Author of "The Alignment Problem" here, to say: Of course this question depends on your semantics of "harm," "AI," and "alignment," but by most definitions (and certainly by mine) the answer is overwhelmingly yes, many.
These harms can be diffuse at massive scale, and acute at small scale.
One example of each:
(1) https://www.science.org/doi/abs/10.1126/science.aax2342
One of USA’s largest health insurers builds ML system for patient triage. It optimizes for a proxy metric of health need (namely, cost) rather than health need itself; consequently it deprioritizes and systematically excludes millions of people from access to health care.
(2) https://en.wikipedia.org/wiki/Death_of_Elaine_Herzberg
Autonomous Uber car builds their braking system on top of a vision model that optimizes for object classification accuracy using categories of {"pedestrian", "cyclist", "vehicle", "debris"}; consequently it fails to determine how to classify a woman walking a bicycle across the street, as a result killing her.
In both cases, optimizing for a naively sensible proxy metric of the thing that was truly desired turned out to be catastrophic.
These harms can be diffuse at massive scale, and acute at small scale.
One example of each: (1) https://www.science.org/doi/abs/10.1126/science.aax2342 One of USA’s largest health insurers builds ML system for patient triage. It optimizes for a proxy metric of health need (namely, cost) rather than health need itself; consequently it deprioritizes and systematically excludes millions of people from access to health care.
(2) https://en.wikipedia.org/wiki/Death_of_Elaine_Herzberg Autonomous Uber car builds their braking system on top of a vision model that optimizes for object classification accuracy using categories of {"pedestrian", "cyclist", "vehicle", "debris"}; consequently it fails to determine how to classify a woman walking a bicycle across the street, as a result killing her.
In both cases, optimizing for a naively sensible proxy metric of the thing that was truly desired turned out to be catastrophic.