Artificial intelligence (AI) is technology capable of performing advanced tasks and calculations by generating text, images, or other media. AI can learns the patterns and structure of input training data and then generates new data that has similar characteristics (Wikipedia). Examples of popular "large language model" artificial intelligence programs that answer questions by composing content gleaned and combined from millions of data points include:
- Chat GPT
- Microsoft Co-Pilot
- Google Gemini
Other ubiquitous tools such as the assistants Alexa and Siri operate with artificial intelligence, as do image creation tools such as Midjourney and Canva.
Naturally, there are many concerns about AI. Among those concerns are:
- Theft of Intellectual Property: AI may produce content that is unattributed to the creator of copyrighted material, thus robbing the creator of credit and compensation.
- Academic Dishonesty: Using AI produced material, including paraphrasing, without disclosing and without attribution are forms of cheating and plagiarism.
- Decline in Learning: Reliance on AI to write and solve problems contributes to a decline in critical thinking and prevents the acquisition of knowledge and skill that comes with repetition and engagement with material that is required in most industries and careers.
With artificial Intelligence now pervasive in daily living, it is important for you to learn how to use it ethically and responsibly. This includes knowing when and how you may be allowed to use AI for the work you do in your classes.
Academic integrity is the principle guiding all members of an academic community to work with fairness, trust, honesty, responsibility, and respect for the ideas of others. For more information, see the Academic Integrity and Plagiarism Avoidance guide.
When using AI, know that it has pitfalls including:.
- Decline in Learning: Reliance on AI to write and solve problems contributes to a decline in critical thinking and prevents the acquisition of knowledge and skill that comes with repetition and engagement with material that is required in most industries and careers. Hallucinations: An overload of input or a lack of data to mine can result in AI generating nonsensical, incorrect, or made up information. This includes fake or incomplete citations.
- Being Flat-Out Wrong: AI can re-generate whatever bad information it mines, or conflate closely related ideas that are frequently mentioned together.
- Theft of Intellectual Property: AI programs mine material taken from online sources that are not located behind a paywall such as a paid newspaper subscription or library database. This work is often restated through AI platforms without the permission or knowledge of the authors/creators and thus the output, particularly when unattributed, may constitute intellectual property theft. Authors, entertainers, information companies and politicians have filed lawsuits against the developers of artificial intelligence tools for violating copyright by using their protected in training or output without asking for permission or compensating the creators.
- Propaganda: AI output can be tainted by misinformation and propaganda, such as doctored images and "deepfake" videos.
- Lack of Transparency: Because the developers of AI do not disclose their algorithms, it is impossible for researchers to analyze output for inherent bias and accuracy of source material.
- Security: Like any other technology, artificial intelligence can be susceptible to malware, spam, phishing, and other forms of privacy breaches and data theft. Also, remember that your questions are used to further train the system, and could end up being revealed to other users in their answers, so be careful not to reveal any personal information to AI platforms.
- Data Quality and Accuracy: AI will provide data that it can find by scouring the free Internet; where one can find data sets and statistics that are incomplete, incorrect, or subject to misinterpretation, and these errors may be repeated in an AI inquiry.
- Algorithmic Bias: Biases can creep into algorithms for many reasons, including biases in the original data, the design of the algorithms, or biases in the evaluation of the output. You can read more about algorithmic bias in this IBM white paper.
- Language and Cultural Bias: AI tends to be biased toward the dominant languages and cultures that are prevalent in the data used to train the program. This can result in less accurate and less comprehensive responses when information about non-dominant or marginalized groups is needed.