Background

Like many institutions, my university recently announced a major investment in the exploration of artificial intelligence. New institutes are quickly being created, new initiatives being launched, and new faculty being hired to capitalize on the rapidly emerging potential of AI. In the midst of this AI transformation, I am inspired by indigenous and humanities scholars to reflect on AI and my own core values, particularly as a scientist and professor at the interface of computation, biology, and medicine. My own views on AI have swayed and changed over the years and will undoubtedly continue to refine with new developments. By explicating these core values, I hope I can (continue to) lean on them for guidance through these uncertain times.

Inspiration:

Short Film Video

Transcript:

What is AI? Is it a revolution? Is it salvation? Is it God? Is it an extension of colonial practices of exploitation? Of extraction? Of control? Or is it another tool to be wielded with responsibility and accountability through relationality and reciprocity.

In the midst of this AI transformation, I’m inspired by indigenous and humanities scholars to reflect on my own core values, particularly as a scientist and professor at the interface of computation, biology, and medicine. By explicating these core values, I hope I can (continue to) lean on them for guidance through these uncertain times.

To me, AI is a collaboration between myself and a machine. Through our collaboration, I aim to do more than what I am able to do alone (such is the nature of a good collaboration).

I am developing AI tools to be integrated into my scientific workflow. My use of AI must support the relationship between me as a scientist and the data that I seek to understand to further our collective understanding of the underlying processes that gave rise to such data.

I believe it is up to us as computational scientists to develop AI that reflects and enacts our best practices and values and to continuously shape and reshape AI as effective members of our scientific community so they can work with us to help our communities grow and thrive.

And we must prepare for this AI future by recommitting to our core mission of training students:

  • to be critical thinkers across computational and other specialized domains
  • to consider and understand data in the context in which they have been collected and generated
  • to evaluate AI algorithms for the problems presented to them, decide which analysis approaches may be appropriate based on features of the data, and if none fits, devise new analysis by integrating and combining statistical and AI techniques in innovative new ways,
  • to apply these analyses towards the discovery of immutable truths that can lay the stable foundation upon which other scientists can build
  • to communicate the details of these discoveries through precisely worded manuscripts, with clear and salient data visualizations, and well-documented open-source software and/or reproducible code
  • and to be able to, in the future, if and when the opportunity arises, integrate this training with their own diverse life experiences in deciding for themselves whether a problem is worth pursuing and be fully prepared with these fundamental skills to tackle those problems.

So what is AI to you?