AI ethics in research isn’t a theoretical topic — it’s a set of practical decisions that affect your daily work. Here are the questions that matter and how to think through them.
Who Bears Responsibility for AI-Generated Content?
You do. This is unambiguous in every institutional and publisher policy. If Claude generates a summary of a paper that contains a factual error, and you include that error in your publication without verifying it, you have published incorrect information. The AI is a tool; you are the author. Error from an AI tool is professionally equivalent to error from a research assistant — you’re responsible for the output of tools you use.
Data Privacy in AI Tool Use
When you paste content into ChatGPT, Claude.ai, or Perplexity, that content may be used for model training (depending on your account settings and terms of service). For sensitive data — confidential patient information, pre-publication findings, proprietary business partner data, classified government research — you need to verify whether the tool’s data handling meets your IRB approval, NDAs, and institutional policies. Local models (Ollama + Llama) avoid this issue entirely. Claude API with a commercial subscription has different data handling terms than the free tier.
Algorithmic Bias in AI Research Tools
Literature search AI tools are not neutral — their training data, relevance algorithms, and language coverage shape what literature you find. Tools trained primarily on English language sources will miss non-English literature even when it’s relevant. Tools with industry funding may surface industry-sponsored research differently. Be aware of these biases when evaluating whether your literature search is comprehensive.
Consent in AI-Assisted Research
If your research involves human subjects and you’re using AI to analyze qualitative data (transcripts, survey responses), your IRB protocol should address this. Sending identifiable participant data to a commercial AI API may violate informed consent if participants weren’t told this would happen. Check your consent forms and IRB approval language before using AI on human subjects data.
Environmental Cost
Training and running large AI models has significant energy costs. A single query to a large model consumes more energy than a Google search. For intensive use (batch processing thousands of documents), the environmental impact is material. Using smaller, more efficient models where quality is sufficient, and local models where privacy is required, reduces this impact. This is a real consideration in institutional sustainability commitments.
