Worldwide spending on cognitive and artificial intelligence (AI) systems continues to grow every year. But while many companies plan to implement AI, a large majority lack confidence in their ability to govern and manage new risk this emerging technology poses. KPMG’s AI in Control can help.
KPMG’s AI In Control helps organizations address key inherent risks and misperceptions associated with Artificial Intelligence and Machine Learning. This, in turn, will help foster transparency and confidence in AI and serve as a foundation for innovation and new use cases.
AI in Control incorporates our AI/ML experience, tools, and methodologies as well as our multidisciplinary capabilities around governance and risk management into one solution designed to complement your AI program and strategy.
We combined leading industry standards and frameworks and practices to define an approach for both establishing a responsible AI program for your organization as well as evaluating individual AI/ML algorithms and models. Our approach is founded on four key AI imperatives including integrity, explainability, free of bias, and resilience.
Our solution, helps organizations stand up a responsible AI program and build and evaluate sound AI/ML models to help drive better adoption, confidence, and compliance.
Through subject matter experience, research and collaboration, KPMG has developed a governance framework to help inform design and operations of responsible AI programs. The framework also allows for assessments and evaluations of AI and ML models. We leverage our own proven methodology for delivering AI solutions to our clients to help better understand the integrity, explainability, bias, and technical robustness of the system.
KPMG tailors its approach to helping companies with AI governance and controls according to their unique needs. Our work can include all or select activities across project initiation; capabilities, data and model reviews; gap analysis; roadmap and action plan development; and control implementation and gap remediation.
Four key pillars of trust guide our application of tools, techniques and knowledge for an AI governance framework. Incorporating these four pillars into the design, build and management of your AI program and models will drive confidence, transparency, and more informed decision making:
Models that have integrity and data validity at the core including lineage and appropriateness of how data is used across the system.
Models that must be free of bias, are inclusive and avoid unfair treatment of certain protected groups and comply with regulation or policy.
Models that can explain learning, decisions in business terms and allow interpretation based on their explanation.
Models are interoperable between various runtimes, providers, or frameworks. The models, ground truth and feedback are safe and secure from harm or adversarial attacks.
The adoption of artificial intelligence capabilities is accelerating across the financial industry, creating regulatory challenges