machine learning

Why is fairness to people with disabilities a different problem from fairness concerning other protected attributes like race and gender?

Disability status is much more diverse and complex in the ways that it affects people. A lot of systems will model race or gender as a simple variable with a small number of possible values. But when it comes to disability, there are so many different forms and different levels of severity. Some of them are permanent, some are temporary. Any one of us might join or leave this category at any time in our lives. It’s a dynamic thing.

I think the more general challenge for the AI community is how to handle outliers, because machine-learning systems—they learn norms, right? They optimize for norms and don’t treat outliers in any special way. But oftentimes people with disabilities don’t fit the norm. The way that machine learning judges people by who it thinks they’re similar to—even when it may never have seen anybody similar to you—is a fundamental limitation in terms of fair treatment for people with disabilities.

Source: Can you make an AI that isn’t ableist?

it’s not just the technology we should pay attention to; it’s those trying to disrupt the social order.

Source: Machine Teaching, Machine Learning, and the History of the Future of Public Education

I think there’s a lot to say about machine learning and the push for “personalization” in education. And the historian in me cannot help but add that folks have trying to “personalize” education using machines for about a century now. The folks building these machines have, for a very long time, believed that collecting the student data generated while using the machines will help them improve their “programmed instruction” – this decades before Mark Zuckerberg was born.

I think we can talk about the labor issues – how this continues to shift expertise and decision making in the classroom, for starters, but also how students’ data and students’ work is being utilized for commercial purposes. I think we can talk about privacy and security issues – how sloppily we know that these companies, and unfortunately our schools as well, handle student and teacher information.

Source: Machine Teaching, Machine Learning, and the History of the Future of Public Education

Anytime you hear someone say “personalization” or “AI” or “algorithmic,” I urge you to replace that phrase with “prediction.”

Source: Machine Teaching, Machine Learning, and the History of the Future of Public Education