IBM Journey to AI blog
SEPTEMBER 20, 2023
AI’s value is not limited to advances in industry and consumer products alone. When implemented in a responsible way—where the technology is fully governed, privacy is protected and decision making is transparent and explainable—AI has the power to usher in a new era of government services.
Marktechpost
MARCH 12, 2024
From the beginning, the primary objective of the Croissant initiative was to promote Responsible AI (RAI). These include data life cycle management, labeling, participatory data, ML safety and fairness evaluation, explainability, compliance, and more.
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IBM Journey to AI blog
JUNE 23, 2023
Interpretation and contextualization: Financial reports need to deliver insights beyond the numbers they feature; they should provide meaningful context that aids in interpreting financial data. If poorly executed, these reports can limit our ability to explain the underlying drivers of performance.
IBM Journey to AI blog
JANUARY 10, 2024
This lack of transparency can be problematic in industries that prioritize process and decision-making explainability (like healthcare and finance). Learning and data handling: Traditional programming is rigid; it relies on structured data to execute programs and typically struggles to process unstructured data. .¹
IBM Journey to AI blog
MARCH 13, 2024
Our solutions are designed to address complexity at every level: from data to governance to scaling. IBM believes that scaling AI with governance is the path to sustainable, ethically responsible AI —boosting customer trust and corporate reputation.
IBM Journey to AI blog
JULY 27, 2023
Precisely conducted a study that found that within enterprises, data scientists spend 80% of their time cleaning, integrating and preparing data , dealing with many formats, including documents, images, and videos. Overall placing emphasis on establishing a trusted and integrated data platform for AI.
The MLOps Blog
MARCH 21, 2023
They work with other users to make sure the data reflects the business problem, the experimentation process is good enough for the business, and the results reflect what would be valuable to the business. The user stories will explain how your data scientist will go about solving a company’s use case(s) to get to a good result.
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