Module 1
AI Literacy & Responsible AI
Understanding the difference between traditional software and modern AI — and committing to responsible use.
Overview
AI differs from traditional software in that traditional software follows fixed, pre-programmed rules, while AI systems learn patterns from data and improve over time. In a business context, this means AI can analyze far more variables simultaneously than a human analyst — such as comparing millions of financial transactions to detect fraud in real time. A personally relevant example is JP Morgan's use of AI to manage massive amounts of financial and customer data, enabling faster and better decisions while limiting fraud risk.
Responsible AI Pledge
Verify Before Use
I will always verify AI-generated information against reliable sources before using it in decisions.
Transparency
I will be transparent about when and how I use AI tools in my academic and professional work.
Fairness & Equity
I will advocate for fair and unbiased AI systems, especially in financial contexts where algorithmic bias can harm people.
Reflection
When could AI cause unintended harm?
One situation where AI could cause serious unintended harm in a business setting is in automated hiring systems. If a company trains an AI model to screen resumes using historical hiring data, and that data reflects decades of bias — for example, consistently promoting candidates from certain schools or backgrounds — the model will quietly learn to replicate that bias at scale. The issue is not that the AI is making a malicious decision. It just does not know any better than what it has been shown. To apply responsible AI principles, I would first audit the training data to identify historical patterns that may reflect bias rather than actual job performance. I would also require the model to be explainable, meaning it must be able to show why a candidate was rejected, not just that they were. Finally, I would keep a human reviewer in the loop for any rejection decision, so that the AI narrows the candidate pool but does not make the final call alone. Responsible AI is not about slowing down technology — it is about making sure the humans who deploy it take ownership of its outcomes.
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