Computing Bias Chapter Notes | AP Computer Science Principles - Grade 9 PDF Download

Introduction

Computing innovations often mirror biases present in society, which can lead to unfair outcomes. This chapter explores what bias is, how it appears in computing through algorithms and data, and its harmful effects. It provides examples like biased criminal risk assessments and facial recognition systems to illustrate how biases can perpetuate inequalities. The chapter also discusses strategies to reduce bias in computing, emphasizing the importance of diverse data and fairness metrics. 

What is Bias?

• Definition: Bias refers to tendencies or inclinations, especially those that are unfair or prejudicial, often harming society when based on identity.
• Presence in society: Biases exist in individuals and the world, influencing how data is chosen for computing innovations.
• Reflection in technology: Computing innovations use real-world data, which can carry existing biases, embedding them into systems.

Examples of Bias in Computing

• Criminal Risk Assessment Tools: These tools predict the likelihood of a defendant reoffending, influencing judicial decisions. Trained on historical data, which often reflects racial and socioeconomic biases, they may unfairly flag certain groups as higher risks.
• Facial Recognition Systems: Many systems are trained on datasets with more images of white men than women or minorities, leading to biased performance. This lack of diversity in training data can cause inaccurate or exclusionary results.
• Recruiting Algorithms: Used to filter job applicants, these algorithms may favor certain demographics. For instance, if historical hires were predominantly male due to past application trends, the algorithm might prioritize male candidates, discriminating against women.

What Can We Do to Prevent Bias in Computing?

• Diverse and Representative Datasets: Using datasets that reflect the broader population helps minimize bias in machine learning models, such as improving facial recognition accuracy across demographics.
• Algorithm Audits: Regularly reviewing algorithms and testing them on diverse datasets can uncover and address potential biases before they cause harm.
• Fairness Metrics: Implementing metrics like demographic parity or equal opportunity ensures models produce equitable outcomes across groups.
• Tackling Human Bias: Developers must actively identify and mitigate human biases in system design and use, fostering fairness throughout the process.
• Promoting Diversity in Tech: A more diverse tech workforce brings varied perspectives, reducing the risk of biased systems by incorporating broader experiences.

These steps not only improve the fairness of computing systems but also contribute to a more equitable society. Since algorithms are created by people, addressing bias in technology can inspire us to confront our own biases.

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