Summary of Towards Algorithmic Fidelity: Mental Health Representation Across Demographics in Synthetic Vs. Human-generated Data, by Shinka Mori et al.
Towards Algorithmic Fidelity: Mental Health Representation across Demographics in Synthetic vs. Human-generated Data
by Shinka Mori, Oana Ignat, Andrew Lee, Rada Mihalcea
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY)
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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 In this paper, researchers explore the potential of using GPT-3, a large language model (LLM), to generate synthetic data for sensitive applications like mental health. They analyze how different demographics are represented in the generated data and identify the predominant stressors for each group. The authors develop HEADROOM, a dataset of 3,120 posts about depression-triggering stressors, controlling for race, gender, and time frame. They then conduct semantic and lexical analyses to compare their synthetic data with a human-generated dataset. This study sheds light on the limitations of LLMs for synthetic data generation in depression research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists use a special computer program (GPT-3) to create fake data about mental health. They want to know if this data is fair and represents different groups of people. To do this, they created a big dataset with 3,120 posts about stressors that can trigger depression. They looked at how these stressors are represented in the generated data and compared it to real data created by humans. This study helps us understand what kinds of stressors GPT-3 assigns to different groups, which is important for making sure this type of data is accurate and fair. |
Keywords
» Artificial intelligence » Gpt » Large language model » Synthetic data
