Why is a majority of Quora using AI to answer questions? Don’t they know it’s obvious and other AIs can confirm its AI wrote?

Basically, AI won't provide the complete or up-to-date answer. When humans are involved in answering, even with the help of AI, they essentially validate the correctness of the data provided by AI.

Additionally, humans often incorporate their own thoughts into the answer. If a person gives the same answer as provided by AI, it implies that they acknowledge the AI's answer to be more detailed and easily understandable than their own.

The above content is personally written, but I still used AI to correct my grammar mistakes and misspellings. AI is here to assist us, but we cannot rely on it entirely.

I hope you found the answer helpful.

Also read this.. for better understanding:

Human involvement in AI-generated data, particularly in the context of labeling, is a common practice that helps improve the accuracy and quality of AI models. Here are some key aspects of human involvement in labeling AI-generated data:

1. Training Data Labeling: AI models often require large amounts of labeled data for training. Human annotators are involved in the process of labeling data, where they review and assign relevant labels or tags to the input data. This labeled data serves as a reference for the AI model to learn patterns and make predictions.

2. Quality Control: Human involvement is crucial in ensuring the quality and accuracy of labeled data. Human annotators follow guidelines and standards provided by data scientists or experts to maintain consistency and minimize errors in labeling. Regular reviews, feedback, and calibration sessions are conducted to maintain data quality.

3. Ambiguity Resolution: In cases where the data is ambiguous or lacks clear labels, human annotators use their judgment and domain expertise to assign appropriate labels or seek clarification from domain experts. This iterative feedback loop helps refine the labeled data and improve the performance of AI models.

4. Bias Mitigation: Human involvement in data labeling also helps address bias in AI models. Annotators are trained to recognize potential biases and follow guidelines to mitigate them during the labeling process. This includes considering diverse perspectives, avoiding stereotypes, and ensuring fairness and inclusivity.

5. Iterative Refinement: Human involvement doesn't end with the initial labeling process. As AI models are trained and deployed, human feedback and continuous evaluation play a crucial role in iteratively refining the model's performance. Human reviewers may review AI-generated outputs, provide feedback, and make adjustments to improve the accuracy and relevance of the AI-generated data.

Human involvement in data labeling ensures that AI models learn from accurate, reliable, and representative data. While AI plays a significant role in automating and scaling processes, human expertise is essential for complex decision-making, handling ambiguity, and maintaining ethical considerations. The collaboration between humans and AI technology helps create more robust and reliable AI systems.