Clients
Arcada University
Description
This project unified two strands of my teaching on AI ethics: a remote university course at Arcada (Finland) and a hands-on notebook series. Both were designed to help students understand that ethical use of AI is not optional but a prerequisite for trustworthy systems. The course began by asking why we love AI and what its limitations are, framing both the appeal of automation and the risks of bias, opacity, and misalignment. The next chapter went deeper into statistical and algorithmic biases, showing how training data and model design can reproduce or amplify discrimination. Real-world case studies included image classifiers that misidentified people of color and recidivism models that encoded racial bias. The notebook component allowed students to code their own examples in Python, such as simple recommendation engines, so they could see how seemingly neutral algorithms could have problematic outcomes. This was paired with discussions about how companies’ profit motives shape recommendation systems, often in ways misaligned with user needs. The course culminated with a discussion of the alignment problem: how to ensure that machine outputs align with human ethical norms. This tied the pre-LLM focus of the course to the current age of chatbots and large language models, making the material still highly relevant.
Impact
The project gave students a practical and conceptual grounding in AI ethics. By coding their own models and simultaneously reflecting on ethical issues, they learned to see bias as both a statistical and a societal challenge. This made them better prepared to build and evaluate AI systems responsibly.
Year
2021 to 2023
Tools
Python, Observable
Challenge
The biggest challenge was defining scope: AI ethics is vast, and narrowing it to a set of examples that were rigorous but teachable was not easy. Another challenge was balancing code and reflection: if the material became too technical, the ethical questions got lost; if it became too reflective, students missed the technical grounding. Remote teaching also posed difficulties in keeping students engaged and ensuring they saw ethics as integral, not secondary, to AI practice.
Learning
This project deepened my knowledge of AI ethics both conceptually and practically. I relearned machine learning fundamentals in Python while framing them through the lens of bias and fairness. I learned how to integrate coding tasks with reflective discussions, striking a balance between technical skill and critical thinking. Teaching remotely at Arcada also taught me new ways to engage students virtually on a topic that can feel abstract.
Topics
AI ethics, human vs. machine bias, accountability, machine learning, recommendation engines, alignment problem
