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    Trustworthy AI

    Our trust in technology relies on understanding how it works. It’s important to understand why AI makes the decisions it does. We’re developing tools to make AI more explainable, fair, robust, private, and transparent.

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    Artificial intelligence systems have become increasingly prevalent in everyday life and enterprise settings, and they’re now often being used to support human decision-making. These systems have grown increasingly complex and efficient, and AI holds the promise of uncovering valuable insights across a wide range of applications. But broad adoption of AI systems will require humans to trust their output.

    When people understand how technology works, and we can assess that it’s safe and reliable, we’re far more inclined to trust it. Many AI systems to date have been black boxes, where data is fed in and results come out. To trust a decision made by an algorithm, we need to know that it is fair, that it’s reliable and can be accounted for, and that it will cause no harm. We need assurances that AI cannot be tampered with and that the system itself is secure. We need to be able to look inside AI systems, to understand the rationale behind the algorithmic outcome, and even ask it questions as to how it came to its decision.

    At IBM Research, we’re working on a range of approaches to ensure that AI systems built in the future are fair, robust, explainable, account, and align with the values of the society they’re designed for. We’re ensuring that in the future, AI applications are as fair as they are efficient across their entire lifecycle.

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    Science for Social Good

    IBM Science for Social Good partners IBM Research scientists and engineers with academic fellows, subject matter experts from NGOs, public sector agencies, and social enterprises to tackle emerging societal challenges using science and technology.

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    Building trustworthy AI with Watson

    Our research is regularly integrated into Watson solutions to make IBM’s AI for business more transparent, explainable, robust, private, and fair.

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