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Generative Artificial Intelligence for Teaching, Research and Learning

Limitations and Bias

Understanding the limitations of AI is extremely important both scientifically and ethically. "Bias in an algorithm is defined as a systematic error in its outputs or processes." (Vicente & Matute, 2023) The critical appraisal of the bias and correctness of the results of using AI is essential. In terms of results you receive, consider of your results. Below you will find an example framework for evaluating the limitations and biases of AI tools.

Evaluating your AI tool

The following framework is meant to guide users in critically assessing the use and output of AI tools.

Reliability Does the responsible creator have a stake in the information that is produced? Do they have the proper credentials/transparency to reliably provide results?
Objective Why was the AI created and why are they sharing information about it?
Bias Are there biases in the machine language model being used or the data it is training on? Have any ethical issues been raised about this product?
Ownership Who owns this? Private company, government or academic institution?
Type Which machine language model is being used and how is it being trained? Does it rely on human intervention?

Source: Hervieux, S. & Wheatley, A. (2020). The ROBOT test [Evaluation tool]. The LibrAIry.

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