Summary of Embedding Privacy in Computational Social Science and Artificial Intelligence Research, by Keenan Jones and Fatima Zahrah and Jason R.c. Nurse
Embedding Privacy in Computational Social Science and Artificial Intelligence Research
by Keenan Jones, Fatima Zahrah, Jason R.C. Nurse
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Emerging Technologies (cs.ET); Human-Computer Interaction (cs.HC)
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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 A new paper highlights the importance of preserving privacy in computational social science (CSS), artificial intelligence (AI), and data science domains. Advanced models can quickly infringe on individuals’ privacy rights, especially for vulnerable groups, if not designed with privacy considerations. The increasing use of large language models like ChatGPT demonstrates the need to embed privacy from the start. The paper discusses the role of privacy in these domains and presents key considerations for researchers to ensure participant privacy is best preserved. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Privacy is a human right that allows individuals to engage online or offline without fear of their data being misused. In areas like computational social science, artificial intelligence, and data science, preserving privacy is crucial because it affects not only individuals but also society as a whole. The paper explains how researchers in these fields can ensure participant privacy by considering design, collection, analysis, and dissemination of research results. |
