Our trust in technology relies on understanding how it works. We need to understand why AI makes the decisions it does. We're developing tools to make AI more explainable, fair, robust, private, and transparent.
When people understand how technology works, and we can asses 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.
- AI TestingWe’re designing tools to help ensure that AI systems are trustworthy, reliable and can optimize business processes.
- Adversarial Robustness and PrivacyWe’re making tools to protect AI and certify its robustness, and helping AI systems adhere to privacy requirements.
- Explainable AIWe’re creating tools to help AI systems explain why they made the decisions they did.
- Fairness, Accountability, TransparencyWe’re developing technologies to increase the end-to-end transparency and fairness of AI systems.
- Trustworthy GenerationWe’re developing theoretical and algorithmic frameworks for generative AI to accelerate future scientific discoveries.
- Uncertainty QuantificationWe’re developing ways for AI to communicate when it's unsure of a decision across the AI application development lifecycle.
Tools + code
Uncertainty Quantification 360
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.View project ↗
AI FactSheets 360
Toolkit to create factsheets outlining the details about how an AI service operates, how it was trained and tested, its performance metrics, fairness and robustness checks, intended uses, maintenance, and other critical details.View project ↗
Causal Inference 360
A Python package for modular causal inference analysis and model evaluations.View project ↗
AI Fairness 360
An open-source toolkit of metrics to check for unwanted bias in datasets and machine learning models, and state-of-the-art algorithms to mitigate such bias. Containing over 70 fairness metrics and 10 bias mitigation algorithms, it’s designed to turn fairness research into practical applications.View project ↗
AI Explainability 360
This open source toolkit contains eight algorithms that help you comprehend how machine-learning models predict labels throughout the AI application lifecycle. It’s designed to translate algorithmic research into the real-world use cases in a range of files, such as finance, human capital management, healthcare, and education.View project ↗
ART: Adverserial Robustness Toolbox
A Python library for machine learning security that enables developers and researchers to defend and evaluate machine learning models and applications against the adversarial threats of evasion, poisoning, extraction, and inference.View project ↗
Yao Ma, Suhang Wang, et al.2021KDD 2021
Ronny Luss, Pin Yu Chen, et al.2021KDD 2021
Yi Fung, Chris Thomas, et al.2021ACL-IJCNLP 2021
Dennis Wei2021ICML 2021
Chulin Xie, Minghao Chen, et al.2021ICML 2021
Huck Yang, Yun-Yun Tsai, et al.2021ICML 2021
- See more of our work on Trusted AI