Trust and responsibility.

Earned and practiced daily.

Dear Stakeholder,

Trust and responsibility have been cornerstones of IBM’s business since the beginning. These core values permeate our culture, from the labs to the boardroom. And they manifest in every relationship: with our employees, with our clients, and with the communities in which we live and work. In this report, you will read about the many achievements we made to further this foundation of trust and responsibility throughout 2018.

Explore these stories of how IBM and our clients are changing work and business — and ultimately, the world.
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Building trust into AI

AI Fairness 360 is a comprehensive open source toolkit to help researchers and developers detect, understand, and mitigate unwanted algorithmic bias in data sets and machine learning models throughout the AI application lifecycle.

AI Fairness 360
Duration: 1:52 minutes
Instrumenting trust into data sets and machine learning models will accelerate the adoption of AI and engender increased confidence in these general-purpose technologies.
Aleksandra Mojsilović
IBM Fellow, Head of Foundations of Trusted AI, IBM Research, and Co-Director of IBM Science for Social Good
Bias and fairness

Artificial intelligence and machine learning are becoming foundational technologies used to inform decisions that make a big difference in the world. As a result, addressing issues of bias and fairness in these systems and applications is essential. “AI is now being used in many different consequential applications, from natural language interaction to flagging compliance challenges. The issue is in building machine learning models that we trust,” says Kush Varshney, IBM researcher and founding co-director of IBM Science for Social Good.

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Enabling developers

One of IBM’s core Trust and Transparency Principles is that new technology, including AI, must be transparent and explainable. IBM’s AI Fairness 360 contains more than 70 fairness metrics and 10 state-of-the-art bias mitigation algorithms designed to translate algorithmic research from the lab into practices as far-reaching as finance, human capital management, healthcare, and education.

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Lack of trust and transparency in machine learning and AI models can impede their ability to deliver significant and measurable benefits for enterprise at scale. The AI Fairness 360 toolkit and other IBM Trusted AI efforts aim to bring more fairness and accountability into the equation and enable businesses to tap into historic levels of opportunity while remaining aligned with our core human values.