Week 1 Discussion
From studying evolutionary adaptation in the Galapagos to measuring the universe's age, analyzing climate change, and understanding children’s theory of mind, scientific practices appear vastly diverse yet retain a practical and social commonality. While they all belong to the broader framework of science, what binds them is a shared commitment to systematic observation, the pursuit of evidence, a detectable truth and use of multiple methods to build robust understanding of ourselves and the worlds around and beyond us. Whether through spectroscopy, CRISPR-Cas9, or experiments with genetic variants, each example reflects the accumulation of evidence over time, embraces complexity, and revises our understanding as new information emerges.
But such diversity raises philosophical questions. Is the unity of science in its method, or in its aim to uncover observable, replicable truths about the world? If science reflects a means of building consensus through evidence, then how do we address a climate where consensus itself is under siege? What counts as science becomes even more urgent in a time when politicized post-truth culture and the erosion of trust in evidence-based discourse is accelerating in its challenge to scientific authority.
The rise of artificial intelligence further frustrates this conversation. AI technologies have begun to participate in scientific discovery, from protein folding to climate modeling, but in doing so challenge traditional human-driven science. Can we trust observable discoveries made by algorithms we barely understand? Operating with a speed and scale already beyond human capability, it also risks perpetuating biases encoded in training data, introducing questions as to whether the practice of science is fundamentally a human practice or if it can expand to include non-human agents.
Science relies not just on internal rigor but on public trust. When we observe dissemination of misinformation outpacing the efforts of researchers to communicate their findings, science risks losing its social relevance. For example, despite overwhelming evidence of human-induced climate change, public debates continue to be mired in contentious politicized disagreement and disinformation far beyond just asking questions of the data. If science is a method of inquiry and a set of shared values - objectivity, skepticism, and openness - then its survival also depends on societies which value those same principles.
In a post-truth world, perhaps it’s not enough for science to accumulate and communicate evidence. It must also navigate a shifting cultural landscape where evidence is frequently dismissed or distorted. As such, we face philosophical challenges about what counts as knowledge and who might be trusted to produce it.
Ultimately, while the examples we explored this week showcase the brilliance and adaptability of scientific practice, they also remind us science doesn’t exist in a vacuum. Its strength lies in its methods, but its social survival depends on a broader, shared cultural commitment to truth, evidence, and rational discourse. In navigating the current era of misinformation and artificial intelligence, the philosophical questions science raises are not just academic. They are existential.
Some interesting links I found on these topics:
Science in the Post-Truth Era: https://www.americanscientist.org/article/science-in-the-post-truth-era
Lee McIntyre on science in the post-truth era: Lies, facts and deniers: https://www.climateforesight.eu/interview/science-in-the-post-truth-era-trust-facts-and-deniers/
Beyond Misinformation: Understanding and Coping with the “Post-Truth” Era: https://cssn.org/wp-content/uploads/2020/12/Beyond-Misinformation-Understanding-and-Coping-with-the-Post-Truth-Era-Stephan-Lewandowsky.pdf
Living in a Post-truth World: https://www.math.columbia.edu/~woit/wordpress/?p=14214
