Trustworthy AI
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.
Overview
Artificial intelligence systems have become increasingly prevalent in everyday life and enterprise settings, and they’re now often being used to support human decision-making. These systems have grown increasingly complex and efficient, and AI holds the promise of uncovering valuable insights across a wide range of applications. But broad adoption of AI systems will require humans to trust their output.
When people understand how technology works, and we can assess 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.
When people understand how technology works, and we can assess 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.
Our work
What is red teaming for generative AI?
ExplainerKim Martineau- Adversarial Robustness and Privacy
- AI
- AI Testing
- Fairness, Accountability, Transparency
- Foundation Models
- Natural Language Processing
- Security
- Trustworthy AI
An AI model trained on data that looks real but won’t leak personal information
ResearchKim Martineau- AI
- AI Privacy
- Data and AI Security
- Finance
- Foundation Models
The latest AI safety method is a throwback to our maritime past
ResearchKim Martineau- AI
- AI Transparency
- Explainable AI
- Fairness, Accountability, Transparency
- Generative AI
What is AI alignment?
ExplainerKim Martineau- AI
- Automated AI
- Fairness, Accountability, Transparency
- Foundation Models
- Natural Language Processing
Find and fix IT glitches before they crash the system
NewsKim Martineau- AI for Code
- AI for IT
- Explainable AI
- Foundation Models
- Generative AI
An open-source toolkit for debugging AI models of all data types
Technical noteKevin Eykholt and Taesung Lee- Adversarial Robustness and Privacy
- AI Testing
- Data and AI Security
- See more of our work on Trustworthy AI
Topics
- 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.
Science for Social Good
IBM Science for Social Good partners IBM Research scientists and engineers with academic fellows, subject matter experts from NGOs, public sector agencies, and social enterprises to tackle emerging societal challenges using science and technology.
Explore the initiative
Publications
- Smit Desai
- Christina Wei
- et al.
- 2024
- CHI 2024
- Harold Benoit
- Liangze Jiang
- et al.
- 2024
- ICLR 2024
- Yu-Lin Tsai
- Chia-yi Hsu
- et al.
- 2024
- ICLR 2024
- Yan Liu
- Yu Liu
- et al.
- 2024
- ICLR 2024
- Felix Petersen
- Aashwin Mishra
- et al.
- 2024
- ICLR 2024
- Subha Maity
- Mayank Agarwal
- et al.
- 2024
- ICLR 2024
Building trustworthy AI with Watson
Our research is regularly integrated into Watson solutions to make IBM’s AI for business more transparent, explainable, robust, private, and fair.
Learn more