Death by Algorithm
Death by Algorithm
Podcast Description
A series on autonomous weapons systems, drones and AI in the military domain. Experts from various disciplines share their research and discuss the black box, responsibility, human-machine interaction, and the future of legal and ethical frameworks for AI in war. How is war regulated? Can the ethics of war be programmed into machines? Does it change how we fight? Can war be cleaned up by technology? How can soldiers understand the systems? Will AI systems be the commanders of tomorrow? Why not just let the robots fight? Episodes are narration and interviews and not chronological
Podcast Insights
Content Themes
The podcast centers around the intersection of technology and warfare, with episodes exploring themes such as the ethical implications of AI in combat, the black box problem in military drones, and legal responsibilities around autonomous weapons. For instance, 'A Taste of Tragedy' examines the impact of autonomous systems through the lens of the Ukraine conflict, while 'Just War' questions the justification of machine-led combat and its philosophical underpinnings.
A series on autonomous weapons systems, decision support systems, and drones in the military domain. Experts from various disciplines share their research and discuss the black box, responsibility, human-machine interaction, and the future of legal and ethical frameworks for AI in war. How is war regulated? Can the ethics of war be programmed into machines? Does it change how we fight? How can soldiers understand the systems? Will AI systems be the commanders of tomorrow? Why not just let the robots fight? Episodes are narration and interviews and not chronological
So, why are autonomous weapons systems such a big deal? Aren’t they just weapons like the rest of them? Well, the black box problem with algorithmically controlled systems raises challenges different from those of “fire and forget” munitions.
Four AI experts explain.
The first part of this episode clarifies what an algorithm is when the black box appears and why it’s important.
The second part clarifies how the data the algorithms rely on can be biased and that constant maintenance and updates do not fix the problem.
Shownotes:
Producer and host: Sune With [email protected]
Cover art: Sebastian Gram
References and literature:
– Algorithmic bias, Wikipedia (Accessed April 8. 2025)
https://en.wikipedia.org/wiki/Algorithmic_bias
– Black Box, Wikipedia (Accessed April 8. 2025)
https://en.wikipedia.org/wiki/Black_box
– Blouin, Lou; Rawashdeh, Samir, March 2023, “AI´s mysterious “black box” problem. Explained”, NEWS University of Michigan-Dearborn (Accessed April 8. 2025)
https://umdearborn.edu/news/ais-mysterious-black-box-problem-explained
– Co-Coders (Accessed April 8. 2025)
– ExekTek (Accessed April 8. 2025)
– Hatherley, J. J., 2020, ”Limits of Trust in Medical AI. ” Journal of Medical Ethics, 46(7), 478-481.
– Hatherley, J., Sparrow, R., & Howard, M. (2024).”The Virtues of Interpretable Medical AI. ” Cambridge Quarterly of Healthcare Ethics, 33(3), 323-332.
– Hatherley, J. (2025).”A Moving Target in AI-Assisted Decision-Making: Dataset Shift, Model Updating, and the Problem of Update Opacity. ” Ethics and Information Technology, 27, 20.
https://link.springer.com/article/10.1007/s10676-025-09829-2 – citeas
– Hyperight, “The Black Box: What We´re Still Getting Wrong about Trusting Machine Learning Models” (Accessed April 8. 2025)
– Lystlund, Lise Bach (Accessed April 8. 2025)
– Nygreen, Jonas (Accessed April 8. 2025)
https://www.linkedin.com/in/jonasnygreen/
Music: Sofus Forsberg
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