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Artificial Intelligence

Artifical Intelligence and the research process

Evaluating Output

Before using generative AI, you should consider whether the task you hope to complete can be accomplished successfully by AI, and if so, what AI tool is best suited to the task. ​

  • This includes considering the potential shortfalls of a tool, such as what bias impacts the AI model, whether it was trained on incomplete, inaccurate or unrepresentative datasets, or has a limited or outdated scope of information. ​

The content created by generative AI, such as ChatGPT and DALL-E, need to be evaluated just like any other source you work with; however, there are unique issues involved with generative AI that make the outputs developed require more scrutiny.​

For example, generative AI:​

  • Can be influenced by bias, and provide biased information​

  • Are susceptible to "hallucinating," or making up information, content, or sources​

  • Generative AI can make up convincing looking sources, or attribute information falsely to an existing resource​

  • Can have inaccurate and outdated data ​

  • Can produce irrelevant results​

  • Predicts, rather than thinks​

  • Does not actually think or understand information or the relationships between information ​

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