AI ethics & governance

Take an interdisciplinary approach that supports agile innovation and ensures governance of your AI systems.

Scale AI responsibly

AI brings unprecedented opportunities to businesses, but also incredible responsibility. The output from AI systems has a real bearing on people’s lives, raising considerable questions around AI ethics, data governance, trust, and legality. The more decisions a business puts into the hands of AI, the more they accept significant risks, such as reputational, employment/HR, data privacy, health and safety issues. However, according to an Accenture global research study, 88% of respondents do not have confidence in AI-based decisions.

So how do we learn to trust AI?

Responsible AI is the practice of designing, developing, and deploying AI with good intention to empower employees and businesses, and fairly impact customers and society—allowing companies to engender trust and scale AI with confidence.

To create trust in AI, organizations must move beyond defining Responsible AI principles and put those principles into practice.

AI for disability inclusion

Learn how AI can unlock the incredible potential of talent with disabilities.

Commit to confidence

With Responsible AI, you can shape key objectives and establish your governance strategy, creating systems that enable AI and your business to flourish.

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.

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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.

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Identify AI bias before you scale

The Algorithmic Assessment is a technical evaluation that helps identify and address potential risks and unintended consequences of AI systems across your business, to engender trust and build supportive systems around AI decision making.

Use cases are first prioritized to ensure you are evaluating and remediating those that have the highest risk and impact.

Once priorities are defined, they are evaluated through our Algorithmic Assessment, involving a series of qualitative and quantitative checks to support various stages of AI development. The assessment consists of four key steps:

  1. Set goals around your fairness objectives for the system, considering different end users.
  2. Measure & discover disparities in potential outcomes and sources of bias across various users or groups.
  3. Mitigate any unintended consequences using proposed remediation strategies.
  4. Monitor & control systems with processes that flag and resolve future disparities as the AI system evolves.
The Algorithmic Assessment consists of four key steps: (1) Set goals, (2) measure and discover, (3) mitigate, (4) monitor and control.

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