IBM Addresses AI Bias with Massive Image Archive

IBM Research has been exploring artificial intelligence and machine learning technologies and techniques for decades. We believe AI will transform the world in dramatic ways in the coming years – and we’re advancing the field through our portfolio of research focused on three areas: towards human-level intelligence, platform for business, and hardware and the physics of AI.

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Kriti Sharma is the vice president of AI at Sage, a market leader in business software that helps manage everything from money to people.Kriti was listed in Forbes 30 under 30 in 2017 and was.

AI is a reflection of its creator. To understand how bias is becoming embedded into AI, we have to look at how we build it. To start, AI creators design these experiences by forming the questions.

Continuing to build trust into AI services. IBM Research has developed a comprehensive strategy that addresses multiple dimensions of trust in AI solutions. We will continue to push innovations not just for checking and explaining bias, but also for debiasing or mitigating bias in data and models. We are also working to add more temporal query.

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IBM Addresses AI Bias with Massive Image Archive. It’s also an important factor in how accurate information from a query using artificial intelligence turns out. IBM has built its reputation.

We have previously shared that ensuring AI models remain free from bias is one of the biggest. an area of research that aims to address this trade-off between performance and interpretability, and.

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The simple perceptron could be used to solve linear separable problems, as shown in the following image. In this illustration, a line divides the two classes (the result of a logical OR operation), which can be implemented as a straight line (or decision boundary). That decision boundary is a function of the weights for the inputs and the bias.

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IBM has also launched the open source AI Fairness 360 toolkit, which includes algorithms, code and tutorials intended to help organizations detect bias themselves. To learn more about IBM efforts to root out bias in AI, read the full article at Venturebeat and the IBM research blog post.

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