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Answer :
Generative AI faces challenges such as data quality, algorithmic bias, security, privacy, ethical concerns, and misinformation.
Generative AI, like ChatGPT and DALL-E, faces several significant challenges when it comes to data.
- Data Quality: For generative AI to function effectively, it needs high-quality data. Poor quality data can lead to inaccurate outputs or the generation of misleading information.
- Algorithmic Bias: Data used to train AI models can contain biases that the AI then perpetuates. This is particularly problematic for applications that require fairness and neutrality.
- Security and Privacy: Ensuring the data used and generated by AI systems is secure and respects user privacy is a critical concern, highlighted by researchers like Hasal et al. (2021).
- Ethical Concerns: There is a need for ethical guidelines in the use of AI, especially in education and other sensitive areas. The rise of generative AI has made this a pressing issue according to scholars like Selwyn & Gašević (2020).
- Misinformation: Generative AI can create content that looks accurate but is actually false, complicating the task of distinguishing reliable sources amid AI-generated misinformation.
These challenges underscore the need for high standards in data quality and ethical considerations to ensure that the benefits of generative AI can be fully realized.
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* security
