Responsible AI at PwC

Building and operating AI you can trust

The potential for AI-based technologies to fundamentally alter how we live, and work is potentially limitless — but to harness and preserve the value created requires attention to and the management of the attendant risks. 

When you infuse AI into business processes, productivity tools, and critical decisions with the purpose of driving incremental value, you need to be sure that you understand what AI is doing and why. Is it making accurate, bias-aware decisions? Is it violating anyone’s privacy? Can you govern and monitor this powerful technology that doesn’t slow growth or innovation? Globally, organisations recognise the need for Responsible AI but are at different stages of the journey.

Responsible AI (RAI) is an approach to managing risks associated with an AI-based solution.  Now is the time to evaluate and augment existing practices or create new ones to help you responsibly harness AI and be prepared for coming regulation. Investing in Responsible AI at the outset can give you an edge that competitors may not be able to overtake.

Risks can originate from many different sources as AI solutions are being implemented. A standardized AI risk taxonomy and toolkit can help assess potential risks and guide necessary mitigation strategies, creating the foundation for an effective and efficient AI governance framework.

Build trust with stakeholders and society

Your stakeholders, including board members, customers, and regulators, will have many questions about your organisation's use of AI and data, from how it’s developed and deployed to how it’s monitored and governed and whether it’s providing the value they expect. You not only need to be ready to provide the answers, but you must also demonstrate ongoing legal and regulatory compliance.

Our Responsible AI Toolkit is a suite of customizable frameworks, tools and processes designed to help you harness the power of AI in a manner that engenders trust in the solution from strategy through execution. With the Responsible AI Toolkit, we’ll tailor our solutions to address your organisation’s unique business requirements and AI maturity.

Potential AI Risks

A variety of factors can impact AI risks, changing over time, stakeholders, sectors, use cases, and technology. Below are the six major risk categories for application of AI technology. 

Performance

AI algorithms that ingest real-world data and preferences as inputs may run a risk of learning and imitating possible biases and prejudices.

Performance risks include:

  • Risk of errors
  • Risk of bias and discrimination
  • Risk of opaqueness and lack of interpretability
  • Risk of performance instability

Security

For as long as automated systems have existed, humans have tried to circumvent them. This is no different with AI.

Security risks include:

  • Adversarial attacks 
  • Cyber intrusion and privacy risks
  • Open source software risks

Control

Similar to any other technology, AI should have organisation-wide oversight with clearly-identified risks and controls.

Control risks include:

  • Lack of human agency
  • Detecting rogue AI and unintended consequences
  • Lack of clear accountability

Economic

The widespread adoption of automation across all areas of the economy may impact jobs and shift demand to different skills.

Economic risks include:

  • Risk of job displacement
  • Enhancing inequality
  • Risk of power concentration within one or a few companies

Societal

The widespread adoption of complex and autonomous AI systems could result in “echo-chambers” developing between machines, and can have broader impacts on human-human interaction.

Societal risks include:

  • Risk of misinformation and manipulation
  • Risk of an intelligence divide
  • Risk of surveillance and warfare

Enterprise

AI solutions are designed with specific objectives in mind which may compete with overarching organisational and societal values within which they operate. Communities often have long informally agreed to a core set of values for society to operate against. There is a movement to identify sets of values and thereby the ethics to help drive AI systems, but there remains disagreement about what those ethics may mean in practice and how they should be governed. Thus, the above risk categories are also inherently ethical risks as well. 

  • Enterprise risks include:
  • Risk to reputation
  • Risk to financial performance
  • Legal and compliance risks
  • Risk of discrimination
  • Risk of values misalignment

Trust is core to our purpose at PwC

Our human-led, tech-powered team can help you build AI responsibly and bring big ideas to life across all stages of AI adoption.

  • AI, Data & Data Use Governance
  • AI and Data Ethics
  • AI Expertise: Machine Learning, Model Operations & Data Science
  • Privacy
  • Cybersecurity
  • Risk Management 
  • Change Management
  • Compliance and legal
  • Sustainability and Climate Change
  • Diversity and Inclusion

We support all phases of the RAI journey

The foundation for responsible AI is an end-to-end enterprise governance framework, focusing on the risks and controls along your organization’s AI journey—from top to bottom.

  • Assess: Technical and qualitative assessments of models and processes to identify gaps
  • Build: Development and design of new models and processes, given a specific need and opportunity
  • Validate + Scale: Technical model validation and deployment services; governance and ethics change management
  • Evaluate + Monitor: Readiness for AI including confirming controls framework design, internal audit training

Innovate responsibly

Whether you're just getting started or are getting ready to scale, Responsible AI can help. Drawing on our proven capability in AI innovation and deep global business expertise, we'll assess your end-to-end needs, and design a solution to help you address your unique risks and challenges.

Recognition & Awards

  • 2020 World Changing Idea, Responsible AI Toolkit, FastCompany
  • Ranked Leader for AI Consulting, Forrester
  • Outstanding achievement in Enterprise, Adoption of AI and AI Ethics, CogX
  • Ranked Leader for Data & Analytics Services, Gartner
  • 100 Brilliant Women in AI Ethics

Contact us

Sean Joyce

Sean Joyce

Partner, Global Cybersecurity and Privacy Leader, Risk Services leader, PwC United States

Sudipta  Ghosh

Sudipta Ghosh

Data & Analytics Leader, PwC India

Tel: +91 9987434327

Chris Oxborough

Chris Oxborough

Lead for Responsible AI, Cloud Leader for Risk, Partner, PwC United Kingdom

Tel: +44 (0) 78 1851 0537

Hendrik Reese

Hendrik Reese

Partner, PwC Germany

Tel: +49 1517 0423-201

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