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Revisiting AI Project Cycle & Ethical Frameworks of AI Video Lecture | Artificial Intelligence for Class 10
FAQs on Revisiting AI Project Cycle & Ethical Frameworks of AI Video Lecture - Artificial Intelligence for Class 10
| 1. What is the AI project cycle and what are its main stages? |  |
Ans. The AI project cycle refers to the systematic process involved in developing AI solutions. Its main stages typically include problem identification, data collection, data preparation, model selection, model training, evaluation, deployment, and monitoring. Each stage is crucial for ensuring that the final AI product is effective and meets the intended goals.
| 2. Why is ethical consideration important in AI projects? | |
Ans. Ethical considerations in AI projects are vital because they help prevent biases, ensure fairness, promote transparency, and protect user privacy. Ethical frameworks guide developers in making responsible decisions that respect human rights and consider the societal impacts of AI technologies, ultimately fostering trust and acceptance among users.
| 3. How do we ensure fairness and avoid bias in AI systems? | |
Ans. Ensuring fairness and avoiding bias in AI systems involves several strategies such as diversifying training data, implementing bias detection tools, and conducting regular audits of AI algorithms. It is important to carefully analyze the data sources and the decision-making processes of AI models to identify and correct any biases that may lead to unfair outcomes.
| 4. What are some common ethical frameworks used in AI development? | |
Ans. Common ethical frameworks in AI development include the Principles of Beneficence, Non-maleficence, Autonomy, and Justice. These frameworks help guide AI practitioners in making decisions that promote positive outcomes, avoid harm, respect individual rights, and ensure equitable treatment across different user groups.
| 5. How can stakeholders be involved in the AI project cycle? | |
Ans. Stakeholders can be involved in the AI project cycle through collaboration and engagement at various stages. This includes gathering input during the problem identification phase, seeking feedback on model outcomes, and ensuring user perspectives are considered during deployment. Involving stakeholders helps to align the AI system with user needs and ethical standards, fostering better acceptance and usability.
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Oct 02, 2025
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