Artificial intelligence (AI) offers tremendous opportunities for innovation, efficiency, and growth across various industries. However, as AI systems become increasingly integrated into business operations, the need for thorough and ongoing assessments becomes crucial. These assessments help organizations mitigate risks, ensure compliance, and build trust among stakeholders. To maximize the benefits while minimizing potential harms, organizations must implement structured, comprehensive AI assessments as part of their governance frameworks.  

A well-designed AI assessment should encompass several key aspects, including the intended use cases, resources, data privacy, and bias. These assessments should be initiated early in the AI system’s lifecycle and continue regularly to adapt to evolving technologies and regulatory landscapes. This blog will explore what should be included in an AI assessment, when these assessments should be conducted, and how often they should be performed to maintain responsible AI governance. 

What Should Be Included in an AI Assessment? 

When designing an AI assessment, it is essential to cover various dimensions that ensure the system is fair, compliant, and aligned with the organization’s strategic goals. The following elements should be included: 

1. Use Cases 

• Current and Future Applications: Understand how the AI system will be utilized, not only in its current form but also for potential future use cases. This helps in aligning the AI tool with the organization’s evolving needs and objectives. 

2. Resources and Implementation 

• Budget and Onboarding: Evaluate the financial resources, onboarding process, and support required to successfully implement the AI technology. Involving multiple business units in this evaluation can provide a comprehensive view of the system’s potential impact and facilitate resource sharing. 

• Support Needs: Assess the ongoing support needed to maintain and update the AI system, including technical support, training, and maintenance. 

3. Data Privacy and Security 

• Data Management: Examine the types of data the AI system will handle, how it will be protected, and whether it complies with relevant privacy regulations. Consider the deployment environment (on-premises or cloud-based) and how data will be managed, especially if the AI system involves personal information. 

• Data Sharing: Be cautious about the data shared with AI, recognizing that the system may not have answers to all topics and could introduce risks if not properly managed. 

4. Bias and Fairness 

• Bias Identification and Mitigation: AI systems can unintentionally amplify existing biases present in data, leading to unfair outcomes. An AI assessment should include methods for identifying and mitigating these biases, ensuring that the system operates fairly across different demographic groups. 

When Should AI Assessments Be Conducted? 

AI assessments should be integrated into the AI lifecycle, beginning at the earliest stages and continuing through to deployment and beyond. The following are key points in the lifecycle when assessments should be conducted:  

• Pre-Deployment Assessment 

• • Purpose and Scope Evaluation: Before selecting an AI tool, organizations must evaluate its intended purpose and scope, ensuring that it aligns with legal obligations, internal policies, and the organization’s goals. 

• • Future Use Considerations: Given that organizational goals and practices may evolve, it is crucial to consider potential future uses of the AI system during the pre-deployment assessment. 

• Post-Deployment Assessment 

• • Regular Reviews and Updates: Once the AI tool is in use, ongoing assessments should be conducted to ensure that it continues to function as expected and remains aligned with organizational goals. This includes reviewing and updating the pre-deployment assessment, monitoring data inputs and outputs, and checking for any new biases or performance issues. 

• • Privacy and Data Protection Review: If the AI system handles personal information, it is vital to conduct regular privacy and data protection assessments to comply with existing regulations and align with fundamental privacy principles. 

• Situational Assessments 

• • Regulatory Changes: AI systems should be reassessed when regulations or industry standards change, as compliance is a critical component of responsible AI governance. 

• • Internal Changes: Significant changes within the organization, such as expanding into new jurisdictions or altering how the AI tool is used, may necessitate an off-schedule assessment. 

How Often Should AI Assessments Be Conducted?  

The frequency of AI assessments depends on various factors, including the risk level associated with the AI system, its application, and changes in the regulatory environment. The following considerations can help determine the appropriate cadence for assessments: 

• Risk-Based Cadence 

• • Risk to Organization and Individuals: Organizations should tailor the frequency of assessments based on the risk posed by the AI system to the organization and individuals. Higher-risk systems may require more frequent evaluations to mitigate potential harms. 

• • Regulatory Requirements: In some jurisdictions, regulations may mandate periodic assessments of AI systems, particularly for high-risk applications. For example, the EU Artificial Intelligence Act and the Colorado AI Act require regular evaluations to ensure compliance with laws related to data privacy, fairness, and accountability.

• Internal Factors 

• • Organizational Use and Risk Tolerance: The frequency of assessments should also reflect how the AI system is used within the organization and the company’s overall risk tolerance. Organizations with low risk tolerance may opt for more frequent assessments to address potential risks proactively. 

• • Resource Availability: The availability of resources, including personnel and technological tools, should be considered when determining the assessment schedule. It is crucial to balance thoroughness with practicality to ensure assessments are both effective and sustainable. 

• Event-Driven Assessments 

• • Technological Changes: Updates to the AI model or underlying technology may require an off-schedule assessment to evaluate its continued effectiveness and compliance. 

• • Incident Response: In cases of breaches, vulnerabilities, or unexpected outcomes, an immediate assessment is necessary to address the issue and implement corrective actions. 

Stay Ahead of Risk with Proper Assessment 

A comprehensive and structured approach to AI assessments is essential for responsible AI governance. By including key elements such as use cases, resources, data privacy, and bias in assessments, organizations can ensure that their AI systems operate fairly, comply with regulations, and align with strategic goals. Conducting these assessments at appropriate stages in the AI lifecycle and at regular intervals allows organizations to adapt to changes in technology and regulations while maintaining trust and accountability.  

Regular AI assessments are not just a regulatory requirement; they are a proactive measure to safeguard the organization’s reputation, ensure the ethical use of AI, and achieve long-term success. As AI continues to evolve, so too must the strategies for its governance, making assessments a cornerstone of responsible AI management. 

Truyo offers assessments in our AI Governance Platform. To learn more, click here or reach out to hello@truyo.com to receive more information on our AI assessment module.