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.

This article originally appeared on ZDNet. IBM said it will launch cloud software designed to manage artificial intelligence deployments, detect bias in models and mitigate its impact and monitor decision across multiple frameworks. The move by IBM highlights how AI management is becoming more of an issue as companies deploy machine learning and various models to make decisions. Executives are likely to have trouble understanding models and the data science under the hood. IBM said its technology will monitor AI so enterprises comply with regulations.

IBM on Wednesday introduced technology that automatically detects bias and explains how AI formulates decisions as they are made. The software runs on the IBM Cloud. IBM Research's bias detection tool, AI Fairness 360 toolkit, will become open-source. The hope is that academics, researchers and data scientists will integrate bias detection into their models. The company puts its bias detection tools on Github, a software development platform that Microsoft acquired in June 2018 for $7.5 billion.