02:26 - Lauren’s Superpower: Remembering Useful Yet Sentimental Facts About People
03:57 - Lauren’s Professional Background
07:35 - Bias in the Downsides of AI
- Automation vs. Augmentation
- Meredith Broussard
11:15 - Media and AI/How the Media Affects People’s Perception of AI
14:32 - Concerns of Small and Midsize Businesses Pertaining to AI
18:37 - How to Mitigate Bias in AI
22:23 - Ethics in AI
25:39 - Defining Bias in AI
32:04 - Fairness vs. Accuracy in Algorithms
38:30 - Preventing Bias in AI Resources
41:00 - Working Remotely
- Proactively Communicating
- Setting Boundaries
50:45 - Diversity and Inclusion in the Workplace
John: Lauren talking about the work she’s doing to pre-educate people so they can prevent themselves from getting in trouble even before they build their models.
Chanté: It’s not enough to just be doing this internally. Bias happens in all shapes, sizes, and forms and it’s important to recognize that.
Jacob: In a biased society we can’t expect completely unbiased data; therefore we can’t train an algorithm on the theoretical equitable world that we want to create. There will always be a trace of the bias we have now.
Lauren: The first step is acknowledging the bias exists in the first place.
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