Imagine opening up about your anxiety to a chatbot at 2 AM, something you usually don’t do with a human. It “listens”, responds, maybe even offers advice. But how does it know what to say? Who trained it, and what values guide its response? Is it really helping or nudging you towards emotional dependence?

These questions are not theoretical. They are at the heart of alignAI’s Mental Health use case.

It’s not so long ago that we thought of AI as belonging solely to the realm of computer scientists, mathematicians and technical experts. But it’s time to move away from this idea.

Who might need to be involved to create an AI system that helps us detect wildfire threats in different forests? Or creating an LLM which aids in the detection of cancerous cells? Or an AI that facilitates smart energy use in a home?

Have you ever wished ChatGPT a good morning? Asked it to “please” do something for you? Or thanked it when it did? Now ask yourself why. Maybe you wanted to be polite? Maybe it was a reflex? Or maybe you expected it to provide better results if you were friendly?

As AI systems (Large Language Models (LLMs) in particular) become more fluent, responsive, and seemingly empathetic, many users report a subtle shift in how they interact with them. ChatGPT remembers things from your past conversations, meets your energy if you are polite, and remembers your name. At times, it feels less like using a tool and more like speaking to a person.

Imagine this: You’re in a courtroom and have been convicted of a crime. Before announcing the sentence, the judge consults a risk assessment tool – an AI system designed to promote consistency and reduce human bias in sentencing. The algorithm generates a score meant to help the judge weigh rehabilitation potential versus public risk. It’s intended to be impartial. Would you trust the tool to fairly assess your case? Would the judge?

In our last blog post, we unpacked what fairness in AI means, why it matters, and why technical fixes alone can not be enough to solve the deeper social and structural challenges at play. Still, just because fairness isn’t the whole answer doesn’t mean it isn’t a necessary part of the solution. Improving how AI systems treat individuals and groups, especially in high-stakes areas, is a critical step toward reducing harm and moving toward more equitable outcomes. That’s why in this second post, we shift our focus to the practical: How is fairness currently being pursued in AI systems today?

When people think about AI, they often imagine objectivity. They imagine algorithms that soberly follow data and numbers, unaffected by personal opinions, emotions, or prejudice. But here’s the problem: AI systems don’t fall out of the sky. Humans develop them, they’re trained on human-generated data, shaped by human choices, and deployed in human contexts – all of which are far from neutral.