IBM Journey to AI blog
OCTOBER 16, 2023
AI governance refers to the practice of directing, managing and monitoring an organization’s AI activities. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. ” Are foundation models trustworthy? . ” Are foundation models trustworthy?
IBM Journey to AI blog
MAY 9, 2023
Success in delivering scalable enterprise AI necessitates the use of tools and processes that are specifically made for building, deploying, monitoring and retraining AI models. Consistent principles guiding the design, development, deployment and monitoring of models are critical in driving responsible, transparent and explainable AI.
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IBM Journey to AI blog
JUNE 26, 2023
Possibilities are growing that include assisting in writing articles, essays or emails; accessing summarized research; generating and brainstorming ideas; dynamic search with personalized recommendations for retail and travel; and explaining complicated topics for education and training. What is watsonx.governance?
IBM Journey to AI blog
DECEMBER 4, 2023
IBM watsonx.governance ™, a component of the watsonx™ platform that will be available on December 5 th , helps organizations monitor and govern the entire AI lifecycle. It helps accelerate responsible, transparent and explainable AI workflows.
IBM Journey to AI blog
OCTOBER 12, 2023
Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.)
IBM Journey to AI blog
FEBRUARY 8, 2023
Manual processes can lead to “black box models” that lack transparent and explainable analytic results. Explainable results are crucial when facing questions on the performance of AI algorithms and models. Your customers deserve and are holding your organization accountable to explain reasons for analytics-based decisions.
IBM Journey to AI blog
FEBRUARY 8, 2024
In addition, stakeholders from corporate boards to consumers are prioritizing trust, transparency, fairness and accountability when it comes to AI. Risk management – preset risk thresholds, and proactively detect and mitigate AI model risks. Monitor for fairness, drift, bias and new generative AI metrics.
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