TL: DR

Not only do you need a strong artificial intelligence (AI) governance program, but you also need a way to monitor and measure its effectiveness. AI governance metrics can help you understand compliance, performance, and risk so you can identify gaps and improve outcomes.

Introduction

When we think about AI, the major tech companies and AI innovators first come to mind. However, businesses of all sizes are deploying AI across industries these days. 

In a recent survey, 64% of business owners said they believe AI will improve customer relationships and productivity and 60% expect AI to help with revenue growth. From streamlining production, communication, and reporting to bolstering cybersecurity and decision-making, AI is revolutionising business operations.

With wide AI adoption, ensuring AI deployments align with organisational goals, ethical standards and compliance requirements is more important than ever. However, responsible AI use requires strong AI governance to serve as guardrails to minimise bias and optimise benefits.

Organisations need to go beyond just establishing their AI governance framework. They must monitor and measure the effectiveness of these programs to ensure they are working properly. AI metrics provide quantifiable measurements for assessing performance, risks and impact. By defining and tracking the right AI governance metrics, you can ensure you are using AI responsibly.

Key Takeaways

• AI governance metrics are crucial for maintaining oversight, control and accountability over AI applications. 
• Key areas to measure are compliance, system performance and outcomes, risk management, ethical implications, social impact and organisational readiness and adoption. 
• Effective AI metrics should be specific and measurable and balance quantitative and qualitative assessments.

The Importance of Metrics in AI Governance

AI metrics are crucial for maintaining oversight and control over AI applications, including:

• Compliance monitoring
• Performance assessment
• Risk management and mitigation
• Adoption and alignment
• Ethical and social impact

By tracking these key metrics, you can evaluate how well you comply with your AI governance rules and identify areas for improvement. While your AI governance should set the framework for AI use, your metrics will tell you whether you are achieving these goals.

Key Areas to Measure in AI Governance

While the specific metrics you track can vary depending on your AI use and governance frameworks, here are some key areas you will want to monitor and measure.

Compliance and Alignment With Standards

A fundamental aspect of AI governance is compliance with industry and governmental regulations along with internal standards.  AI development and use must align with responsible AI principles and frameworks. 

Common ethical AI frameworks include:

• Institute of Electrical and Electronics Engineers (IEEE) Ethically Aligned Design
• European Union (EU) Ethics Guidelines for Trustworthy AI  
• Organisation for Economic Co-operation and Development (OECD) AI Principles

Organisations should review any new AI deployments and third-party applications to identify potential compliance gaps that need attention.

Performance and Outcomes

It is also important to use metrics to evaluate the efficiency and effectiveness of AI systems in achieving intended goals, such as:

• Model accuracy
• Response times and throughput
• Productivity gains or process improvements

Continuous monitoring keeps AI in alignment.

Risk Management

AI introduces new risks. Metrics should focus on tracking risk data and pinpointing privacy risks, security incidents and operational failures. For example, it should serve to spot and evaluate:

• Breaches and privacy violations
• System downtime and reliability
• Identification and remediation of AI-relates risks

Organisations should employ security by design and privacy by design principles as a priority to keep data safe and secure in AI projects and workflows.

Organisational Adoption and Alignment

If you want to maximise your AI deployments' return on investment (ROI), your team members must fully embrace technology. Metrics that will help you estimate the level of AI adoption are:

• User AI skills and adoption rates
• Maturity level of AI governance
• Resource allocation for governance

Ethical Implications and Social Impact

System design and use can have widespread ethical implications. Without proper AI governance, there can be bias, lack of fairness and discrimination. While companies generally design systems to avoid such issues, model training data can skew results without proper governance.

AI governance metrics should track:

• Algorithm transparency and explainability
• Fairness scores
• Adherence to ethical AI principles

AI using machine learning (ML) and deep learning can also adapt over time with additional data. Accuracy can degrade when new production data differs from training data, such as user inputs and new models. So, constant monitoring and testing are essential for bias mitigation.

Developing Effective AI Governance Metrics

Companies should develop effective AI governance metrics at the same time they create AI governance policies. The two go hand in hand and the metrics businesses measure must reflect the key provisions. 

Here are some steps to help you define the key metrics to track for AI governance.

Identify Key Performance Indicators (KPIs)

Define specific and measurable KPIs that reflect the goals and objectives of your AI governance program. KPIs should align with regulatory requirements, industry standards and organisational priorities.

It helps to include diverse stakeholders in this phase of the process to gather broad viewpoints that take into account operations, ethics and legal concerns.

Ensure Relevance and Reliability

Choose metrics directly relevant to the core functions and objectives of AI governance. You will want to make sure these metrics can reliably measure what they intend to without introducing their own bias or inaccuracies.

Review and update these requirements over time, especially when technology or regulations change.

Balance Quantitative and Qualitative Metrics

You will also want to incorporate quantitative and qualitative data to provide a comprehensive overview of AI governance effectiveness. 

Quantitative metrics produce objective measurements based on numerical data to measure performance against goals. Qualitative metrics offer insight into user and customer experiences, capturing their perceptions. Combining both metrics provides a more holistic understanding of your effectiveness.

Examples of AI Governance Metrics

Some additional examples of AI metrics for governance include:

• Data lineage: Tracking compliance with data origin, flow and processing rules
• Data quality: Measuring the accuracy, relevance and completeness of data
• Compliance with AI ethics guidelines: Monitoring the percentage of projects adhering to established ethical guidelines
• AI system downtime and reliability: Tracking system uptime, response times and failure rates.
• Security incidents: Monitoring the number of breach attempts or data exposure incidents
• Incident response time: Understanding how long it takes to identify, respond and mitigate AI-related incidents
• Stakeholder satisfaction and feedback: Using surveys to assess transparency and accountability of AI systems

Implementing and Monitoring AI Governance Metrics

So, how do you get started with AI governance metrics and AI audits? Here are the key considerations.

