Combining AI and Behavioral Science Responsibly
If you haven’t spent the last five years living under a rock, you’ve likely heard at least one way in which artificial intelligence (AI) is being applied to something important in your life. From determining the musical characteristics of a hit song for Grammy-nominated producers1 to training NASA’s Curiosity rover to better navigate its abstract Martian environment,2 AI is as useful as it is ubiquitous. Yet despite AI’s omnipresence, few truly understand what is going on under the hood of these complex algorithms — and, concerningly, few seem to care, even when it is directly impacting society. Take for example the United Kingdom, where one in three local councils are using AI to assist with public welfare decisions, ranging from deciding where kids go to school to investigating benefits claims for fraud.3
What is AI?
In simple terms, AI describes machines that are made to think and act human. Like us, AI machines can learn from their environments and take steps towards achieving their goals based on past experiences. Artificial intelligence was first coined as a term in 1956 by John McCarthy, a mathematics professor at Dartmouth College.4 McCarthy posited that every aspect of learning and other features of human intelligence can, in theory, be described so precisely that a machine can be made to mathematically simulate them.
Back in McCarthy’s era, AI was merely conjecture that was limited in scope to a series of brainstorming sessions by idealistic mathematicians. Now, it is undergoing a sort of renaissance due to massive advancements in computing power and the sheer amount of data at our fingertips.
While the post-human, dystopian depictions of advanced AI may seem far-fetched, one must keep in mind that AI, even in its current and relatively rudimentary form, is still a powerful tool that can be used to create tremendous good or bad for society. The stakes are even higher when behavioral science interventions make use of AI. Problematic outcomes can occur when the uses of these tools are obfuscated from the public under a shroud of technocracy — especially if AI machines develop the same biases as their human creators. There is evidence that this can occur, as researchers have even managed to deliberately implement cognitive biases into machine learning algorithms according to an article published in Nature in 2018.5
References
1. Marr, B. (2017, January 30). Grammy-nominee Alex Da kid creates hit record using machine learning. Forbes. https://www.forbes.com/sites/bernardmarr/2017/01/30/grammy-nominee-alex-da-kid-creates-hit-record-using-machine-learning/#79e589832cf9
2. NASA. (2020, June 12). NASA’s Mars Rover drivers need your help – NASA’s Mars exploration program. NASA’s Mars Exploration Program. https://mars.nasa.gov/news/8689/nasas-mars-rover-drivers-need-your-help/
3. Marsh, S. (2019, October 15). One in three councils using algorithms to make welfare decisions. the Guardian. https://www.theguardian.com/society/2019/oct/15/councils-using-algorithms-make-welfare-decisions-benefits
4. Nilsson, N. J. (2009). The quest for artificial intelligence. Cambridge University Press.
5. Taniguchi, H., Sato, H. & Shirakawa, T. A machine learning model with human cognitive biases capable of learning from small and biased datasets. Sci Rep 8, 7397 (2018). https://doi.org/10.1038/s41598-018-25679-z
6. Facebook. (2017, November 27). Machine learning. Facebook Research. https://research.fb.com/category/machine-learning/
7. Baer, T., & Kamalnath, V. (2017, November 10). Controlling machine-learning algorithms and their biases. McKinsey & Company. https://www.mckinsey.com/business-functions/risk/our-insights/controlling-machine-learning-algorithms-and-their-biases
8. Knight, M. (2018, February 10). Here’s why Facebook is such an awful echo chamber. Business Insider. https://www.businessinsider.com/facebook-is-an-echo-chamber-2018-2
9. Blank, G., & Dubois, E. (2018, March 9). The myth of the echo chamber. OII | Oxford Internet Institute. https://www.oii.ox.ac.uk/blog/the-myth-of-the-echo-chamber/
10. Hrnjic, E., & Tomczak, N. (2019, July 3). Machine learning and behavioral economics for personalized choice architecture. arXiv.org e-Print archive. https://arxiv.org/pdf/1907.02100.pdf
About the Author
Julian Hazell
Julian is passionate about understanding human behavior by analyzing the data behind the decisions that individuals make. He is also interested in communicating social science insights to the public, particularly at the intersection of behavioral science, microeconomics, and data science. Before joining The Decision Lab, he was an economics editor at Graphite Publications, a Montreal-based publication for creative and analytical thought. He has written about various economic topics ranging from carbon pricing to the impact of political institutions on economic performance. Julian graduated from McGill University with a Bachelor of Arts in Economics and Management.
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