Organizations are ramping up their use of AI to make business processes more efficient. It doesn’t look like they are slowing down anytime soon. Adoption is high, and the same goes for the advancement of the AI models powering this technology.
The ISO/IEC 42005:2025 standard provides guidance for businesses to evaluate how their use of AI could affect individuals and society at large. This article provides a human-friendly guide on how to conduct an AI Impact Assessment consistent with the ISO/IEC 42005:2025.
What’s an AI Impact Assessment?
You’ve just built a virtual assistant. It is an AI system that is designed to help people make the right decisions quickly, analyze data and maybe even talk like a human being during the entire process. It’s sleek, smart and holds a lot of potential, but before you release it to the world, your compliance team shows you the red light.
“What if it unintentionally favors some groups, misuses the data provided to it, or causes unintended harm that no one was expecting?”
That’s where the AI impact assessment comes in. It ensures that the probability of your AI virtual assistant pulling an “iRobot” is significantly low.
An AI impact assessment is a well-thought out process that helps organizations ascertain how their deployed AI systems might affect society, people (intended users) and the environment. It looks at both the good and the bad, the risks and benefits. If this assessment is done properly, it helps enterprises to identify issues with the systems early, stay legally and ethically compliant, and build trust with their users.
What’s the ISO/IEC 42005:2025?
ISO/IEC 42005:2025 is a standard that provides industry guidance for enterprises to perform AI impact assessments, with major focus on how these systems could pose a risk or give benefits to individuals and society. The standard recommends that AI impact assessments should be integrated into existing AI risk management and organizational processes, rather than treating them as standalone tasks.
Now, let’s demystify the process of carrying out this assessment.
A Guide to Conducting an AI Impact Assessment
1. Define the Scope and Context
You should begin by clearly describing the AI system in terms of what it is, what it does, its intended purpose, and the specific stage of development or deployment it is in. From there, you need to identify all the relevant stakeholders, including individuals, communities or groups that will be impacted directly or indirectly by the use of the intended system. It’s also important to look beyond the intended uses and anticipate possible unintended applications or potential misuse, as these can introduce significant risks if they are left unchecked.
2. Integrate with Organizational Processes
It is important to ensure consistency and avoid duplication. Consequently, the AI impact assessment should be integrated with the organization’s existing risk management frameworks covering areas such as data privacy, human rights, etc. Doing this creates a much better understanding of risks and ensures that AI-related considerations are a part of the organization’s wider decision-making ecosystem.
3. Establish Timing and Triggers
Timing refers to when an impact assessment should be carried out. Triggers refer to the events that should take place first, warranting an immediate impact assessment.
Impact assessments should be planned to happen at different stages throughout the AI system’s lifecycle. The lifecycle includes the design, development, deployment and post-deployment phases so that risks can be identified and addressed immediately.
Additionally, organizations must define specific triggers for an impact reassessment, such as when changes are made to the system’s structure/functionality or a shift in the context in which it is used.
4. Allocate Responsibilities
In order to carry out a thorough and properly accountable impact assessment, responsibilities must be clearly assigned to individuals or teams that are tasked with conducting the evaluations, reviewing the results and implementing the recommended remedial measures. It is also important to bring in a mix of expertise across different domains including technical, legal and compliance domains so that the assessment is comprehensive and captures risks and benefits from different points of view.
5. Conduct the Assessment
The impact assessment should be a thorough analysis of the AI system’s potential effects, both positive and negative, on identified stakeholders, with high levels of attention paid to fairness, transparency and accountability. This exercise should also assess the quality and bias of input data, in addition to evaluating how resilient and trustworthy the underlying model is. It is important to factor in scenarios in which the system could fail or be misused, along with the consequences of such events.
6. Document Findings
Every stage of the impact assessment, from processes followed to decisions made and mitigation strategies applied, should be properly documented to provide a clear and traceable record. This document should be accessible by the relevant internal and external stakeholders for accountability and trust purposes.
7. Implement Monitoring and Review Mechanisms
After an impact assessment has been done and the AI system is deployed, it should be subject to continuous monitoring to track its real-time performance and the sprouting of any unconsidered risks. Regular reviews of the impact assessment should be scheduled to ensure that it remains up to date as the system changes over time.
We have now seen how to carry out AI impact assessments. Let’s now explore some best practices to consider while carrying out this kind of assessment.
Best Practices for Effective AI Impact Assessments
- Integrate Early and Throughout: Include impact assessments from day one of the AI system being development and maintain them throughout the lifecycle until the system is retired.
- Avoid Overload: Focus on meaningful assessments that will inform decision-making, rather than creating excessive documentation.
- Customize to Organizational Needs: Streamline the assessment process to fit the specific context, scope and requirements of your organization.
- Encourage a Culture of Responsibility: Encourage a culture in which ethical considerations and stakeholder inputs are important to AI system development and deployment.