity in communication regarding the function of AI systems—is widely considered desirable. Unfortunately, it is a nebulous concept, difficult to both define and quantify. This is problematic, as previous work has demonstrated possible trade-offs and negative consequences to disclosive transparency, such as a confusion effect, where “too much information” clouds a reader’s understanding of what a system description means. Disclosive transparency’s subjective nature has rendered deep study into these problems and their remedies difficult. To improve this state of affairs, We introduce neural language model-based probabilistic metrics to directly model disclosive transparency, and demonstrate that they correlate with user and expert opinions of system transparency, making them a valid objective proxy. Finally, we demonstrate the use of these metrics in a pilot study quantifying the relationships between transparency, confusion, and user perceptions in a corpus of real NLP system descriptions.

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Investigating Memorization of Conspiracy Theories in Text Generation
Sharon Levy | Michael Saxon | William Yang Wang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021