AI Summer School - AI Ethics

Your weekly guide to getting the most out of generative AI tools

Author

Innovation Profs
July 25, 2025

Welcome to Gen AI Summer School

We’re spending the summer teaching you the essentials you need to succeed in an AI-forward world.

Here’s the plan:

AI ethics

In our presentations to a wide range of audiences, the most frequent category of questions we encounter involve the ethics of generative AI. These questions often come in two flavors: The first is, “What are some of the ethical concerns raised by generative AI?” and the second is, “How can I use generative AI responsibly?”

In our view, these questions can be seen as two sides of the same coin, as many of the ethical concerns raised by generative AI determine what counts as responsible use of generative AI. In other words, to be a responsible user of generative AI, you need to know what ethical issues might arise in using generative AI and how to respond to those issues.

Introducing the ethical cast of characters

Here’s how we explain this to our students. In our course on generative AI last spring, for each of the categories of AI generation tools we covered in class — text, image, audio, video — we discussed ethical and legal issues raised by these technologies, framed by what we refer to as our “ethical cast of characters,” depicted below (as a throwback to Porter’s childhood, these are conceptualized as a sequence of bosses you have to defeat in a retro video game):

Let’s discuss each of these characters in turn.

Hallucination ghost

Hallucination

First up is Hallucination, representing the well-known fact that large language models can unintentionally yield incorrect outputs. How can we ensure that we receive accurate outputs from LLMs?

Bias

Next, we have Bias, which represents the fact that generative AI tools can propagate any biases that are explicitly or implicitly contained in the data used to train the models underlying these tools. How can we ensure that bias is being mitigated in our use of generative AI tools?

Intellectual Property

Next up is Intellectual Property, which represents uncertainty about the status of training generative AI tools on copyrighted material as well as the status of the ownership of the outputs of generative AI tools.

Security and Privacy

Our next character represents the dual nature of Security and Privacy. This is a common concern we are regularly asked about. What happens to the data we enter into LLMs? Is there any way for LLMs to handle proprietary information? How do we protect our customer’s privacy if we use LLMs to process their data?

Environmental Impact

Next up is Environmental Impact, which corresponds to the fact that generative AI tools require a considerable amount of resources to train and deploy (e.g., the training of GPT-4 at the Microsoft data centers in West Des Moines in July 2022 require 11.5 million gallons of water for cooling the servers). What are the environmental costs associated with building and using generative AI systems, and how can they be reduced?

Workforce Displacement

Workforce Displacement refers to the challenge raised as the automation of certain tasks and processes via generative AI reshapes the workforce. What jobs will be rendered obsolete, and which will be significantly altered by generative AI? How do we get ahead of these changes?

Misinformation

Our next villain is Misinformation, representing the fact that generative AI tools can be used for nefarious purposes, for instance, to intentionally produce seemingly authentic but misleading text, images, audio, and video. What protections can we put into place to prevent this abuse of AI tools?

Finally, we have Harmful Content: as generative AI systems are trained on content from the internet, harmful content is latent within these systems. How can we build in safeguards into these systems to prevent the production of harmful content?

How to proceed?

Given these ethical challenges, what steps do we need to take to be responsible users of generative AI?

First, it’s worth noting that with a number of these challenges, we can draw upon our own experience and expertise to filter out undesirable outputs of generative AI tools by carefully scrutinizing what gets generated. In this way, we can help minimize Hallucination, Bias, and Harmful Content.

Other challenges, however, have to be addressed either at an organizational level or even at the level of legal policy: organizations need to decide how to ensure security and privacy of user data, questions of intellectual property are being worked out in the courts, and questions of misinformation, environmental impact, and workforce displacement require a broader policy framework to be addressed.

Of course, there’s much more one can say about each of these issues, but what all of them have in common is that we need to know how to navigate them individually and collectively if we want to be responsible users of generative AI.

Ultimately, being a responsible user of generative AI means recognizing that these ethical issues don’t lie on the periphery of our generative AI use. Rather, they’re central to the practice. Whether you’re a student, educator, policymaker, or business leader, understanding this “ethical cast of characters” gives you a vocabulary for asking better questions, making informed decisions, and shaping practices that align with your values. Responsibility doesn’t mean having all the answers; it means staying engaged, curious, and committed to using these powerful tools in ways that are thoughtful, transparent, and accountable.

Free AI update Aug. 25

Join us as we kick off the new school year with a free virtual overview of recent developments in generative artificial intelligence. Whether you are new to generative AI or a seasoned veteran, this event is for you.

This virtual event is free and open to the public, so sharpen your pencils and join the fun.

When: Monday, Aug. 25, 12-12:45 p.m. Central time
Where: Virtual event on Zoom
Sign up here.