Ethics in AI Made Easy: Value-Sensitive Design for Data Scientists

Author(s): Jelle Van Bost

Originally published on Towards AI.

Practical Strategies for Building Trustworthy Algorithms
Image generated by the author on leonardo.ai — Commercial license

Remember those jeans you swore fit two years ago? The ones you confidently squeezed in, only to experience a very public wardrobe malfunction? Data science can have these moments too. Without careful consideration of values and implications, our algorithms can become the digital equivalent of these jeans — uncomfortable, embarrassing, and with huge gaps exposed.

But here is the thing: We live in a fast-evolving world, where new AI models are released weekly. Striking the balance between innovation and safety has never been more important. If we wait too long to care about these things, the next digital wardrobe malfunction might have devastating consequences.

This problem isn’t just about errors in the code, it’s about real consequences. Imagine a medical algorithm trained on a dataset that doesn’t reflect your racial group, leading to a potential misdiagnosis. Or a hiring algorithm that demonstrates gender bias. Value Sensitive Design (VSD) is a framework that can help us create strong, well-fitting datasets and algorithms. The ones we can trust to work for everyone.

In this article, I will explore what VSD is and how it can impact your work as a data scientist. I will also give… Read the full blog for free on Medium.

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Published via Towards AI