Trust is key to any effective technology implementation. If people don't trust it, they're not going to use it. This challenge comes up again and again, especially with AI. While we have mountains of data to train systems on, creating a system that users trust demands thoughtful use of data to ensure meaningful results, and therefore, trustworthy AI.
An example of how this shows up in the real world is the case of seatbelts. Seatbelts are safer for men than women. Why? Data bias. When seatbelts were initially designed and safety tested, the test dummies were modeled on the average body dimensions of men who served in the U.S. Army in the 1960s. Could this existing data be used to create AI that predicts injury risks as accurately for women as for men? Unlikely. But does that mean it's not trustworthy?
All systems have weaknesses. AI struggles more than traditional software because AI performance depends on the data quality, population properties, and the ability of a machine-learning algorithm to learn a function. You need to study how those features affect the problem to solve in order to build in failsafe mechanisms to warn users when the AI becomes unreliable.
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