Trusted Ai Trustworthy ai our trust in technology relies on understanding how it works. it’s important to understand why ai makes the decisions it does. we’re developing tools to make ai more explainable, fair, robust, private, and transparent. About this is a two and a half hour workshop over two days on the theme of trustworthy ai. the first day is a lecture and demo on the trust 360 toolkits and their enhanced editions for making your machine learning models more fair, robust, explainable, and transparent. the second day starts with a demo of a new way to discover trust issues: multidimensional subset scanning and concludes with a.

Trusted Ai Addressing the need for trusted ai in a range of practical industrial applications from recruitment, to fintech and advertising. The adversarial robustness toolbox (art) is an open source project, started by ibm, for machine learning security and has recently been donated to the linux foundation for ai (lfai) by ibm as part of the trustworthy ai tools. art focuses on the threats of evasion (change the model behavior with input modifications), poisoning (control a model with training data modifications), extraction. Ai factsheets from ibm research ai is designed to foster increased levels of trust in ai by increasing transparency and enabling governance. Abstract this paper proposes a cloud based framework and platform for end to end development and lifecycle management of artificial intelligence (ai) applications. we build on our previous work on platform level support for cloud managed deep learning services, and show how the principles of software lifecycle management can be leveraged and extended to enable automation, trust, reliability.

Trusted Ai Robotics Trusted Ai Ai factsheets from ibm research ai is designed to foster increased levels of trust in ai by increasing transparency and enabling governance. Abstract this paper proposes a cloud based framework and platform for end to end development and lifecycle management of artificial intelligence (ai) applications. we build on our previous work on platform level support for cloud managed deep learning services, and show how the principles of software lifecycle management can be leveraged and extended to enable automation, trust, reliability. Abstract we invite papers that describe innovative use of ai technology or techniques in election processes. the workshop is intended to provide a forum for discussing new approaches and challenges in building ai that people trust and use for critical applications that power society – conducting elections, and for exchanging ideas about how to move the area forward. Trusted ai literature to date has focused on the trust needs of users who knowingly interact with discrete ais. conspicuously absent from the literature is a rigorous treatment of public trust in ai. we argue that public distrust of ai originates from the underdevelopment of a regulatory ecosystem that would guarantee the trustworthiness of the ais that pervade society. drawing from. Trusted ai allows citizens to have greater trust in public organizations and their decision making processes, while it also enables public authorities and policy makers to be more transparent and accountable, providing citizens with greater visibility into how policies are developed. Pin yu chen is a principal research staff member of the trusted ai group, ibm thomas j. watson research center. he is also the chief scientist of rpi ibm ai research collaboration program and a pi of mit ibm watson ai lab projects.