Trustworthy AI
Our trust in technology relies on understanding how it works. It’s important 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.
Our work
- Q & AKim Martineau
IBM further strengthens Granite for enterprise deployment with HackerOne
NewsMike MurphyDebugging LLMs to improve their credibility
ResearchKim MartineauHow IBM’s Kush Varshney became the face of the modern ‘camera man’
Q & AKim MartineauA 360 review of AI agent benchmarks
ResearchKim MartineauAn invisible watermark to keep tabs on tabular data
ResearchKim Martineau- See more of our work on Trustworthy AI
Topics
AI Testing
We’re designing tools to help ensure that AI systems are trustworthy, reliable and can optimize business processes.Adversarial Robustness and Privacy
We’re making tools to protect AI and certify its robustness, and helping AI systems adhere to privacy requirements.Explainable AI
We’re creating tools to help AI systems explain why they made the decisions they did.Fairness, Accountability, Transparency
We’re developing technologies to increase the end-to-end transparency and fairness of AI systems.Trustworthy Generation
We’re developing theoretical and algorithmic frameworks for generative AI to accelerate future scientific discoveries.Uncertainty Quantification
We’re developing ways for AI to communicate when it's unsure of a decision across the AI application development lifecycle.
Publications
Agentic Process Observability: Discovering Behavioral Variability
- 2025
- ECAI 2025
XABPs: Towards eXplainable Autonomous Business Processes
- Peter Fettke
- Fabiana Fournier
- et al.
- 2025
- ECAI 2025
When in Doubt, Cascade: Towards Building Efficient and Capable Guardrails
- 2025
- AIES 2025
Highlight All the Phrases: Enhancing LLM Transparency through Visual Factuality Indicators
- Hyo Jin Do
- Rachel Ostrand
- et al.
- 2025
- AIES 2025
Localizing Persona Representations in LLMs
- 2025
- AIES 2025
Bridging Expertise and Participation in AI: Multistakeholder Approaches to Safer AI Systems for Youth Online Safety
- Ozioma Collins Oguine
- Johanna Olesk
- et al.
- 2025
- CSCW 2025
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.