When Explainability Meets Privacy: An Investigation at the Intersection
  of Post-hoc Explainability and Differential Privacy in the Context of Natural
  Language Processing
In the study of trustworthy Natural Language Processing (NLP), a number of
important research fields have emerged, including that of
explainability and privacy. While research interest in both
explainable and privacy-preserving NLP has increased considerably in recent
years, there remains a lack of investigation at the intersection of the two.
This leaves a considerable gap in understanding of whether achieving
both explainability and privacy is possible, or whether the two are at
odds with each other. In this work, we conduct an empirical investigation into
the privacy-explainability trade-off in the context of NLP, guided by the
popular overarching methods of Differential Privacy (DP) and Post-hoc
Explainability. Our findings include a view into the intricate relationship
between privacy and explainability, which is formed by a number of factors,
including the nature of the downstream task and choice of the text
privatization and explainability method. In this, we highlight the potential
for privacy and explainability to co-exist, and we summarize our findings in a
collection of practical recommendations for future work at this important
intersection.
689f01b1a4caabb4320e5ea2
Mahdi Dhaini
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2025-08-19T10:49:49.505Z
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Mahdi Dhaini
user
mdhaini
689f01b1a4caabb4320e5ea3
Stephen Meisenbacher
689f01b1a4caabb4320e5ea4
Ege Erdogan
689f01b1a4caabb4320e5ea5
Florian Matthes
689f01b1a4caabb4320e5ea6
Gjergji Kasneci
When Explainability Meets Privacy: An Investigation at the Intersection
  of Post-hoc Explainability and Differential Privacy in the Context of Natural
  Language Processing
In the study of trustworthy Natural Language Processing (NLP), a number of
important research fields have emerged, including that of
explainability and privacy. While research interest in both
explainable and privacy-preserving NLP has increased considerably in recent
years, there remains a lack of investigation at the intersection of the two.
This leaves a considerable gap in understanding of whether achieving
both explainability and privacy is possible, or whether the two are at
odds with each other. In this work, we conduct an empirical investigation into
the privacy-explainability trade-off in the context of NLP, guided by the
popular overarching methods of Differential Privacy (DP) and Post-hoc
Explainability. Our findings include a view into the intricate relationship
between privacy and explainability, which is formed by a number of factors,
including the nature of the downstream task and choice of the text
privatization and explainability method. In this, we highlight the potential
for privacy and explainability to co-exist, and we summarize our findings in a
collection of practical recommendations for future work at this important
intersection.
The study investigates the relationship between privacy and explainability in NLP, using Differential Privacy and Post-hoc Explainability methods, and provides recommendations for balancing both.
Differential Privacy Post-hoc Explainability