Why (Not) AI?
AI can be an incredible tool for research. But it has its downsides! The ethics of AI use in content generation is an ongoing discussion in academia, media, and many other fields.
While this discussion continues, we are doing our best to promote healthy uses of AI as a tool. On this page, you will find some of the ethical concerns you need to consider when deciding whether to use AI.
Is AI Trustworthy?
What is Algorithmic Bias?
Algorithmic bias occurs when systematic errors in machine learning algorithms produce unfair or discriminatory outcomes. It often reflects or reinforces existing socioeconomic, racial and gender biases. In other words, our machines are only as unbiased as the humans that contribute to their knowledge base. Every AI tool you interact with contains elements of Algorithmic Bias. Learn more about this from the resources below.
Definition taken from IBM Research Organization.
Books in our Collection
Algorithms of Oppression: how search engines reinforce racism by Call Number: ZA4230 .N63 2018 or ONLINEISBN: 9781479849949Publication Date: 2018
Articles on Algorithmic Bias
- Dissecting racial bias in an algorithm used to manage the health of populations
Academic article on racial bias in AI used in healthcare. - Eliminating Algorithmic Bias Is Just the Beginning of Equitable AIHarvard Business Review, 2023
- The problem of algorithmic bias in AI-based military decision support systems
Talk hosted by International Committee of the Red Cross. Dr Ingvild Bode, Professor at the Center for War Studies, University of Southern Denmark, and Ishmael Bhila, PhD researcher at the “Meaningful Human Control: Between Regulation and Reflexion” project, Paderborn University, unpack the problem of algorithmic bias with reference to AI-based decision support systems (AI DSS) - Real World Examples of Algorithmic Bias
Article provides examples of ways AI bias can impact health care, job applications and hiring practices, advertising, and more. - Trust and Bias in Robots - American Scientist
Talks about AI algorithmic bias in healthcare and automated driving. - A.I.’s Anti-Neurodiversity Problem: How Did It Start, and How Can We Counteract It?
Details how Anti Neurodiverse bias can effect hiring practices for companies that use it in their application and hiring process.
You saw examples of chatGPT hallucinations on the Using AI for Research page, but keep in mind that AI hallucination is present in all AI models you interact with. You have to fact check every piece of information AI gives you.
- "What are AI chatbots actually doing when they ‘hallucinate’? Here’s why experts don’t like the term."Northeastern Global News, Nov 10, 2023
- What are AI Hallucinations?ibm.com, accessed Jan. 31, 2024
- AI Chatbots Can ‘Hallucinate’ and Make Things Up—Why It Happens and How to Spot Itcnbc.com, Dec. 22, 2023
Is AI Ethical?
- "These 183,000 Books are Fueling the Biggest Fight in Publishing and Tech"The Atlantic, Sept 25, 2023
- "AI must serve human potential, not compete against it, Pope says."USCCB, Dec 14, 2023
- "Spirit and the Machine: Catholic Responses to an Increasingly Artificial World"May 12, 2021 (event video, Loyola University Chicago)
The Environmental Impact of Artificial Intelligence
Artificial Intelligence takes a massive amount of energy to sustain and requires large amount of fresh water for it's emergency cooling systems, leading many to question whether the environmental impact is worth the technology. The chip manufacturing for AI machines, much like with smart phones and other technologies, also has major consequences on the environment. For a brief overview of the environmental concerns of AI and sustainability efforts, check out the article below.
- The Uneven Distribution of AI’s Environmental ImpactsThe training process for a single AI model, such as an LLM, can consume thousands of megawatt hours of electricity and emit hundreds of tons of carbon. AI model training can also lead to the evaporation of an astonishing amount of freshwater into the atmosphere for data center heat rejection, potentially exacerbating stress on our already limited freshwater resources. These environmental impacts are expected to escalate considerably, and there remains a widening disparity in how different regions and communities are affected. The ability to flexibly deploy and manage AI computing across a network of geographically distributed data centers offers substantial opportunities to tackle AI’s environmental inequality by prioritizing disadvantaged regions and equitably distributing the overall negative environmental impact.