Posted August 3, 2022

Key Gaps You Need To Address To Achieve AI Transparency, Trust, And Confidence

Artificial Intelligence (AI) applications are revolutionizing the way we do business. But the complex and abstract environment of ungoverned AI apps running across the enterprise is a ticking time bomb waiting to explode. According to a HelpNet security report, a company’s reputation and bottom line are at risk when AI interactions result in ethical issues.

In contrast, customers trust the company more when they perceive their AI interactions are ethical. The majority of surveyed customers (55%) said that “they would purchase more products [from the company] and provide high ratings and positive feedback on social media.”

This is why it is crucial that you enable AI transparency, trust, and confidence. But this is easier said than done due to the inherent complexity and abstract nature of AI systems.

You can achieve transparency, trust, and confidence in your AI by addressing the following gaps.

  1. Silos in your company’s AI ecosystem

Silos occur not only during AI development and production, where blended teams or various stakeholders work separately to build the product. The AI ecosystem also becomes fragmented when business units or departments develop and use disparate AI applications for different use cases. Low-code or no-code is compounding this fragmentation, making AI Auditing complicated and laborious.

Silos make it harder to govern AI assets, users, and components and monitor changes and anomalies that could lead to ethical issues and other risks. Due to silos, mapping the system to pinpoint the root cause of the problem and the possible rippling impact is challenging.

To fill this gap, you need to find a way to harmonize all players, assets, and components in your company’s AI ecosystem — which points us to the next gap.

  1. Lack of a global view of all AI applications, users, and asset information.

The lack of transparency and understanding of an AI system’s inner workings partly “stems from the sheer technical complexity of AI systems.” To achieve AI transparency, trust, and confidence, you need a global view of all your company’s AI applications, all AI users, and all AI components/asset information, including the following:

  • Models
  • Logic/Provenance
  • Proprietary data
  • Third-party data

These assets and components must be visible to all stakeholders so everyone can proactively sense and respond to any issue.

But visibility into these assets and components alone is not enough. The AI ecosystem is inherently complex, so combing through these assets and their information to pinpoint the problem will remain tedious unless a mechanism that allows for easy mapping of the AI system is in place. This leads us to the next gap.

  1. Lack of means to map the AI system.

An AI transparency or trust management system must fulfill three functions: Map, Measure, and Manage. But most solutions are designed for measuring and managing risks only.

Mapping is equally crucial. It helps pinpoint the what, when, where, why, and how of the problem by providing end-to-end visibility with relationships.

Mapping also helps prevent the rippling impact of the problem by allowing stakeholders to see the relationships among AI assets and components and how an issue that arose from one element can impact another or the entire AI ecosystem. It empowers them to quickly identify all components, applications, and customers that could be affected to ensure business continuity. Confidence increases when this map is crystal clear to the stakeholders.

Now, how do we fill the gaps?

To address these gaps and achieve AI transparency, trust, and confidence, take advantage of a solution that allows you to:

  • Overcome silos by enabling a digital holistic overview of all your company’s AI assets, components, and users.
  • Enable a robust AI transparency/trust management system that allows for Mapping, Measuring, and Managing the entire AI system to prevent/deal with issues and risks in real-time.
  • Create an operational framework of robustness, transparency, and security, leading to Trusted AI/ML solutions.
  • Elevate Traditional AI/ML Development Life Cycles into Trusted AI/ML Development Life Cycles

This is where Konfer Confidence Cloud comes in.

Konfer Confidence Cloud service empowers your organization to be “confAIdent” about your AI initiatives by providing a unified view of your fragmented AI ecosystem and a holistic governance and risk management framework to safeguard you across the AI lifecycle.

Konfer automates the mapping of your AI systems so all stakeholders can easily determine and evaluate the various facets of AI that can generate business risks and confidently manage their business metrics.


By operationalizing Trust of your AI framework, you can avoid unintended consequences and compliance challenges that are not only harmful to businesses, but also could result in security, legal, regulatory, and financial troubles.

Konfer Confidence Cloud increases the trust of AI-powered decision-making, allowing your organization to reap various competitive advantages including business continuity, improved collaboration, and increased regulatory confidence.

Drop us a line if you want to get first-hand experience of how Konfer’s AI Transparency Cloud helps achieve confidence in your AI.

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