Yoonyoung Park

Mathias Steiner

Hendrik Hamann

Marina Rakhlin

Tim Erdmann

Jed Pitera

Daniel P. Sanders

Kristin Schmidt

Young-Hye Na

Maxwell Giammona

News

Research

Explainer

IBM researchers investigate ways to help reduce bias in healthcare AI

Closing the STEM career gender gap

IBM Research is accelerating discoveries to tackle climate change

Accelerating discovery for societal and economic impact

Privacy and the Connected Mind

How to make AI fair

Science

AAAI 2022

FAccT 2022

NeurIPS 2022

Trustworthy AI

Fairness, Accountability, Transparency

Responsible Technology

Exploratory Science

Materials Discovery

Healthcare

Climate

Impact Science

Accelerated Discovery

Technology impacts every aspect of our work and lives. It is imperative that technology is built responsibly to reduce differential effects on various populations. At IBM Research, we are working to ensure that the technologies we create promote beneficial outcomes for our clients and for the world.

To respond to the global climate emergency, we're using AI and hybrid cloud to accelerate discovery of climate mitigation and adaption solutions. We're improving carbon emission performance through optimization and capture, and preparing enterprises for the impact of climate change.

Climate and Sustainability

Using AI and the hybrid cloud to accelerate the discovery of climate mitigation and adaption solutions to respond to the growing global climate emergency.

Much of the information that can help transform enterprises is locked away in text, like documents, tables, and charts. We’re building advanced AI systems that can parse vast bodies of text to help unlock that data, but also ones flexible enough to be applied to any language problem.

Natural Language Processing

To trust AI systems, explanations can go a long way. We’re creating tools to help debug AI, where systems can explain what they’re doing. This includes training highly optimized, directly interpretable models, as well as explanations of black-box models and visualizations of neural network information flows.

Explainable AI

We’re creating tools to help AI systems explain why they made the decisions they did. 

Biases can lead to systematic disadvantages for marginalized individuals and groups — and they can arise in any point in the AI development lifecycle. To increase the accountability of high-risk AI systems, we're developing technologies to increase their end-to-end transparency and fairness.

We’re developing technologies to increase the end-to-end transparency and fairness of AI systems. 

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A machine learning-generated animation of satellite images of Earth.

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Accelerating discovery of more sustainable materials through AI and automation