The Dangers of Bias in Machine Generated Interfaces

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UX can be super helpful both in helping to prevent sort of bad bias as much as possible and also surface it in the algorithm when we're necessary but what I'm talking about here is that the machines only know what we give them. The risk is is that well I think that there's a hope and an assumption that the machines can be more objective than people. But ignoring the fact that there's bias at all in the system means that it is just not being honest with ourselves.

Josh Clark enlightens us to everything we need to know about the current state of UX in AI. He takes us on an unsettling stroll through uncanny valley. He encourages us to let machines do what they're really good at, and humans do what they're really good at. He guides us into how to begin getting our hands dirty with AI/Machine-Learning. He also articulates how our software/machines are embedded with values, and inspires us (for future's sake) to be intentional about the kinds of values we embed into them.

Josh passes the Turing Test (07:33)
What is AI/Machine Learning? (10:04)
Is AI’s uncertainties driving engineers nuts? (18:28)
Listener Q: How do we address AI's failure in presenting errors? (27:00)
Taking a stroll down "uncanny valley” + “mechanophilia" (34:35)
Is there hope for the UX of Voice UI? (42:05)
Will the machines we’ve built eventually overtake and destroy us? (55:08)
Will AI eventually take our jobs? (68:35)
Is there a specific area of focus that will benefit designers interested in AI? (77:50)
Listener Q: At what point in the AI development process should UX get involved and how? (91:09)
Do we take these awesome technologies for granted? If so, why do we do that? (100:13)
If today was your last day, what would be your final plea to designers and developers building the future of AI? (105:03)

Check out the detailed show notes including the transcript at userdefenders.com/uxai

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