Prathyusha Charagondla

Algorithmic Bias- Preventing Unfairness in your Algorithms

Amazon's hiring algorithm penalized female applicants. Learn how to prevent your systems from amplifying historical bias and creating unfair outcomes.

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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Matching moments

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How AI can create more human moments in HR

The Future of HR Lies in AND – Not in OR

Moving from 'or' to 'and' thinking in HR strategy
06:59 MIN

Moving from 'or' to 'and' thinking in HR strategy

The Future of HR Lies in AND – Not in OR

Shifting from talent acquisition to talent architecture
03:28 MIN

Shifting from talent acquisition to talent architecture

The Future of HR Lies in AND – Not in OR

How the HR function has evolved over three decades
05:10 MIN

How the HR function has evolved over three decades

The Future of HR Lies in AND – Not in OR

Navigating ambiguity as a core HR competency
04:22 MIN

Navigating ambiguity as a core HR competency

The Future of HR Lies in AND – Not in OR

Understanding global differences in work culture and motivation
06:10 MIN

Understanding global differences in work culture and motivation

The Future of HR Lies in AND – Not in OR

Balancing business, technology, and people for holistic success
06:51 MIN

Balancing business, technology, and people for holistic success

The Future of HR Lies in AND – Not in OR

The importance of a fighting spirit to avoid complacency
06:04 MIN

The importance of a fighting spirit to avoid complacency

The Future of HR Lies in AND – Not in OR

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