Integration Into Governance Frameworks

Effective implementation of AI governance metrics requires seamless integration with existing governance frameworks and operational processes. This integration should involve:

• Aligning metrics with governance policies: Ensure that the defined metrics directly measure and monitor adherence to the organisation's AI governance policies, ethical principles and regulatory requirements.
• Establishing clear roles and responsibilities: Assign specific roles and responsibilities for metric collection, analysis and reporting across relevant teams and stakeholders, such as data scientists, compliance officers, risk managers and executive leadership.
• Developing standardised processes: Create standardised processes for collecting, validating and reporting metric data, ensuring consistency and accuracy across different AI initiatives and business units.
• Fostering cross-functional collaboration: Encourage collaboration between various teams, including data science, IT, legal and business operations, to ensure a comprehensive understanding and effective implementation of AI governance metrics.
• Leveraging existing reporting and monitoring systems: Integrate AI governance metrics into existing reporting and monitoring systems, such as dashboards and performance management tools, to streamline data visualisation and analysis.

Regular Review and Adaptation

You should review AI governance metrics periodically to ensure their continued effectiveness. This process should involve:

• Regular metric evaluation: Conduct evaluations of the defined metrics regularly to assess their relevance and accuracy with evolving AI technologies, regulatory changes and organisational priorities.
• Stakeholder feedback: Solicit feedback from relevant stakeholders, including data scientists, subject matter experts and business leaders, to identify potential gaps or areas for improvement.
• Continuous improvement: Based on the evaluation and feedback, refine and update the AI governance metrics to better reflect your evolving needs and goals.
• Staying updated with industry best practices: Monitor industry trends, emerging AI governance frameworks and best practices to ensure your metrics meet current standards and recommendations.
• Adapting to technological advancements: As AI technologies and methodologies evolve, reevaluate and update the metrics to account for new risks, ethical considerations and potential impacts.

Use of Technology and Tools

Leveraging appropriate technology and tools can greatly enhance the efficiency and effectiveness of collecting, analysing and reporting on AI governance metrics. Some potential solutions include:

• AI governance platforms: Use specialised AI governance platforms or modules that offer integrated metric tracking, reporting and monitoring capabilities. These platforms often provide customisable dashboards and automated calculations.
• Data analytics and visualisation tools: Employ data analytics and visualisation tools to process and present metric data in a clear and actionable manner. These tools can help identify data trends, patterns and anomalies.
• Automated data collection and integration: Implement automated data collection and integration processes to streamline the gathering of metric data from various sources, such as AI models, data pipelines and operational systems.
• Machine learning and AI techniques: Leverage machine learning and AI techniques to analyse large volumes of data, identify patterns and generate insights related to AI governance metrics.
• Workflow automation and incident management: Automate workflows and incident management processes based on thresholds or anomalies — enabling more rapid response to potential issues.

Challenges in Measuring AI Governance

Measuring AI governance is not easy. You must recognise and address all the challenges to ensure effective measurement.

Data Availability and Quality

One of the biggest challenges is the availability and quality of data required. AI systems typically rely on mass amounts of data from diverse sources. Ensuring the completeness, accuracy and relevance of this data can be difficult. Incomplete or inaccurate data can create unreliable or misleading metric results, undermining the effectiveness of the governance program.

Organisations should implement strict data management practices, including data lineage tracking, quality assurance processes and data governance frameworks. Advanced data integration and analysis tools can consolidate and harmonise data from different sources.

Aligning Metrics With Evolving AI Technologies

As new AI techniques and applications emerge, existing metrics may become obsolete or fail to capture the unique risks and considerations associated with evolving technology.

Continuously monitoring industry trends, emerging technologies and best practices in AI governance is essential. You should conduct regular reviews and update metrics to ensure alignment with the latest developments. 

Subjective Bias in Qualitative Assessments

While quantitative metrics provide objective measurements, AI governance programs can learn from qualitative assessments like surveys and feedback. However, subjective biases or personal experiences can influence these qualitative measures — potentially skewing the overall assessment.

Organisations should implement standardised procedures for collecting and evaluating qualitative data to mitigate subjective bias. This may include using validated survey instruments, ensuring a diverse representation of stakeholders, or employing statistical techniques to analyse and interpret qualitative data objectively. 

Complexity and Interdependencies

AI governance includes many components such as compliance, risk management, performance and ethical considerations. These elements are interdependent, making it challenging to isolate and assess the impact of individual metrics.

To address this complexity, you should take a holistic approach to AI governance measurement that considers the relationships and the collective impact. Some organisations leverage causal modelling and simulation to better understand the impact of governance decisions.

Robust AI Governance and Measurement Produce Trust

By establishing well-defined metrics and continuously refining them, you can ensure the responsible development and deployment of AI systems. You can remain compliant with internal and external requirements while fostering trust in the outcomes. And without trust in the data, AI will not have the impact you want.

Taking a data-driven approach to AI governance and leveraging the right KPIs are critical steps in navigating the challenges related to AI use. With the right governance policies and measurements, you can ensure accuracy, compliance and privacy.

Zendata helps integrate robust privacy by design as part of your AI governance program. By combining data context and risk data with how data is used across the entire lifecycle, you can actively mitigate risks.

Contact Zendata to learn more.