Managing AI Risk, Governance, and Visibility through Stakeholder Collaboration in AI Development and Production

The key to managing AI risk, governance, and visibility lies in understanding and tracing the contributions of different stakeholders throughout AI development and production projects. Effective collaboration among stakeholders is essential to ensure AI systems are reliable, compliant, and perform as intended.

According to the global management consulting firm McKinsey, AI systems may inherit risks from various sources, resulting in potentially flawed AI models. These risks can manifest in various ways, such as biased decision-making, discrimination, or non-compliance with regulatory requirements.

To address these risks and enhance AI governance and visibility, companies must be able to identify the factors influencing AI decision-making and ensure stakeholders have a clear understanding of how the AI system operates. This involves tracing the contributions of different stakeholders during AI development and production and creating a transparent environment that enables stakeholders to grasp the inner workings of the AI system.

Enhancing AI Risk Management, Governance, and Visibility through Tracing

In the AI development cycle, businesses must acquire and analyze data from stakeholders so that the AI can learn and improve. This stage involves exploratory analysis, finding and addressing target leakage, and feature engineering. By applying these techniques, businesses can identify target variables and develop algorithms based on the selected variables.

The most critical stage is when stakeholders transform data into insights, often lacking traceability. This step requires interpreting and communicating the project's value to management and key stakeholders. According to the Word Economic Forum, risk management and governance are important aspects of AI development. Integrating risk management, governance, and visibility into the process is crucial and it can be achieved by mapping, measuring, and managing the AI system.

Map: Obtain a comprehensive view of your AI assets and their interrelationships. A unified view and shared understanding of AI inventory allow all stakeholders to collaborate more effectively, ensuring confidence in AI initiatives.

Measure: Assess the usage and outcomes of all your AI assets. Utilize AI-powered applications with quantifiable parameters such as robustness, security, and performance to gauge business risks.

Manage: Provide stakeholders with a clear path to the truth by generating reports, incidents, and cases. In doing so, they can manage business metrics as outcomes of a secure and reliable AI system.

The AI development cycle concludes with the implementation, documentation, and maintenance of the project. By mapping, measuring, and managing your AI-powered applications throughout solution development, deployment, and production, traceability, explainability, and understanding are improved, resulting in enhanced risk management, governance, and visibility.

Enhance AI Risk Management, Governance, and Visibility with Konfer

Konfer offers a comprehensive solution for businesses seeking to improve their AI systems' risk management, governance, and visibility by employing a "map-measure-manage" approach.

Map: Konfer utilizes KonferKnowledge, a repository of all AI software assets and associated attributes, including AI/ML applications, models, features, and data. The KonferKnowledge graph serves as a single source of truth about all AI applications, their locations, dependencies, shared resources, and the metrics and KPIs they influence within the organization.

Measure: KonferConfidence is a quantitative measure that enables businesses to profile and assess AI/ML applications based on performance, robustness, privacy, compliance, and regulatory risk factors. Scores are calculated using metrics and observations and aggregated over time to provide a comprehensive risk assessment.

Manage: KonferTrust generates operational alerts and reports by integrating into an organization's service management and collaboration systems, such as Slack. Stakeholders are alerted to noncompliance issues, and the compliance status of AI/ML applications, models, and data is automatically documented, ensuring effective risk management.

Konfer assists businesses in enhancing AI risk management, governance, and visibility, empowering business leaders to trust AI-powered decision-making and maintain a competitive edge. Schedule a demo with us today to discover how Konfer can help your business achieve improved AI risk management, governance, and visibility in your AI development and production processes.