Summary of Promoting the Responsible Development Of Speech Datasets For Mental Health and Neurological Disorders Research, by Eleonora Mancini et al.
Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Research
by Eleonora Mancini, Ana Tanevska, Andrea Galassi, Alessio Galatolo, Federico Ruggeri, Paolo Torroni
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the significance of data collection in machine learning and artificial intelligence, highlighting the limitations and biases that can negatively impact trustworthiness and reliability. Specifically, it focuses on speech data used for AI applications in mental health and neurological disorders, where fairness and diversity are crucial. The authors present a comprehensive list of desiderata for building speech datasets and provide an actionable checklist focused on ethical concerns to promote responsible research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that the data we use to train artificial intelligence (AI) models is good quality and fair. Right now, AI researchers are mostly focused on how well their models work, but they need to start thinking more about where this data comes from. This matters a lot when we’re talking about sensitive areas like mental health and neurological disorders. The authors of this paper want to make sure that the speech data used in these areas is trustworthy and reliable. They do this by listing out what makes good speech data and providing some easy-to-follow guidelines for researchers. |
Keywords
» Artificial intelligence » Machine learning
