What will it be like when machines make and execute decisions without any human intervention? Why would we make such systems and what are their implications for the future of human judgment and free will? Hundreds, if not thousands, of science fiction stories tell us it's a bad idea to build automated systems without "Human-in-the-loop" (HITL) processes for keeping them in check. In real life, the need for human intervention before executing an automated process is most obvious when it has serious, irreversible consequences: like killing a person with a drone. In high stakes situations like drone strikes, humans make the difficult judgment call before the weapon's deadly automation kicks in.
Let's expand this question asked by Alan Turing in the 50s. The countless disaster scenarios, in which artificial intelligence (AI) takes over the world and destroys humanity, are already made-up and still being told in Hollywood. AI has not yet taken control of humanity, but it has indeed taken control of many aspects of our lives even if we do not perceive it as such. We accept AI as a part of our lives. The simplest example is our smartphones!
This workshop brought together 20 computer scientists, psychologists, and human-computer interaction (HCI) researchers to exchange results and views on human error and judgment bias. Human error is typically studied when operators undertake actions, but judgment bias is an issue in thinking rather than acting. Both topics are generally ignored by the HCI community, which is interested in designs that eliminate human error and bias tendencies. As a result, almost no one at the workshop had met before, and the discussion for most participants was novel and lively.
Thee symposium focused on combining human and machine inference. For unique events and data-poor problem, there is no substitute for human judgment. Even for data-rich problems, human input is needed to account for contextual factors. However, human are notorious for underestimating the uncertainty in their forecasts and even the most expert judgments exhibit well-known cognitive biases. The challenge is therefore to aggregate expert judgment such that it compensates for the human deficiencies. We hope that bringing researchers in this venue will provide meaningful discussions and further inspire interesting research in this direction.
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