Artificial Intelligence is being used in almost every aspect of life. AI symbolizes growth and productivity in the minds of some, but it is raising questions as well on the fairness, privacy, and security of these systems. Many legitimate issues exist, including biased choices, labor replacement, and a lack of security. When it comes to robots, this is very frightening. Self-driving automobiles, for example, can cause injury or death if they make mistakes. Responsible AI addresses these difficulties and makes AI systems more accountable.
- Interpretability: We obtain an explanation for how a model makes predictions when we interpret it. An AI system makes predictions for a user. Even if these selections are correct, a user is likely to seek an explanation. Responsible AI can describe how we create interpretable models.
- Fairness: AI systems have the potential to make judgments that are biased towards particular groups of people. Bias in the training data is the source of this bias. The easier it is to assure fairness and rectify any bias in a model, the more interpretable it is. As a result, we need a Responsible AI framework to explain how we evaluate fairness and what to do if a model makes unjust predictions.
- Safety and Security: AI systems aren’t deterministic. When confronted with new situations, they are prone to making poor choices. The systems can even be tampered with to make unwise decisions. Therefore, we need to ensure safety and security in these systems.
- Data Governance: The data used must be of high quality. If the data used by AI has errors, the system may make wrong decisions.
