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

An interdisciplinary, innovation-friendly approach can help you design responsibility into your AI from the start.

Operational: Set up governance and systems that will enable AI to flourish.

Technical: Ensure systems and platforms are trustworthy and explainable by design.

Organizational: Democratize the new way of working and facilitate human + machine collaboration.

Reputational: Articulate the responsible AI mission and ensure it’s anchored to your company’s values, ethical guardrails, and accountability structure.

Working together to mitigate AI risk

Develop workshops tailored to your business’ needs across every pillar of our Responsible AI approach.

Operational workshop

Use your customized set of Responsible AI principles to help establish your governance strategy, shape key objectives, and outline desired outcomes.

Technical workshop

Architect and deploy AI models, systems, and platforms that are trustworthy and explainable by design.

Organizational workshop

Identify new and changing roles and see where you need to upskill, re-skill, or hire employees to accommodate the new way of working.

Reputational workshop

Articulate a Responsible Business mission, anchored to your company’s core values and informed by brand and public risk assessments and guidance.

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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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