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PY/RE3721 Topics in Religion: Daoism: AI & Research

A complementary course guide for the PY/RE 3721 B2025 class on Daoism.

AI Tools for Research

PLEASE DO NOT UPLOAD COPYRIGHTED OR PRIVILEGED INFORMATION INTO ANY AI TOOL.

All of the following are considered "open" and not secure.

Beware "hallucinations" with all generative AI tools.

  • AI Resource Library by AcademicID.io
    A community-assembled collection of open-source resources and links to helpful material for academics looking to utilise AI in their research.
  • Consensus AI
    Consensus is an academic search engine powered by AI. They use LLMs and vector search technology to surface the most relevant papers. They synthesize both topic-level and paper-level insights. Its dataset of over 200 million papers was made possible by a partnership with Semantic Scholar.
  • Elicit
    Elicit is an AI-powered academic search engine trained on a knowledge base of over 125 million academic papers via the Semantic Scholar database. Summarizes papers, extracts data from PDFs, and identifies concepts. Requires an account.
  • Inciteful
    Create a network of academic papers, or input two papers and analyze their relationships.
  • Keenious
    By uploading a PDF or copy/pasting text, you can find relevant articles via the Open Alex dataset. It is known for having good privacy protection. That said, please do not upload library materials into Keenious.
  • Litmaps
    Freemium tool that uses open access metadata from Crossref, Semantic Scholar, and Open Alex to provide visualization and monitoring tools.
  • Research Rabbit
    Research Rabbit is a visual citation-mapping tool that uses PubMed and Semantic Scholar databases. Requires an account.
  • SemanticScholar
    Semantic Scholar is a database that includes over 200 million publications from all fields of science. AI features: craft a one-sentence summary (TL;DR); "ask the paper questions;" and a research feed where your ratings of previous work generates recommendations of continued reading.
  • SciSpace
    Chat with a PDF to learn more about its contents.

AI Research Ethics Guidelines

Ethical Considerations:

  • Bias and Fairness:
    • Researchers should be aware of potential biases in AI algorithms and training data that could lead to unfair or discriminatory outcomes.
    • Guidelines should emphasize the importance of using diverse and representative datasets and employing bias detection and mitigation techniques.
    • Regularly evaluate AI models for fairness across different demographic groups.
  • Transparency and Explainability:
    • Promote the use of AI models that are transparent and whose decision-making processes can be understood and interpreted (Explainable AI - XAI).
    • Document the AI methods and models used, including data sources, hyperparameters, and model performance metrics, to ensure traceability.
  • Accountability and Responsibility:
    • Clearly define the roles and responsibilities of researchers when using AI tools.
    • Researchers are accountable for the outputs generated by AI and must verify the accuracy and reliability of the information.
    • Establish mechanisms for auditing AI systems and addressing unintended consequences.
  • Privacy and Data Governance:
    • Adhere to data protection regulations and institutional policies regarding the collection, storage, and use of research data in AI applications.
    • Ensure that the privacy of research participants is maintained and that data is handled securely.
    • Understand the terms of service of AI tools, especially regarding data ownership and usage by third-party providers.
  • Informed Consent:
    • Obtain informed consent from participants when AI systems are involved in data collection or interact with human subjects.
    • Clearly explain how AI will be used in the research and the potential risks and benefits.
  • Intellectual Property and Authorship:
    • Address issues related to intellectual property rights when using AI tools for content generation or analysis.
    • Clearly define authorship responsibilities when AI contributes to research outputs, adhering to relevant publication ethics guidelines.

Best Practices for Using AI in Research:

  • Complementary Tool: AI should be viewed as a complementary resource to enhance research, not a replacement for critical thinking and researcher expertise.
  • Verification and Validation: Researchers must critically evaluate and verify the outputs generated by AI tools using reliable sources and their own expertise.
  • Literature Review: Exercise caution when using AI for literature reviews; always verify the accuracy and relevance of cited sources.
  • Data Quality: Use high-quality, well-documented data for training and analysis with AI models.
  • Transparency in Publications: Disclose the use of AI tools in research publications, including the name and version of the tool and how it was used.
  • Reproducibility: Ensure that AI-assisted research is reproducible by documenting the data, code, and AI model parameters used.
  • Human Oversight: Maintain human oversight in critical decision-making processes where AI is involved.
  • Training and Education: Provide researchers with adequate training and resources on the responsible and ethical use of AI tools.

This text was copied from Rukmal Ryder's excellent guide on AI and Information Literacy at Salem State University. Link to source.

AI Tools in Databases

Databases are rolling out 'small language model' tools that allow you to "chat with a PDF." This tool  can help you assess whether an article is relevant to your research and worth reading further, but it cannot replace actual in-depth reading of an article by human researchers. JSTOR and ScienceDirect require you to make a personal account before you can use the tool; ProQuest does not.