Blog Post by Bruce Cahan
mediaX Distinguished Visiting Scholar
Receivers. Amplifiers. Tuners. Devices from the bygone age of high fidelity sound?
Robin Hood. The legendary champion protecting ordinary people from the abuses of systems that mistreat them by ignoring their natural rights?
At the mediaX Learning Analytics Conference, I explained that in a world connected by Artificial Intelligence (AI) and Machine Learning (ML), Receivers will collect and sell all of the data about us, Amplifiers will aim a corporate or government product or service, belief or choice at us, and so we will want and rely on Tuners to ask and challenge whether with all their AL/ML personal data and their AI/ML optimized choice manipulations, the result benefits ordinary people, the corporate and government owners of AI/ML, or ideally both.
To cultivate Tuners, to create its ethical curiosity within AI and to quantify its positive impacts, my MS&E colleague David Scheinker and I proposed a Robin Hood AI Clinic where the benefits of aiming AI/ML out on behalf of people who make dozens of choices daily can be gauged and optimized to improve their quality of life, and the quality of life for the demographic or lifecycle cohort they represent.
I grew up in Philadelphia, enjoying much of my after school pre-dinner hour watching the Three Stooges, Moe, Larry and most adorably, Curly. The day before our mediaX Learning Analytics Conference, Curly would have turned 116 years old. In homage to Curly, I recalled his classic routine in A Plumbing We Will Go, where Curly is destroying an elegant home’s plumbing by connecting any available pipe to the prior pipe, and spraying water in altogether more directions, while imprisoning himself in a jungle-gym of pipes. During my talk on the 3 Algorithmists, I analogized data to the water in this scene, and each individual AI application, as another pipe, using data (water) and spraying more data (water), all unaccountably, and eventually imprisoning the engineer or the end target. Without Tuners in the mix, AI might pipe our data in ways that spray it everywhere, but don’t help our homes, businesses and quality of life improve measurably, in the bargain.
Bruce Cahan is a Lecturer in Stanford’s School of Engineering, where he designs and applies new theories for creating financial and insurance that improve regional quality of life systems. He is also CEO and co-founder of Urban Logic, a nonprofit that harnesses finance and technology to change how systems think, act and feel. He is an Ashoka Fellow and a CodeX Fellow at Stanford’s Center for Legal Informatics. Mr. Cahan was trained as an international finance lawyer at Weil Gotshal & Manges in NYC (10 years) and as merchant banker at Asian Oceanic in Hong Kong (2 years).