AI driving transparency, explainability, and trust
Algorithms can be destructive when they produce inaccurate or biased results, an inherent concern amplified by the black box facing any leader who wants to be confident about their use. That is why there is hesitancy in handing over decisions to machines without being confident in how decisions are made and whether they’re fair and accurate. This is an AI trust gap.
Gaining confidence with your AI
The benefits of AI will only fully emerge when algorithms become explainable (and, hence, understandable) in simple language, to anyone. The trust gap exists because there is no transparency of AI; instead, there is an inherent fear of the unknown surrounding this technology. Gaining trust also involves understanding the lineage of the AI models and protecting them (and data that forms them) from different types of adversarial attacks and unauthorized use. Critical business decisions made by AI affect the brand—and consumer trust in the brand—and they can have an enormous impact on the well-being or safety of consumers and citizens. No one wants to say “because the machine said so.” No one wants to get AI wrong.
This report is for leaders involved in the world of Artificial Intelligence and Machine Learning algorithms. The business and compliance imperative to understand and be confident in AI technologies has reached critical mass. This paper explains the urgency and describes methods and tools that can help leaders govern their AI programs.
Most leaders don’t know how to close the trust gap because they don’t know how to govern AI and see the big picture of its operation.
An effective framework will help organizations gain confidence in their AI technology. Such approach should dig deep into AI at the enterprise and individual model to help ensure that key trust imperatives are integrated and controlled throughout. It should continuously assess and maintain control over sophisticated, evolving algorithms by putting in place methods, controls, and tooling that secure the trust anchors along the lifecycle, from strategy through evolution. It should also provide clear guidance for the organizations—stakeholders across various management and oversight functions.
KPMG’s AI in Control
KPMG developed the AI in Control framework to help organizations drive greater confidence and transparency through tested AI governance constructs, as well as methods and tooling along the AI lifecycle, from strategy through evolution. By design, this framework addresses the inherent risks outlined in the sections above and it includes some of the key recommendations and leading practices for establishing AI governance, performing AI assessments, and building continuous AI monitoring and visualizations.
Continuous monitoring and dashboards
Uncovering the full potential of your AI
Today’s organizations rely heavily on algorithm-based applications to make critical business decisions. While this unlocks opportunities, it also raises questions about trustworthiness. As we enter an age of governance by algorithms, organizations must think about the governance of algorithms to build trust in outcomes and achieve the full potential of artificial intelligence.