IBM has introduced technology designed to give businesses new transparency into AI and enable customers to have the ability to fully harness the technology’s power.
The software service on the IBM Cloud automatically detects bias and explains how AI makes decisions as they are being made. The software assists organizations as they manage AI systems from a wide variety of industry players. IBM Services will also work with businesses to help them harness the new software service.
“IBM (News - Alert) led the industry in establishing Trust and Transparency principles for the development of new AI technologies,” said Beth Smith, GM, Watson AI, IBM. “It's time to translate principles into practice. We are giving new transparency and control to the businesses who use AI and face the most potential risk from any flawed decision making.”
These developments came after new research by IBM's Institute for Business Value revealed that while 82 percent of enterprises are considering AI deployments, 60 percent fear liability issues. Also 63 percent lack the in-house talent to confidently manage the technology.
IBM's new trust and transparency capabilities on the IBM Cloud work with models built from a wide variety of machine learning frameworks and AI-build environments such as Watson, Tensorflow, SparkML, AWS SageMaker, and AzureML. This allows organizations to take advantage of these new controls for most of the popular AI frameworks used by enterprises.
IBM Research has made available the AI Fairness 360 toolkit to the open source community. This is a library of novel algorithms, code, and tutorials that will give academics, researchers, and data scientists the necessary tools and knowledge to integrate bias detection as they build and deploy machine-learning models. While other open-source resources have focused solely on checking for bias in training data, this toolkit will help check for and mitigate bias in AI models. It allows the global open source community to work together to advance the science and make it easier to address bias in AI.