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IBM is helping to advance responsible AI with a multidisciplinary, multidimensional approach
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Now is the moment for responsible AI

Businesses are facing an increasingly complex, ever-changing global regulatory landscape when it comes to AI. The IBM approach to AI ethics balances innovation with responsibility, helping you adopt trusted AI at scale.

Foundation models: Opportunities, risks and mitigations.

Fostering a more ethical future by leveraging technology

Case study: Building trust in AI

Our principles and pillars The Principles for Trust and Transparency are the guiding values that distinguish the IBM approach to AI ethics. Read the Principles for Trust and Transparency Pillars The purpose of AI is to augment human intelligence

IBM believes AI should make all of us better at our jobs, and that the benefits of the AI era should touch the many, not just the elite few.

Data and insights belong to their creator

IBM clients’ data is their data, and their insights are their insights. We believe that government data policies should be fair and equitable and prioritize openness.

Technology must be transparent and explainable

Companies must be clear about who trains their AI systems, what data was used in training and, most importantly, what went into the recommendations of their algorithms.

The Principles are supported by the Pillars of Trust, our foundational properties for AI ethics. Explainability

Good design does not sacrifice transparency in creating a seamless experience.

AI Explainability 360

Properly calibrated, AI can assist humans in making choices more fairly.

AI Fairness 360

As systems are employed to make crucial decisions, AI must be secure and robust.

Adversarial Robustness 360

Transparency reinforces trust, and the best way to promote transparency is through disclosure.

AI FactSheets 360

AI systems must prioritize and safeguard consumers’ privacy and data rights.

AI Privacy 360 toolkit

Ethics for generative AI

When ethically designed and responsibly brought to market, generative AI capabilities support unprecedented opportunities to benefit business and society alike.

Foundation models: Opportunities, risks and mitigations Read the paper
The CEO’s Guide to Generative AI: Platforms, data, governance and ethics

Human values are at the heart of responsible AI.

The urgency of AI governance

IBM and the Data & Trust Alliance offer insights about the need for governance, particularly in the era of generative AI.

A policymaker’s guide to foundation models

A risk- and context-based approach to AI regulation can mitigate potential risks, including those posed by foundation models.

Putting principles into action across the organization

The IBM AI Ethics Board was established as a central, cross-disciplinary body to support a culture of ethical, responsible and trustworthy AI throughout the organization.

Co-chaired by Francesca Rossi and Christina Montgomery, the Board’s mission is to support a centralized governance, review and decision-making process for IBM ethics policies, practices, communications, research, products and services. By infusing our long-standing principles and ethical thinking, the Board is one mechanism by which IBM holds our company and all IBMers accountable to our values.

Learn more about ethical impact in the 2023 IBM Impact Report


Francesca Rossi

Learn more about Francesca

 Christina Montgomery

Learn more about Christina

Our positions

IBM advocates for policies that balance innovation with responsibility and trust to help build a better future for all.

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IBM's five best practices for including and balancing human oversight, agency and accountability over decisions across the AI lifecycle.

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IBM's perspective on the opportunities posed by foundation models as well as their risks and potential mitigations.

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 Awareness about risks and potential mitigations is a crucial first step toward building and using foundation models responsibly.

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White paper outlining seven recommendations about data-driven business model risks for policymakers.

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Companies should utilize a risk-based AI governance policy framework and targeted policies to develop and operate trustworthy AI.

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White paper on privacy risks of Brain-Computer Interfaces.

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Companies that collect, store, manage or process data have an obligation to handle it responsibly, ensuring ownership and privacy, security and trust.

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IBM no longer produces facial recognition or analysis software. We believe in a governance framework informed by precision regulation.

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Five priorities to strengthen the adoption of testing, assessment and mitigation strategies to minimize bias in AI systems.

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A pioneering paper on accountability, compliance and ethics in the age of smart machines.

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IBM's point of view on protecting at-risk groups in AI bias auditing.

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watsonx.governance Accelerate responsible, transparent and explainable data and AI workflows. Learn more Get the AI governance ebook

Next steps

Find out how IBM can help you accelerate your responsible AI journey.

Book an AI strategy session Discover watsonx.governance