Youssef Mroueh

Karthikeyan Shanmugam

Vera Liao

Prasanna Sattigeri

Payel Das

Research

Release

AI boosts the discovery of metamaterials vital for next-gen gadgets

IBM’s Uncertainty Quantification 360 toolkit boosts trust in AI

ICML 2021

AAAI 2021

NeurIPS 2020

AIES 2021

Trusted AI

Trustworthy Generation

Human-Centered AI

Materials Discovery

Uncertainty Quantification

When AI can explain to us that it's unsure, it adds a critical layer of transparency for its safe deployment and use. We’re developing ways to foster and streamline the common practices of quantifying, evaluating, improving, and communicating uncertainty in the AI application development lifecycle.

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

It can take over 10 years to come up with new materials. At IBM Research, we’re looking to accelerate the discovery process using new AI methods, robotics, the hybrid cloud, and quantum computers. Our goal is to unlock new properties and materials to address global challenges in years not decades.

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.

Fairness, Accountability, Transparency

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

An open-source Python package that provides a diverse set of algorithms to quantify uncertainty, as well as capabilities to measure and improve UQ to streamline the development process.

Uncertainty Quantification 360

Example of a metasurface without light direction.

metamaterial-surface.png

IBM’s Uncertainty Quantification 360 toolkit increases AI uncertainty

Uncertainty Quantification

Our work

IBM’s Uncertainty Quantification 360 toolkit boosts trust in AI

AI boosts the discovery of metamaterials vital for next-gen gadgets

Tools + code

Uncertainty Quantification 360

Publications

Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond

Fair Selective Classification via Sufficiency

Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty

Learning Prediction Intervals for Model Performance

Approximate Cross-Validation for Structured Models

Active learning of deep surrogates for PDEs: application to metasurface design

Related teams

Explainable AI

Fairness, Accountability, Transparency

Materials Discovery

Causality