Learn from Twitter

When Algorithms Discriminate:
The Twitter Image Crop Debacle

In 2020, Twitter's image cropping algorithm sparked outrage when it was discovered to consistently favor white faces over black faces. Incident Drill helps your team practice identifying and mitigating algorithmic bias before it impacts your users and your brand.

Twitter | 2020 | Algorithmic Bug

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The Problem: Unintended Algorithmic Bias

Algorithmic bias is a serious issue that can lead to unfair and discriminatory outcomes. It arises when machine learning models are trained on biased data, leading them to perpetuate and amplify existing societal biases. Failing to address this can result in reputational damage, legal liabilities, and a loss of user trust.

PREPARE YOUR TEAM

How Incident Drill Helps You Prepare

Incident Drill provides realistic incident simulations based on real-world events like the Twitter image crop bias bug. Your team will practice identifying potential bias, investigating the root cause, and implementing solutions to prevent similar incidents from happening in your own systems. Our simulations foster collaboration and critical thinking, leading to a more robust and ethical engineering culture.

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Bias Detection Drills

Practice identifying potential sources of algorithmic bias.

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Root Cause Analysis

Simulate investigations to pinpoint the underlying causes of biased outcomes.

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Ethical AI Discussions

Facilitate conversations about ethical considerations in AI development.

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Code Review Exercises

Learn to spot code patterns that might introduce or exacerbate bias.

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Data Audit Simulations

Practice auditing training data for potential biases.

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Collaboration Training

Improve team communication and coordination during incident response.

WHY TEAMS PRACTICE THIS

Mitigate Algorithmic Bias Risks

✓ Prevent reputational damage
✓ Reduce legal liabilities
✓ Build user trust and confidence
✓ Promote ethical AI development
✓ Improve team collaboration
✓ Strengthen incident response skills

2020 Initial Algorithm Deployment

Aug 2020 User Reports of Bias Bias Detected

Sept 2020 Twitter Acknowledges Issue

Oct 2020 Algorithm Update & Testing Mitigation Attempt

How It Works

Step 1: Identify the Problem

Recognize the potential for algorithmic bias in image cropping.

Step 2: Investigate the Root Cause

Analyze the training data and model architecture for biases.

Step 3: Implement Mitigation Strategies

Develop and test solutions to reduce or eliminate the bias.

Step 4: Monitor and Evaluate

Continuously monitor the algorithm's performance and address any remaining issues.

EXPLORE MORE

Related Incidents

Be Prepared for the Next Algorithmic Challenge

Join the Incident Drill waitlist and be among the first to practice handling complex incidents like the Twitter image crop bias bug. Build a more resilient and ethical engineering team.

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When Algorithms Discriminate: The Twitter Image Crop Debacle