Computing Bias

David Foster

7 min read

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Study Guide Overview

This study guide covers bias in computing, including its definition, how it manifests in technology, and real-world examples like criminal risk assessment tools, facial recognition, and recruiting algorithms. It also details strategies to prevent bias such as using diverse datasets, algorithm review, fairness metrics, and increasing tech diversity. Finally, it provides practice questions and exam tips focusing on identifying, analyzing, and mitigating bias.

#AP Computer Science Principles: Bias in Computing - The Night Before Guide 🚀

Hey there! Feeling the pre-exam jitters? No worries, we've got this! Let's dive into a high-impact review of bias in computing, designed to make everything click right before the big day. Remember, you're not just memorizing facts; you're understanding how tech interacts with the real world. Let's go!

#Understanding Bias in Computing

#What is Bias? 🤔

Definition: Bias refers to tendencies or inclinations, especially those that are unfair or prejudicial. It's like a tilt in the way we see or do things.
Ubiquitous Nature: Everyone has biases, but some, particularly those based on identity, can be harmful. 🌍
Reflection in Tech: Computing innovations often reflect existing biases because they use data from the world – data that's already influenced by human perspectives. 💡

Key Concept

Computing innovations can inadvertently perpetuate societal biases if not carefully designed and monitored.

# Examples of Bias in Computing 💻

Bias can creep in at any stage of development. Here are some real-world examples:

Criminal Risk Assessment Tools
- Issue: These tools predict the likelihood of a defendant re-offending based on historical data.
- Problem: Historical data often reflects existing societal biases against certain races and classes, leading to disproportionate flagging of specific groups. ⚖️
- Impact: Can lead to unfair judicial decisions.
Facial Recognition Systems
- Issue: These systems are trained on datasets ...

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