Responsible AI enables the design, development and deployment of ethical AI systems and solutions. Ethical AI acts as intended, fosters moral values and enables human accountability and understanding. Organizations may expand or customize their ethical AI requirements, but fundamental criteria include soundness, fairness, transparency, accountability, robustness, privacy and sustainability.

What are concerns involving AI ethics?

AI—if built without the right algorithmic considerations, if trained on data that has inherent bias in it, or if left ungoverned—has the potential to perpetuate unintended consequences and not perform the task it was designed and intended to perform. All of which puts customer privacy and safety at risk, and weakens trust in the technology (and the company using it) in the process. Any company that has an intention of scaling AI needs to think about the ethical implications of using AI to make decisions that will impact not just the business, but its employees and customers.

What are the key principles of responsible AI?

The key principles of Responsible AI are: Soundness : Comprehend context as well as uphold data quality and model performance Fairness : Identify and remove discrimination and support diversity and inclusion Transparency : Provide explainability, understandability and traceability Accountability : Manage oversight, redress and auditability Robustness : Ensure security and resilience of systems from breaches or tampering as well as readiness of a response plan Privacy : Safeguard personally identifiable information, data ethics and human rights as well as comply with data owner consents Sustainability : Consideration of human-centred ethics as well as societal and environmental well-being

What are steps to ensure AI is ethical?

Organizations should use the four pillars of Responsible AI to apply AI ethically and responsibly: Principles and governance : define and articulate a Responsible AI mission and principles and establish a cross-organization governance structures that builds confidence in AI technologies. Risk, policy and control : strengthen compliance with current laws and regulations, while monitoring for developments; develop policies to mitigate risk that you operationalize through a risk management framework. Technology and enablers : develop tools and techniques to support ethical AI principles and build them into AI systems and platforms. Culture and training : empower leadership to elevate Responsible AI as a critical business imperative; and require training to give all employees a clear understanding of ethical AI principles success criteria.

AI ethics & governance

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Responsible AI: Scale AI with confidence

Artificial Intelligence

From AI compliance to competitive advantage

Explore organizations' attitudes towards AI regulation and their readiness to embrace it.

Minimize unintended bias

Build responsibility into your AI to ensure that the algorithms – and underlying data – are as unbiased and representative as possible.

Ensure AI transparency

To build trust among employees and customers, develop explainable AI that is transparent across processes and functions.

Create opportunities for employees

Empower individuals in your business to raise doubts or concerns with AI systems and effectively govern technology, without stifling innovation.

Protect the privacy and security of data

Leverage a privacy and security-first approach to ensure personal and/or sensitive data is never used unethically.

Benefit clients and markets

By creating an ethical underpinning for AI, you can mitigate risk and establish systems that benefit your shareholders, employees and society at large.

Responsible AI in HR

Enabling trustworthy AI

Principles and governance

Define and articulate a Responsible AI mission and principles, while establishing a transparent, governance structure across the organization that builds confidence and trust in AI technologies.

Risk, policy and control

Strengthen compliance with current laws and regulations while monitoring future ones, develop policies to mitigate risk and operationalize those policies through a risk management framework with regular reporting and monitoring.

Technology and enablers

Develop tools and techniques to support principles such as fairness, explainability, robustness, traceability and privacy, and build them into the AI systems and platforms that are used.

Culture and training

Empower leadership to elevate Responsible AI as a critical business imperative and require training to provide all employees with a clear understanding of Responsible AI principles and criteria for success.