Machine learning models often make predictions that bias against certain subgroups of input data. When undetected, machine learning biases can constitute significant financial and ethical implications. Semi-automated tools that involve humans in the loop could facilitate bias detection. Yet, little is known about the considerations involved in their design. In this paper, we report on an interview study with 11 machine learning practitioners for investigating the needs surrounding semi-automated bias detection tools. Based on the findings, we highlight four considerations in designing to guide system designers who aim to create future tools for bias detection.

Although I work at a tech company, I have no technological background whatsoever. I graduated in linguistics from the Department of Humanities, meaning I had to sit through compulsory philosophy and ethics classes. It got me thinking: how am I going to use all this? There is no better time to talk about Machine Learning (ML) ethics than now. ML is spreading, and however useful, it must also be treated with caution.

As a company that specializes in training AI systems, Alegion knows that models in fact do precisely what they are taught to do. AI models comprise algorithms and data, and they are only as good as their underlying mathematics and the data they are trained on. When things go wrong with AI it's for one of two reasons: either the model of the world at the heart of the AI is flawed, or the algorithm driving the model has been insufficiently or incorrectly trained. AI is far from infallible. Whether it's autonomous vehicle accidents or facial recognition mishaps, it's tempting for the public to think that AI can't be trusted.

AI systems are becoming more and more the norm as machine and deep learning gain ground -- especially within the data center and colocation markets. That said, artificial intelligence systems are only as good as their underlying mathematics and the data they are trained on. That's according to a new white paper from Alegion that explores the bias behind machine learning. AI systems and models are made up of algorithms and data, and the professionals who craft the models, etc., are largely in charge of underlying mathematics and data. According to the new Alegion white paper, when things go wrong with AI it's for one of two reasons: The Alegion report contends there are four different types of machine learning or AI systems bias.

Alegion provides ground truth training data for machine learning initiatives. We offer a white glove level of service at massive scale, combining a data and task management software platform with a global pool of trained data specialists.

As a company that specializes in training AI systems, we know only too well that AI systems do precisely what they are taught to do. Models are only as good as their mathematical construction and the data they are trained on. Algorithms that are biased will end up doing things that reflect that bias. AI exists as a combination of algorithms and data. There can be bias in both of these elements.

This guest post from Alegion explores the reality of machine learning bias and how to mitigate its impact on AI systems. It exists as a combination of algorithms and data; bias can occur in both of these elements. When we produce AI training data, we know to look for biases that can influence machine learning (ML). In our experience, there are four distinct types of bias that data scientists and AI developers should avoid vigilantly. The key to successfully mitigating bias is to first understand how and why it occurs.