Understanding Unwanted Bias in Technology and AI

Today's world has a lot of unwanted bias that filters what we see and what we know about our world. Let's make it better. Watch video below for a beginner's guide to bias in AI.

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Areas where we must mitigate unwanted bias

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  • Healthcare

    Misdiagnosis or delays in treatment have hurt many women and people of color

  • Ads and Data

    Ads have improperly been targeted to exclude people from certain offers based on a race and geography

  • Policing and Lack of Transparency

    Without transparency or laws to prevent abuse police departments do not undergo audits for fairness or human rights

  • AI In HR

    HR platforms utilize AI to understand candidacy and to rank resumes. Often candidates do not have a way to challenge these computer based assessments

  • Financial Lending

    Historically descriminatory towards people of color, the bias has made its way through housing markets and into buisness development and venture capital.

  • Manufacturing

    Many workers have robotic teammates and work side by side with them. AI models are also used to train the workforce. 

What we can make better

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More Data

AI models must use large datasets to mitigate bias

More Feedback

Mitigating bias in AI means auditing our models for bias

Listen to marginalized voices

Black Women have been speaking on this issue. And research conferences do not highlight their work.

"Correlation is not Causation"

In the video below I disscuss what happens if we do not understand how poverty or marginilization plays a role in people's lives.

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The way we use algorithms can have serious consequences for our collective knowledge of each other.

I have offered both a keynote talk and a workshop which featured:

  • Fairness AI toolkits from top tech companies
  • Framework for presenting ideas to colleagues
  • Workbook on the Bias in AI journey
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Recognizing Cultural Bias in AI

My name is Camille Eddy. I'm a robotics and product Engineer. I have traveled internationaly to present my ideas on our need to understand bias in AI. My hope is to give engineers and managers alike an opportunity to examine our work and build better technology. I have begun this journey myself to understand how I might mitigate bias in the technology I am working with today.

Interested?

Send me an email if you have an event or writeup and would like to collaborate on this topic!

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