Prathyusha Charagondla
Algorithmic Bias- Preventing Unfairness in your Algorithms
#1about 4 minutes
How a hiring algorithm learned to be biased against women
An automated hiring tool failed because its training data, based on a male-dominated workforce, taught it to penalize female candidates.
#2about 7 minutes
Uncovering racial and gender bias in facial recognition
The Gender Shades project revealed that commercial facial recognition systems have significantly higher error rates for dark-skinned women, leading to misidentification.
#3about 4 minutes
How exam grading algorithms penalize students unfairly
Algorithms used to predict student exam scores during the pandemic unfairly downgraded high-achieving students from historically underperforming schools.
#4about 1 minute
The impact of bias and the need for future regulation
Algorithmic bias leads to real-world discrimination, and government regulations similar to GDPR may be necessary to ensure ethical AI development.
#5about 3 minutes
Preventing bias by starting with data and designing for fairness
The first steps to mitigate bias are acknowledging its existence, performing exploratory data analysis, and incorporating fairness into the design phase.
#6about 7 minutes
Creating an ethical framework for trustworthy AI
The European Union's guidelines for trustworthy AI provide a seven-point framework covering human oversight, transparency, fairness, and accountability.
#7about 1 minute
Increasing team diversity to counter groupthink and find bias
Diverse teams with varied backgrounds and perspectives are crucial for challenging assumptions, fostering innovation, and identifying potential biases early.
#8about 3 minutes
Recap of key problems and four prevention strategies
A summary of how biased algorithms cause discrimination and the four key strategies to prevent it: analyzing data, designing for fairness, using ethical frameworks, and building diverse teams.
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Increasing the value of talk recordings post-event
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The future of recruiting beyond talent acquisition
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Incentivizing automation with a 'keep what you kill' policy
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Balancing the trade-off between efficiency and resilience
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