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AI ethics & governance

Design and deploy Responsible AI solutions that are ethical, transparent, and trustworthy.

NEW GLOBAL RESEARCH

The art of AI maturity: Advancing from practice to performance

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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.

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From AI compliance to competitive advantage

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.

Responsible AI in HR

Responsible AI practices can be applied to any industry or function. Take Human Resources (HR) as an example. When done correctly, AI systems can allow organizations to make more ethical, effective and efficient talent decisions by eliminating potential sources of bias. Explore more in our interactive report.

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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.

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.

Case studies

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Creating a sense of belonging

A global retailer and Accenture co-created a multiyear inclusion and diversity strategy to facilitate a greater sense of belonging for...

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Evolving financial services

The Monetary Authority of Singapore and Accenture established the Veritas industry consortium to provide groundbreaking...

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Banking

Fairness you can bank on

Applying algorithmic fairness to the real world of retail banking.

 

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Our leaders

Ray Eitel-Porter

Managing Director – Applied Intelligence, Global Lead for Responsible AI
LinkedIn

Marisa Tricarico

Regional Lead – North America, Responsible AI
LinkedIn

Rossana Bianchi

Regional Lead – Growth Markets, Responsible AI
LinkedIn

Related capabilities

No two clients are alike. Whether it's using a proven turnkey solution or a custom program, we'll bring our deep industry expertise to solve your unique needs, backed by a team spanning more than 125 countries. Some of our offerings include:

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Frequently asked questions

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.

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.

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

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.