AI-900: How Does AI Ensure Fairness by Avoiding Bias in Decision-Making?

Learn how AI solutions address fairness by avoiding biased decision-making based on ethnicity. Explore the principles of responsible AI and their impact on ethical technology.

Question

You develop an AI solution that avoids biased decision making based on ethnicity. Which aspect of responsible AI does this solution fulfil?

A. Accountability
B. Reliability and Safety
C. Fairness
D. Transparency

Answer

C. Fairness

Explanation

Your AI solution directly addresses the challenge of biased decision-making based on ethnicity, which falls under the Fairness principle of responsible AI. This principle emphasizes that AI systems should treat everyone equally and avoid discriminatory outcomes based on certain characteristics.

Reliability and Safety is crucial for ensuring proper system functioning. However, it does not directly address bias in decision-making.

Accountability involves holding individuals responsible for AI systems, ensuring that ethical and legal standards are met. While important, it does not directly address the avoidance of biased decision making.

Transparency is important for understanding how AI systems work, but it does not specifically influence fairness in outcomes. Transparency involves making AI systems understandable, providing insights into how they work.

The six key principles of responsible AI include:

• Fairness: AI systems should treat all people fairly, avoiding biases based on factors such as gender and ethnicity.
• Reliability and Safety: AI systems should perform reliably and safely, with rigorous testing and deployment management to ensure expected functionality and minimize risks.
• Privacy and Security: AI systems should be secure and respect privacy, considering the privacy implications of the data used and decisions made by the system.
• Inclusiveness: AI systems should empower and engage everyone, bringing benefits to all parts of society without discrimination.
• Transparency: AI systems should be understandable, with users fully aware of the system’s purpose, functioning, and limitations.
• Accountability: People should be accountable for AI systems, working within a framework of governance and organizational principles to meet ethical and legal standards.

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