1.1: Beginner

Bias

Artificial Intelligence comes in many forms, but all require data. ChatGPT, for example, is a large language model that is trained on a huge dataset which includes the “common crawl“; a text-based database of over 12 years’ worth of web pages. These datasets give the models tremendous capabilities, but they are also inherently biased. Indiscriminately “scraping” the internet lets in the bad along with the good, meaning that the dataset can contain racist, sexist, ableist, and otherwise discriminatory language.

Unfortunately, AI large language models hold up a mirror to internet society, and the reflection isn’t pretty. Like other societies, the online community underrepresents marginalised groups, and overrepresents others. The prevalence of racism and bigotry on sources like Reddit and Twitter can bleed through the datasets and be reproduced in the output.

Bias can also come from the methods of training and reinforcement used when developing the AI systems. For example, police in the US have used systems for “predictive policing” which use algorithms to predict people likely to commit crimes. These algorithms disproportionately target poor, Black, and Latinx communities, reinforcing existing systemic biases.

Teaching points

• “Garbage in, garbage out” is a common phrase in computing. Unfortunately, much of the data that goes into AI models is biased, resulting in discriminatory output.
• Datasets can exclude minority groups or marginalised people.
• AI algorithms can perpetuate systemic biases, for example by targeting communities who already suffer under biased judicial systems.

Subject examples

• Humanities (Legal studies): What legal precedents exist for protecting marginalised groups from discrimination? Is a biased algorithm legal?
• English and Literature: How have certain groups been silenced or oppressed throughout history? What is the implication of this “gap” in the written record of the internet when it is used as data to train an AI?
• Mathematics: What is an algorithm? How do algorithms and probability link to policing and other societal functions?

Environmental impact

The technology industry as a whole has an enormous impact on the environment. Most devices from smartphones to laptops incorporate metals like lithium and rare earth minerals which are in short supply and costly to extract. The mining and refining of these products adds to the environmental impact of developing the technologies that AI is built from. These costs include soil erosion, water pollution, and greenhouse gas emissions.

AI computing is increasingly carried out in “the cloud”. Cloud services sound like an ethereal and temporary arrangement, but the name actually hides the physical reality of the technology. Cloud computing relies on huge data centres and infrastructure, all of which consumes energy and produces waste.

Although many of the major companies such as Google, Microsoft, and Meta have pledged to make their data centres carbon neutral, in reality this often means engaging in carbon-trading or offsetting schemes rather than actually reducing the amount of waste or environmental damage. This “greenwashing” is heavily criticised by those who would rather see an actual reduction in emissions.

Teaching points

• The infrastructure behind AI – including the devices it runs on – has a huge impact on the environment through processes like mining and industry.
• Cloud computing still requires enormous amounts of energy.
• AI companies have been accused of “greenwashing” when they commit to reducing their carbon footprint, but do not actively reduce their impact on the environment.

Subject examples

• Humanities (Geography): What is the impact of the climate crisis on different global populations? Which parts of society are responsible for the most emissions due to AI technologies?
• Science: Why does computing use so much energy and produce so much waste heat? What is the impact on the environment?
• Design and Technology: How might we design more sustainable systems for the manufacturing of AI technologies?

The cloud isn’t as fluffy as it sounds. Via Midjourney. Prompt in alt text.

Academic integrity and “truth”

Academic integrity – or using AI language models to cheat – has been by far the biggest potential issue of AI covered recently in the media. There have been widespread fears that students will use language models like ChatGPT to write essays, answer questions, and cheat on assignments. These fears seem particularly strong in secondary and tertiary education, where many assignments are provided in written form.

It is also still unclear to what extent using an AI constitutes “cheating”. It is not, strictly speaking, plagiarism as the output of the model is not copied from another source. Rather, the output is an original creation which is generated “probabilistically“. Knowing where to draw the line raises ethical questions about academic integrity and honesty. This has already led some universities to permit the technologies as long as they are credited. In fact, this article was written with the assistance of ChatGPT. All of the words you’re reading are mine (I happen to enjoy writing), but I used the AI to help organise the structure and to fill some of my knowledge gaps in different subject areas. I’ll explain the process in full in my next post.

As well as cheating, there are concerns that AI will be used to produce massive amounts of “fake news” or deliberately harmful media. This may be unintentional – one of the biggest current flaws of most language models is that they can generate very convincing lies. Or, people may use these technologies maliciously to spread political misinformation or otherwise cause harm.

Teaching points

• Academic integrity is an important part of any education system. Think about the rules and regulations that exist, and why they are in place.
• AI models produce original output that cannot be detected by plagiarism detectors (and “AI detectors” are currently hit and miss).
• AI can be used to produce “fake news” or deliberately misleading information.

Subject examples

• English: If an AI can write an essay, what is the point of writing essays? how do essays help us to build knowledge and not just demonstrate it?
• Religion: What are the ethical and moral implications of academic integrity?
• Humanities (Legal studies): Is using an AI to write an essay cheating? Australia has laws against contract cheating (getting another person to produce an essay on your behalf). Is using an AI a form of contract cheating and therefore illegal?