Arguments for using algorithms over human decision making, inlcuding research on level of accuracy and bias.

Algorithms are choosing who's the right fit to sell fast food or handle angry cable customers, by sizing up the human candidates' voices.

Start-ups say that they can eliminate biases and create more skilled and diverse workplaces, but data science will probably still need human supervision.

Algorithms are, in part, our opinions embedded in code. They reflect human biases and prejudices that lead to machine learning mistakes and misinterpretations.

Amazon.com Inc's AMZN.O machine-learning specialists uncovered a big problem: their new recruiting engine did not like women.

Evan Peck, Associate Professor at Bucknell University's teaching modules. His lesson plan connect ethics values to code for CS 1 students, and is centred around MIT's Moral Machine.