Nearly 9 out of 10 organizations worldwide have encountered ethical issues resulting from the use of AI. (Capgemini Article).
Whether it be ethical issues or failure to operate resiliently and deliver ROI, it is crucial that AI systems be governed to minimize risk and earn our trust. With the increasing availability and use of transformative technology, the stakes are higher than ever before. As AI technology matures, so too should our AI lifecycle management practices. Forethought and understanding of the dimensions of trust will not only help mitigate AI system risks, but also reflect your organization's values and goals.
Join two members from the DataRobot team, Colin Priest,
Global Lead in AI Governance, and Haniyeh Mahmoudian,
Global AI ethicist, as they drive deep into the intricacies of trust needed in the implementation of AI/ML models as well as how DataRobot offers solutions and capabilities in doing so. You will also hear from guest speaker Brandon Purcell,
Responsible and Ethical AI Lead Analyst at Forrester, about Responsible AI principles, Explainable AI, and why the demand for Ethical AI is rising.
- Top trends across industries for implementing Trusted AI Governance
- The concept of trust in an AI system - explanation of the many dimensions and how to use these to design the AI system and inform your business process
- AI failures and how to avoid them
- DataRobot's guardrails and Continuous AI
- Human governance - humans and AI best practices
- AI trust survey in collaboration with the World Economic Forum and the University of St. Gallen
- DataRobot's fairness and bias capabilities
- How ethics is more than unfair bias
