Prerequisites:
None. No prior experience with AI is required.
Overview:
As Artificial Intelligence (AI) toolsets become embedded in academic research, responsible use is no longer just about avoiding plagiarism—it requires navigating data privacy, IRB compliance, intellectual property, and institutional policies.
Led by Dr. Daniel R. Kirienko, CIP (Manager of Regulatory Services in Rice’s Office of Research Integrity), this workshop breaks down the ethical landscape of generative and analytical AI tools in research. It discuss what AI uses are Approved, Questionable, or Dangerous at every stage of the research lifecycle. Participants will gain practical strategies to leverage AI effectively while maintaining compliance, research integrity, and publication standards.
Topics Include:
- Study Design & Proposals: Navigating confidentiality when prompting LLMs; ethical limits of using AI to generate hypotheses or write grant applications.
- Data Collection & Processing: Safeguarding Human Subjects Data (IRB/HIPAA considerations), synthetic data generation, and limiting algorithmic bias.
- Data Analysis & Code Generation: Distinguishing between acceptable assistance (script optimization, debugging) and research misconduct (data fabrication, algorithmic halluncinations).
- Writing, Editing & Visualization: Reviewing journal disclosure policies and co-authorship ethics, and avoiding unverified or AI-manipulated figures.
- Peer Review & Dissemination: Protecting unpublished manuscripts, peer-review confidentiality rules, and intellectual property rights.
Contact information:
Please email researchdata@rice.edu if you have questions about the Data@Rice workshop series. For questions related to research integrity and ethics, email researchintegrity@rice.edu.