Bias
Systematic errors or prejudices in AI models that can lead to unfair or skewed outcomes.
Description
Bias in AI refers to systematic errors or prejudices present in AI models that can lead to unfair, discriminatory, or skewed outcomes. These biases can stem from various sources, including biased training data, flawed algorithm design, or the reflection of societal biases in the data. AI bias is a significant ethical concern, as it can perpetuate or amplify existing social inequalities when AI systems are deployed in real-world applications. Recognizing and mitigating bias is crucial for developing fair and equitable AI systems.
Examples
- 👥 Gender bias in hiring algorithms
- 🏠 Racial bias in loan approval systems
- 👮 Bias in predictive policing models
Applications
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