Reliability & Objectivity of AI Inference
Responsible AI is essential for building trust in AI systems and ensuring they are used to benefit society.
AI technology plays a significant role in business transformation. The reliability and objectivity of AI inference are critical concerns, especially given AI's increasing role in various aspects of modern life. AI systems can exhibit biases, leading to discriminatory outcomes in areas like hiring, lending, law enforcement, and healthcare. These biases often stem from the training data used; if the data reflects historical prejudices or lacks diverse representation, the AI system is likely to perpetuate these biases.
Combating bias in AI systems involves several best practices:
-Using diverse and representative training data.
-Implementing mathematical processes to detect and mitigate biases.
-Developing transparent and explainable algorithms.
-Adhering to ethical standards that prioritize fairness.
-Conducting regular system audits to monitor bias continuously.
-Engaging in continuous learning and improvement to reduce bias over time.
Different types of bias can arise in various ways, especially within AI systems, leading to unfair or discriminatory outcomes. These biases often stem from the data used to train these systems. So, determining fairness and bias involves subjectivity, and AI models need to reflect the world as it is, making it a work in progress. Responsible AI is essential for building trust in AI systems and ensuring they are used to benefit society.
