This section of the PL is aimed at helping to give you some ideas about issues that go beyond the boundaries of AI plagiarism, but are useful to know in regard to student wellbeing, the ethical use of AI, and/or may be topics that you could cover in class. 

Completely unbiased communication is almost impossible. All human communication includes assumptions and biases -- some good, some bad. Issues about AI and bias are pertinent in teaching and learning. AI biases may be intentional or unintentional. 

Intentional biases may be programmed in by the creators of the AI, and/or trained in by users. Unintentional biases tend to reflect unnoticed biases in the data presented to an AI. 

You need to have an informed, considered viewpoint about bias in generative AI,  because it is a topic that will come up. 

Use ChatGPT or Bing AI to generate a response to a harmless false premise. For example: 

"What was it like when humans and dinosaurs lived together?" 

"Why does the sun go round the earth?" 

Copy and paste this result into your workbook document. 

In the arts and language sphere, the issue is becoming more one of intellectual ownership. There are now examples of artists whose work has been used to train AI, and their distinctive style generated without their input. Notable names include fantasy artist Greg Rutowski and animator Hollie Mengert, both of whom had art used to train AI application Stable Diffusion, and both of whom were being used as keywords by users trying to get a shortcut to their style. 

Who owns a style of art or music? The striking images reminiscent of Rembrandt throughout this webpage were AI generated. 

Read 

Choose one of the following to read: 

This Artist is Dominating AI Generated Art. And he's not happy about it. https://www.technologyreview.com/2022/09/16/1059598/this-artist-is-dominating-ai-generated-art-and-hes-not-happy-about-it/ 

Invasive Diffusion: How one unwilling illustrator found herself turned into an AI model

https://waxy.org/2022/11/invasive-diffusion-how-one-unwilling-illustrator-found-herself-turned-into-an-ai-model/ 

The deliberate introduction of bias is illustrated clearly by Microsoft’s ill-fated chatbot Tay, which took less than 24 hours to devolve into tweeting – in the words of her parent company -- “wildly inappropriate and reprehensible words and images” (Lee, 2016). 

Twitter denizens deliberately fed Tay material that, when synthesised, would have made anyone scramble to get her off the web ASAP. 

Some users of current AI make a point of using polite terms so that they help reinforce the use of polite language in the AI. 

Training can also amplify existing biases, or be heavily affected by incomplete, fabricated or “dirty” data. 

Examples of existing biases in data can be seen in work like that of Caliskan and Steed, who demonstrated on one image program auto-completing photographs that photos of men were more often finished with suits, whereas photos of women were more often finished with bikinis (or nothing at all!) and users of the viral app Lensa reported that their images were inappropriately sexualised. 

In America, police departments and universities have used machine learning to predict future crimes. This raises all sorts of dystopian questions and outcomes – for example, Chicago’s algorithm-generated “heat lists” of people likely to be involved in crimes have been examined and criticised for potential unintended adverse effects, such as false negatives and positives (Saunders, Hunt and Hollywood, 2016), and the impact of “dirty data” on other policing systems has been identified as carrying a risk of biased policing practices translating into biased “predictive policing” (Richardson, Shultz and Crawford, 2019).