Part 5/12:
Accuracy & Reliability: AI systems must provide correct and dependable responses. For example, in banking, incorrect loan eligibility data could lead to financial loss, while in healthcare, inaccuracies could have severe health implications.
Accountability & Transparency: Stakeholders must understand why AI systems produce specific outputs. Given the opacity of many LLMs trained on trillions of data points, ensuring transparency and reasoned outputs is vital.
Fairness & Non-discrimination: AI should avoid biases that favor or disadvantage specific groups. Notable concerns include the risk of models reflecting societal biases—racism, sexism, or inequality—that can have real-world consequences.