KPMG artificial intelligence in control
KPMG artificial intelligence in control

KPMG artificial intelligence in control

Establish greater confidence in your AI technology.

Unleash the full potential of your AI

Many organizations today are turning to algorithm-based applications to make critical business decisions. This unlocks great opportunity — but at the same, raises questions about trustworthiness. How do you find a way to address the trust gap?

Do you trust the black box?

We surveyed 2,400+ C-level executives:


are worried about D&A's impact on reputation


of respondents say they have a high level of trust in their own organization's use of  different types of analytics


We surveyed 120+ internal auditors:


were not confident about the governance in place around AI


question trustworthiness of data and analytics, and are worried about the impact on reputation

Source: A commissioned study conducted by Forrester Consulting on behalf of KPMG International, July 2017

As we enter an age of governance by algorithms, we must think about the governance of algorithms to build trust in their outcomes and, ultimately, realize the full potential of artificial intelligence.

That's where KPMG Artificial Intelligence In Control comes into play.

KPMG’s AI In Control implements a model to help guide organizations along the AI evolutionary lifecycle from strategy through execution to evolution. The AI In Control solution includes methodologies, tools and controls for an AI program to drive better business outcomes through:

Artificial Intelligence Governance – key features:

  • Designs and sets up criteria for building and continuous monitoring and control of AI solutions and their performance, without impeding innovation and flexibility.

Artificial Intelligence Assessment – key features:

  • Conducts diagnostic reviews of Al solutions, and risk assessments of control environments to determine organizational readiness for effective Al control.
  • Provides methods and tools to evaluate business-critical algorithms, puts testing controls in place, and oversees design, implementation and operation of Al programs to help address Al's inherent challenges: integrity, explainability, fairness and agility.

Building an environment that fosters greater AI control

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