AI-900: How to Avoid Bias in AI Solutions

Learn what bias is, how it affects AI solutions, and how to prevent it using various techniques.

Question

There are challenges and risks associated with developing Al solutions. Which of the following statements is true?

A. An Al algorithm is always correct.
B. Bias can affect results.
C. Humans are not responsible for AI-driven decisions.
D. Al solutions are always more reliable than humans.

Answer

B. Bias can affect results.

Explanation

Feedback: Al systems can inadvertently incorporate bias based on gender, ethnicity, or other factors that can result in an unfair advantage or disadvantage to specific groups of people.

The correct answer is B. Bias can affect results.

Bias is a systematic error or deviation from the truth that can affect the performance and fairness of an AI solution. Bias can be introduced in different stages of the AI development process, such as data collection, data preparation, model training, model evaluation, and model deployment. Bias can also come from different sources, such as human prejudices, cultural assumptions, historical imbalances, or algorithmic limitations.

Some examples of bias in AI are:

• Facial recognition systems that perform poorly on people of certain skin tones or genders.
• Natural language processing systems that generate offensive or stereotypical sentences based on the input.
• Recommendation systems that reinforce existing preferences or beliefs of the users, creating echo chambers or filter bubbles.
• Credit scoring systems that discriminate against certain groups of people based on their demographic or socio-economic characteristics.

Bias can have negative impacts on the trustworthiness, reliability, and ethicality of AI solutions. Therefore, it is important to identify, measure, and mitigate bias in AI, using various techniques such as data augmentation, data anonymization, data balancing, feature selection, model explainability, model auditing, model testing, and human oversight.

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