AI ethics lessons about autonomous systems is gleaned from a recent Alaskan Airlines flight that had the pilot and copilot vigorously disagree and opt to taxi back to the terminal. Resolving disagreements between AI and human-in-the-loop operators will be an arising challenge.
As you might directly guess, trying to pin down the specifics underlying these principles can be extremely hard to do. Even more so, the effort to turn those broad principles into something entirely tangible and detailed enough to be used when crafting AI systems is also a tough nut to crack.
ML/DL is a form of computational pattern matching. The usual approach is that you assemble data about a decision-making task. You feed the data into the ML/DL computer models. Those models seek to find mathematical patterns. After finding such patterns, if so found, the AI system then will use those patterns when encountering new data. Upon the presentation of new data, the patterns based on the “old” or historical data are applied to render a current decision.
You could somewhat use the famous or infamous adage of garbage-in garbage-out. The thing is, this is more akin to biases-in that insidiously get infused as biases submerged within the AI. The algorithm decision-making of AI axiomatically becomes laden with inequities.
Third, there usually has to be something seriously on the line for these guidelines to come into play.
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