Not Directly Stated, Not Explicitly Stored:

Larson, Martha; Oostdijk, N.H.J.; Borgesius, Frederik Zuiderveen

doi:10.1145/3450614.3463601

Cited by 5 publications

(1 citation statement)

References 9 publications

(7 reference statements)

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“…They tend to be developed without transparent ethical oversight, and are typically rolled out with profit motives that incentivise generating hype over enabling careful scientific work. They allow companies to mask exploitative labour practices, privacy implications [27] and murky copyright situations [49]. Today there is a growing division between global academia and the handful of firms who wield the computational resources required for training large language models.…”

Section: Introductionmentioning

confidence: 99%

Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators

Liesenfeld¹,

Lopez²,

Dingemanse³

2023

Proceedings of the 5th International Conference on Conversational User Interfaces

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Section: Introductionmentioning

confidence: 99%

Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators

Liesenfeld¹,

Lopez²,

Dingemanse³

2023

Proceedings of the 5th International Conference on Conversational User Interfaces

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Is Your Model Sensitive? SPEDAC: A New Resource for the Automatic Classification of Sensitive Personal Data

2023

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In recent years, there has been an exponential growth of applications, including dialogue systems, that handle sensitive personal information. This has brought to light the extremely important issue of personal data protection in virtual environments. Sensitive information detection (SID) covers different domains and languages in literature. However, if we refer to the personal data domain, the absence of a shared standard benchmark makes comparison with the state-of-the-art difficult for this task. To fill this gap, we introduce and release SPEDAC, a new annotated resource for the identification of sensitive personal data categories in the English language. SPEDAC enables the evaluation of computational models for three different SID subtasks with increasing levels of complexity. SPEDAC 1 regards binary classification, a model has to detect if a sentence contains sensitive information or not; in SPEDAC 2 we collected labeled sentences using 5 categories that relate to macro-domains of personal information; in SPEDAC 3, the labeling is finegrained and includes 61 personal data categories. We conduct an extensive evaluation of the resource using different state-of-the-art-classifiers. The results show that SPEDAC is challenging, particularly with regard to fine-grained classification. Classifiers based on the transformer architectures achieve good results on SPEDAC 1 and 2 but have difficulties to discern among fine-grained classes in SPEDAC 3.

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Holistic Multi-layered System Design for Human-Centered Dialog Systems

Oruche,

Akula,

Goruganthu

et al. 2024

2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS)

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Not Directly Stated, Not Explicitly Stored:

Abstract: The following full text is a publisher's version.For additional information about this publication click this link. https://repository.ubn.ru.nl/handle/2066/236516Please be advised that this information was generated on 2021-11-15 and may be subject to change.

Cited by 5 publications

References 9 publications

Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators

Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators

Is Your Model Sensitive? SPEDAC: A New Resource for the Automatic Classification of Sensitive Personal Data

Holistic Multi-layered System Design for Human-Centered Dialog Systems

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