AI ethics & governance
Design and deploy Responsible AI solutions that are ethical, transparent, and trustworthy.
Responsible AI: Scale AI With Confidence
Responsible AI: Scale AI with confidence
AI brings unprecedented opportunities to businesses, but also incredible responsibility. Its direct impact on people’s lives has raised considerable questions around AI ethics, data governance, trust and legality. In fact, Accenture’s 2022 Tech Vision research found that only 35% of global consumers trust how AI is being implemented by organizations. And 77% think organizations must be held accountable for their misuse of AI.
The pressure is on. As organizations start scaling up their use of AI to capture business benefits, they need to be mindful of new and pending regulation and the steps they must take to make sure their organizations are compliant. That’s where Responsible AI comes in.
So, what is Responsible 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.
Explore organizations' attitudes towards AI regulation and their readiness to embrace it.
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Benefits of Responsible AI
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.
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:
- Set goals around your fairness objectives for the system, considering different end users.
- Measure & discover disparities in potential outcomes and sources of bias across various users or groups.
- Mitigate any unintended consequences using proposed remediation strategies.
- Monitor & control systems with processes that flag and resolve future disparities as the AI system evolves.
What we think
To create trust in AI, organizations must move beyond defining Responsible AI principles and put those principles into practice.
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