Mike Murphy

Kim Martineau

News

Research

Explainer

IBM’s Stacy Hobson wants to build tech that works for everyone

What is AI alignment?

The latest AI safety method is a throwback to our maritime past

What is red teaming for generative AI?

IBM reaffirms its commitment to the Rome Call for AI ethics

IBM’s Mikhail Yurochkin wants to make AI’s “cool” factor tangible

IBM’s safety checkers top a new AI benchmark

We're developing technological solutions to assist subject matter experts with their scientific workflows by enabling the Human-AI co-creation process.  

Accelerator Technologies

ICLR 2025

CHI 2025

NAACL 2025

Trustworthy AI

Fairness, Accountability, Transparency

Adversarial Robustness and Privacy

AI Transparency

Responsible Technology

Foundation Models

Automated AI

Human-Centered AI

Natural Language Processing

Generative AI

Security

Granite

Explainable AI

AI Testing

Causal modeling is crucial to the effectiveness and trust of AI by ensuring that actions lead to intended outcomes. We study the inference of causal effects and relationships, as well as the application of causal thinking to out-of-distribution generalization, fairness, robustness, and explainability.

Causality

Even advanced AI systems can be vulnerable to adversarial attacks. We’re making tools to protect AI and certify its robustness, including quantifying the vulnerability of neural networks and designing new attacks to make better defenses. And we’re helping AI systems adhere to privacy requirements.

We’re making tools to protect AI and certify its robustness, and helping AI systems adhere to privacy requirements.

The combination of increasingly powerful computers and AI offers the possibility to be able to detect, diagnose, and cure diseases like never before. At IBM Research, we’re working on creating software and AI systems that can convert reams of health data into useable information for clinicians the world over.

Healthcare and Life Sciences

We're building systems that can convert reams of health data into useable information for clinicians the world over.

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.

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.

Fairness, Accountability, Transparency

Our work

IBM’s safety checkers top a new AI benchmark

IBM’s Mikhail Yurochkin wants to make AI’s “cool” factor tangible

IBM reaffirms its commitment to the Rome Call for AI ethics

What is red teaming for generative AI?

The latest AI safety method is a throwback to our maritime past

What is AI alignment?

Projects

Accelerator Technologies

Publications

The Literary Canons of Large-Language Models: An Exploration of the Frequency of Novel and Author Generations Across Gender, Race and Ethnicity, and Nationality

Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation

Responsible Prompting Recommendation: Fostering Responsible AI Practices in Prompting-Time

Ethical Co-Development of AI Applications with Indigenous Communities

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons

JUSTICE OR PREJUDICE? QUANTIFYING BIASES IN LLM-AS-A-JUDGE

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