2024 Essay Challenge 1

Essay Challenge 1: Trustworthy AI for Biomedical Science 

Artificial Intelligence continues to accelerate advancements in biomedical science in a variety of fronts, such as image-based diagnostics, behavior prediction, and strategies for engineering movement in cases of injury, birth defects, or various neurological and cardiovascular conditions. 

Examine the ethical and scientific implications of utilizing a scientific AI training dataset or AI process in biomedical science with applications toward scientific discovery.

If you choose to focus on training data, you may choose multiple examples of AI training sets to support your reasoning or focus on a single example. The data must be publicly available and published by a reputable source. Judges should be able to access the data set(s).  

If you choose to focus on AI algorithms or processes, detail the steps taken for your analysis of how the process was engineered and how it is used. 

Choose a particular ethical framework for your discussion. 

Optional: Demonstrate an AI model trained with the dataset to support arguments for responsible and accurate AI applications in scientific research.

 Your submission should be crafted for a general audience as much as possible. Remember that we are open to creative formats for your work. 

Guidance

We need to think early and often about the potential biases inherent in AI algorithms and training data. Ultimately you need to explore what needs to be done to demonstrate that the AI process or training data can be trusted to deliver rigorous and ethical science over time. You need to define specifically what you mean by rigorous and ethical.

The questions below are not meant to be prescriptive but are there to help you think about how to frame your discussion. 

  • Explore ethical principles that can inform the development of AI technologies in biomedical science, ensuring they align with societal values and environmental sustainability. What particular ethical frameworks exist to help us think about building trustworthy AI for biomedical science?
  • Discuss how AI can enhance the scientific method in biomedical science, from data collection to hypothesis testing and theory development.
  • Highlight the importance of data provenance, transparency, and techniques to identify and mitigate biases within AI models.
    Address the ability of AI systems to reproduce findings and adapt to new data in the dynamic field of biomedical science.
  • Outline strategies for managing errors and quantifying uncertainty within AI models, ensuring reliable and actionable science.
    Propose methods for dataset creation and AI process engineering that aim to reduce the potential for unforeseen biases or errors.
  • Consider how the ethical use of AI in biomedical science must evolve in response to technological advancements and changing regulations.

Background Material

  1. Ntoutsi, E, Fafalios, P, Gadiraju, U, et al. Bias in data-driven artificial intelligence systems—An introductory survey. WIREs Data Mining Knowl Discov. 2020; 10:e1356. https://doi.org/10.1002/widm.1356

  2. Liang, W., Tadesse, G.A., Ho, D. et al. Advances, challenges and opportunities in creating data for trustworthy AI. Nat Mach Intell 4, 669–677 (2022). https://doi.org/10.1038/s42256-022-00516-1

  3. Dubber, Markus Dirk, Frank Pasquale, and Sunit Das, eds. The Oxford handbook of ethics of AI. Oxford Handbooks, 2020.

  4. Ethical Framworks 101- https://aese.psu.edu/teachag/curriculum/modules/bioethics-1/what-are-ethical-frameworks