Fujitsu open sources AI technology with the Linux Foundation

Fujitsu and the Linux Foundation have officially launched Fujitsu’s automated machine learning and AI fairness technologies as open source software (OSS). The two projects will offer users access to software that automatically generates code for new machine learning models, as well as a technology that addresses latent biases in training data.

The Linux Foundation approved the incubation of two new projects, “SapientML” and “Intersectional Fairness” on August 24 to encourage developers worldwide to further experiment and innovate with AI and machine learning technologies, with plans to host future activities like hackathons to engage and build a community to promote open source AI.

Fujitsu holds the largest number of AI-related inventions in Japan, with 970 patents from 2014 to October 2022, and in April 2023, launched “Fujitsu Kozuchi — Fujitsu AI Platform”. Kozuchi enables users to rapidly and securely test advanced AI technologies and has offered a wide range of customers and partners access to some of Fujitsu’s most advanced AI technologies.

AI represents one of the most rapidly developing technologies of our time, contributing to the solution of various societal and industrial issues. However, this ongoing development requires advanced expertise in the development and operation of AI technologies, and concerns about the fairness of AI technology keep increasing. To enable further spread and enhancement of AI, a platform to openly share AI technologies with engineers worldwide represents an important prerequisite.

Fujitsu is providing automated machine learning and AI fairness technologies as OSS via the Linux Foundation, enabling developers around the world to access and widely use Fujitsu’s technology at the source code level to accelerate technological advancement and the development of new applications, while also addressing concerns around fairness and transparency that remain a critical priority in the field of AI ethics.

• SapientML focuses on developing an Automated Machine Learning (AutoML) technology that produces code for rapidly building machine learning models using tabular data. It empowers data scientists to create precise and interpretable AI models swiftly. With this innovation, data scientists can instantly generate highly accurate models and fine-tune them for improved performance using the generated code, streamlining the model development process.
• The Intersectional Fairness project addresses the issue of fairness in AI by targeting the detection and mitigation of biases that may not be readily apparent. It focuses on combatting discrimination against particular groups resulting from biased training data. The project’s primary goal is to create an AI fairness technology capable of identifying and mitigating “intersectional biases,” which arise when multiple attributes, such as age, gender, and nationality, interact in complex ways, often overlooked in AI systems. For example: imagine a group where the pass rate of young women in an exam is only 33.3%, a significantly lower rate than the rest of the group; this bias, pass rate difference, becomes apparent only when the combination of attributes for investigating the rate is considered; AI fairness technology aims to mitigate such biases while maintaining sufficient accuracy by using a method that adjusts the bias for each group and determines the acceptance line for readjusting the bias from the perspective of the whole.

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