AI Ethics
IBM’s multidisciplinary, multidimensional approach is helping advance responsible AI. Read our perspective on foundation model opportunities, risks and mitigations.
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Building trust in AI

AI is embedded in everyday life, business, government, medicine and more. At IBM®, we are helping people and organizations adopt AI responsibly. Only by embedding ethical principles into AI applications and processes can we build systems based on trust.

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Accelerate responsible, transparent and explainable AI workflows for both generative AI and machine learning models

AI Academy

Watch the episode: Trust, transparency and governance in AI

Discover how AI governance can help build responsible AI workflows
Our Principles and Pillars

The Principles for Trust and Transparency are the guiding values that distinguish IBM’s approach to AI ethics.

The purpose of AI is to augment human intelligence

At IBM, we believe 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 their algorithms’ recommendations.

Principles for Trust and Transparency

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



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

AI Explainability 360


Properly calibrated, AI can assist humans in making fairer choices.

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

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Train, validate, tune, and deploy foundation and machine learning models with ease. 


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Accelerate responsible, transparent and explainable data and AI workflows.

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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 our point of view
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

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

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.

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Read the 2022 IBM Impact Report


Francesca Rossi

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 Christina Montgomery

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Our positions

A Policymaker's Guide to Foundation Models

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

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Foundation models: Opportunities, risks, and mitigations

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

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Precision regulation for data-driven business models

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

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Precision regulation for AI

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

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Responsible advancement of neurotechnology

White paper on privacy risks of Brain-Computer Interfaces.

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Data responsibility

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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Facial recognition

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

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Mitigating bias in AI

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

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Learning to trust AI systems

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

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Standards for protecting at-risk groups in AI bias auditing

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

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Wide-ranging partnerships and internal initiatives furthering AI ethics U.S. Chamber of Commerce

Automation With a Human Touch: How AI Can Revolutionize Our Government.

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Notre Dame-IBM Tech Ethics Lab

Addresses ethical concerns raised by the use of technologies to address society’s problems.

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European Commission Expert Group on AI

Defines the ethics guidelines for trustworthy AI.

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Rome Call for AI ethics

IBM partners with the Vatican to endorse ethical guidelines around AI.

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Partnership on AI

Brings together diverse global voices to define best practices for beneficial AI.

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Everyday Ethics for AI

A guide for embedding ethics in AI design and development.

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Embracing Our Quantum Future

Preparing for Tomorrow by Future-Proofing in the Present.

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The Data & Trust Alliance

Putting trust into practice through the responsible use of data and AI.

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Advancing AI ethics beyond compliance

Explores how AI ethics can progress from abstract theories to concrete practices.

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IBM Institute for Business Value

Discover how trustworthy AI can deliver business value.

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