The application of AI algorithms in domains such as criminal justice, credit scoring, and hiring holds unlimited promise. At the same time, it raises legitimate concerns about algorithmic fairness. There’s a growing demand for fairness, accountability, and transparency from ML systems. And we need to remember that training data isn’t the only source of possible bias and adversarial contamination. It can also be introduced through inappropriate data handling, inappropriate model selection, or incorrect algorithm design. We need a pipeline that’s open, transparent, secure, and fair, and that fully integrates into the AI lifecycle. Such a pipeline requires a robust set of bias and adversarial checkers, debiasing and defense algorithms, and explanations.
Animesh Singh, Svetlana Levitan, and Tommy Li demonstrate how to build an ML pipeline that’s open, secure, and fair and that fully integrates into the AI lifecycle, using open source tools like AIF360, ART, Fabric for Deep Learning (FfDL), Model Asset Exchange (MAX), and Seldon Core.
Animesh Singh is a senior technical staff member (STSM) and program director for the IBM Watson and Cloud Platform, where he leads machine learning and deep learning initiatives on IBM Cloud and works with communities and customers to design and implement deep learning, machine learning, and cloud computing frameworks. He has a proven track record of driving design and implementation of private and public cloud solutions from concept to production. Animesh has worked on cutting-edge projects for IBM enterprise customers in the telco, banking, and healthcare industries, particularly focusing on cloud and virtualization technologies, and led the design and development first IBM public cloud offering.
Svetlana Levitan is a developer advocate with the IBM Center for Open Source Data and Artificial Technologies (CODAIT). Previously, she was a software engineer implementing SPSS statistical and machine learning algorithms. She earned her PhD in applied math and MS in CS from University of Maryland. She loves learning more on AI and sharing her knowledge.
Tommy Li is a software developer at IBM focusing on cloud, container, and infrastructure technology. He’s worked on various developer journeys that provide use cases on cloud-computing solutions, such as Kubernetes, microservices, and hybrid cloud deployments. He’s passionate about machine learning and big data.
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