Responsible AI for Developers: Fairness & Bias Quiz

What are the three main causes of bias in AI?

Which method ensures fairness in AI by having the same predictions when a protected feature is hidden from the model?

How can bias affect the results of an AI model according to the text?

What is a potential risk highlighted in the text regarding data exposure in AI applications?

In the context of AI ethics, what does it mean to use proxy measurements?

Which principle of responsible AI involves ensuring the same performance metrics across different subgroups?

According to the principles of responsible AI, who is liable for AI-driven decisions that result in harm?

Which of the following is not a principle of responsible AI as outlined by Microsoft?

What is one way to reduce bias in AI models?

What is a common challenge associated with AI?

Which of the following is not a common AI workload?

Which of the following is not a tool provided by Microsoft Azure for responsible AI?

Responsible AI

AI-driven decisions can lead to unintended consequences, such as an innocent person being convicted of a crime based on biased facial recognition evidence.
Microsoft's Responsible AI principles aim to address challenges and risks associated with AI, including bias, fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability.

Causes of Bias in AI

Three main causes of bias in AI are:
- Data imbalance and errors
- Using proxy measurements instead of direct measurements
- Failing to enforce equal metrics across subgroups

Measures of Fairness

Fairness in AI can be measured by:
- Group-independent predictions
- Same predictions when a protected feature (e.g. sex) is hidden from the model
- Equal metrics across subgroups (e.g. accuracy and TP/FN rates for men and women)

Methods for Fairness

To make AI fair, it's essential to:
- Ensure data is balanced and all subgroups are equally represented
- Directly measure instead of using proxy measurements
- Enforce model training to have same metrics for subgroups
- Use different decision thresholds for subgroups to counter bias

Other Challenges and Risks with AI

Errors in AI systems can cause harm, such as an autonomous vehicle experiencing a system failure and causing a collision.
AI systems can expose sensitive data, such as a medical diagnostic bot trained using insecure patient data.
Solutions may not work for everyone, such as a predictive app providing no audio output for visually impaired users.
Users must trust complex AI systems, such as an AI-based financial tool making investment recommendations without clear explanations.

Test your knowledge on fairness, bias, and ethical considerations in artificial intelligence with this quiz based on the course SEA100 Explorations of Artificial Intelligence Ethics at Seneca College. Explore causes of bias, measures of fairness, methods for ensuring fairness, as well as other challenges and risks associated with AI.