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Artificial Intelligence

Artificial Intelligence (AI) impacts all fields of study and is not subject specific. This guide is here to support research and learning involving Artificial Intelligence.

Environmental Impacts

GenAI has environmental consequences, centering around the energy and spaces required to power the data-centers (and increasing demand for them) that GenAI run on. These environmental issues are exacerbated by the practices of large technological companies and conglomerates to not disclose or record carbon emissions of their industries (a current and historical issue). Research on the environmental consequences of GenAI continues to be done. To start learning more about the environmental impacts of AI, please see the video below:

Ghost Work/Invisible Labor

When it comes to AI and algorithmic systems, we tend to think of computers magically doing all the work for us. But behind the scenes, there’s a lot of human labor involved in the training and maintenance of these systems. 

Most machine-language models learn how to make decisions from datasets that have been labelled, classified, verified, and filtered by real people. These quality control tasks, which are invisible to end users, are referred to as ghost work (Gray & Suri, 2019).

Privacy

When using an AI application, you should avoid entering any personal, sensitive, or confidential information. Why?

  • Any data you input into a generative AI tool may be incorporated into its training dataset and used to generate future content.
  • There are concerns about the collection, use, and sharing of personal data by AI tech companies, such as ChatGPT.
  • AI systems can be susceptible to hacking, thus posing a risk of information theft.