ISES Membership Survey on AI Use and Needs

The ISES Ethics Committee is interested in learning about member use of AI tools in their work, concerns about using these tools, and how ISES can provide support for its members on this topic. Please complete this <5 minutes survey. The survey also provides an opportunity to express your interest in forming a Working Group on AI and exposure science.

Current Role

Familiarity with AI

2.How familiar are you with large language models (LLMs) like ChatGPT, Google Gemini, and Meta AI?(Required.)

Not familiar

Somewhat familiar

Moderately familiar

Very familiar

3.How familiar are you with machine learning (ML) methods like random forest, neural network, gradient boosting, and decision tree?(Required.)

Not familiar

Somewhat familiar

Moderately familiar

Very familiar

Current Use

4.To what extent do you currently use AI tools in your work? (e.g., exposure assessment, statistical modeling, data analysis, risk communication, writing, teaching)?

Areas of Application

5.In which areas of exposure science do you see the greatest current or potential applications of large language models (LLMs) and machine learning (ML) methods? (Select all that apply)(Required.)

Other (please specify)

Prioritization

6.How important is the integration of AI methods to advancing exposure science over the next 5 years?(Required.)

Barriers

7.What are the main challenges you face (or anticipate) in using AI in your work? (Select up to 3)(Required.)

Computational resources

Data availability or quality

Ethical or regulatory concerns

Lack of transparency or interpretability of AI models

Limited access to training or expertise

Limited funding

Mitigating environmental impacts from AI use

Other (please specify)

Ethical Considerations

8.What are your biggest ethical concerns regarding the use of AI in exposure science? (Select up to 3 options.)(Required.)

Bias or poor data quality leading to inaccurate or inequitable outcomes

Data privacy and potential misuse of sensitive information

Ethical or sustainability implications of AI development and use (e.g., environmental footprint, long-term societal impact)

Lack of transparency or explainability in AI models (“black box” issue)

Overreliance on AI without adequate expert review or validation

Unclear accountability for AI-generated errors

Use of AI in academic settings (e.g., teaching, assignment completion)

Other (please specify)

9.In which areas of exposure science do you think ethical challenges related to AI use are most likely to arise? (Select up to 3 options.)(Required.)

Communication with study participants or the public (e.g., use of AI summaries or chatbots)

Data analysis and modeling (e.g., use of LLM or ML)

Data collection (e.g., use of photography, phones, or other personal exposure measurement equipment)

Data management (e.g., consent, data privacy, data sharing)

Interpretation and communication of findings (e.g., explaining uncertainty, report back of biomonitoring results)

Peer review and publication (e.g., use of AI in the peer review process)

Other (please specify)

Support Needs

10.What kinds of support could ISES provide that would most help you integrate AI into your work? (Select all that apply)(Required.)

Access to shared datasets or tools

Case studies or best-practice guidelines

Collaborative opportunities with AI experts

Development of safeguards to ensure responsible AI use in exposure science

Funding opportunities specific to AI projects

Trainings or workshops on AI methods

Webinars on AI methods

Other (please specify)

11.Are you interested in forming an official ISES Working Group on Artificial Intelligence to advise on this topic? If yes, please provide your name, affiliation and email address.(Required.)