Ethical AI and Bias Mitigation

With great power comes great responsibility. Knowledge of ethical AI principles, understanding biases in data and algorithms, and learning how to mitigate these biases are crucial skills to ensure the development of fair and unbiased AI systems.

With great power comes great responsibility. Knowledge of ethical AI principles, understanding biases in data and algorithms, and learning how to mitigate these biases are crucial skills to ensure the development of fair and unbiased AI systems.

Empowered by Artificial Intelligence and the women in tech community.
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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

Biases in AI are insane—and the worst part is, they often go unnoticed until someone is excluded or harmed. I remember working on a group project during my undergrad where we were building a system to control computers with hand gestures. It was exciting—until it wasn’t. My hand literally wouldn’t get recognized by the model. It just… didn’t work for me. At first I was confused. Then it hit me: the dataset barely had any examples of darker-skinned hands. That was my first real experience of algorithmic bias. And it was surreal. I couldn’t even fathom it back then. But this isn’t just about minor inconveniences. The long-term consequences of biased AI can be serious—even dangerous. Think about: Facial recognition tech misidentifying people of color at disproportionately high rates (which has led to wrongful arrests). Healthcare algorithms that underestimate the severity of illness in Black patients. Hiring tools trained on biased data that systematically filter out women and marginalized groups. These aren’t just technical bugs. They’re reflections of the biases we feed into our systems—often unconsciously. That’s why understanding ethical AI principles isn't optional. It’s essential. I’m still learning this field. I don’t have all the answers. But I know that building fair, inclusive systems starts with asking better questions about who’s represented, who’s missing, and who might be harmed. It means pushing for transparency, advocating for diverse datasets, and being aware that fairness isn't a one-time checkbox—it’s a constant responsibility

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