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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.04116 |
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| _version_ | 1866910829565181952 |
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| author | Mancini, Eleonora Tanevska, Ana Galassi, Andrea Galatolo, Alessio Ruggeri, Federico Torroni, Paolo |
| author_facet | Mancini, Eleonora Tanevska, Ana Galassi, Andrea Galatolo, Alessio Ruggeri, Federico Torroni, Paolo |
| contents | Current research in machine learning and artificial intelligence is largely centered on modeling and performance evaluation, less so on data collection. However, recent research demonstrated that limitations and biases in data may negatively impact trustworthiness and reliability. These aspects are particularly impactful on sensitive domains such as mental health and neurological disorders, where speech data are used to develop AI applications for patients and healthcare providers. In this paper, we chart the landscape of available speech datasets for this domain, to highlight possible pitfalls and opportunities for improvement and promote fairness and diversity. We present a comprehensive list of desiderata for building speech datasets for mental health and neurological disorders and distill it into an actionable checklist focused on ethical concerns to foster more responsible research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04116 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Research Mancini, Eleonora Tanevska, Ana Galassi, Andrea Galatolo, Alessio Ruggeri, Federico Torroni, Paolo Artificial Intelligence Computation and Language Current research in machine learning and artificial intelligence is largely centered on modeling and performance evaluation, less so on data collection. However, recent research demonstrated that limitations and biases in data may negatively impact trustworthiness and reliability. These aspects are particularly impactful on sensitive domains such as mental health and neurological disorders, where speech data are used to develop AI applications for patients and healthcare providers. In this paper, we chart the landscape of available speech datasets for this domain, to highlight possible pitfalls and opportunities for improvement and promote fairness and diversity. We present a comprehensive list of desiderata for building speech datasets for mental health and neurological disorders and distill it into an actionable checklist focused on ethical concerns to foster more responsible research. |
| title | Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Research |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2406.04116 |