Answer Posted / Pramod Pratap Singh
Several principles guide ethical AI development, including transparency (making the workings of AI systems clear to users), accountability (ensuring those responsible for an AI system can be held accountable for its actions), fairness (avoiding biases in AI algorithms and their effects on individuals or groups), privacy (protecting personal data), and security (preventing unauthorized access or manipulation of AI systems).
| Is This Answer Correct ? | 0 Yes | 0 No |
Post New Answer View All Answers
What techniques can improve the explainability of AI models?
What is in-processing bias mitigation, and how does it work?
Can AI systems ever be completely free of bias? Why or why not?
Explain the risks of adversarial attacks on AI models.
Explain demographic parity and its importance in AI fairness.
Explain the difference between data bias and algorithmic bias.
What measures can ensure the robustness of AI systems?
What challenges do organizations face in implementing fairness in AI models?
How can preprocessing techniques reduce bias in datasets?
How do societal biases get reflected in AI models?
How do biases in AI models amplify existing inequalities?
Provide examples of industries where fairness in AI is particularly critical.
What are the societal benefits of explainable AI?
How do you measure fairness in an AI model?
What ethical concerns arise when AI models are treated as "black boxes"?