How can explainable AI (XAI) build trust in AI systems?

How can explainable AI (XAI) build trust in AI systems?

• A. By providing inconsistent results to users
• B. By enhancing the complexity of model outcomes
• C. By making the decision-making process transparent
• D. By limiting user access to information

Answer: (C)

Explainable AI (XAI) builds trust in AI systems by making the decision-making process transparent. When AI systems are able to clearly articulate how they arrive at certain decisions or predictions, users can comprehend the rationale behind the outcomes. This transparency allows users to see the factors and data that contribute to the AI's conclusions, which can demystify the processes involved and alleviate concerns about bias or errors. Moreover, when users understand the reasoning of an AI system, they are more likely to feel confident in its reliability and effectiveness. This sense of clarity also enables stakeholders to provide informed feedback, improve system designs, and foster a collaborative relationship between humans and AI. Conversely, the other options would not instill trust. Inconsistent results, increased complexity, or limited access to information would lead to confusion and skepticism regarding the reliability and integrity of the AI's decision-making processes.

Explainable AI (XAI) builds trust in AI systems by making the decision-making process transparent. When AI systems are able to clearly articulate how they arrive at certain decisions or predictions, users can comprehend the rationale behind the outcomes. This transparency allows users to see the factors and data that contribute to the AI's conclusions, which can demystify the processes involved and alleviate concerns about bias or errors.

Moreover, when users understand the reasoning of an AI system, they are more likely to feel confident in its reliability and effectiveness. This sense of clarity also enables stakeholders to provide informed feedback, improve system designs, and foster a collaborative relationship between humans and AI.

Conversely, the other options would not instill trust. Inconsistent results, increased complexity, or limited access to information would lead to confusion and skepticism regarding the reliability and integrity of the AI's decision-making processes.