AI model evaluation: bridging technical metrics and business impact
Written by: Anna Samuelsson
Evaluating AI models goes beyond simplistic performance metrics; it is a nuanced strategic journey that requires everyone who is involved in AI development; data scientists, stakeholders, project leaders and subject matter experts all need to understand that a single accuracy score can be misleading, and that quantifiable error measures is just the first step of the model evaluation process. A comprehensive approach is essential to assess real-world implications, identify potential biases, and appreciate the complex interplay between technical capabilities and business impact.
Successful AI implementation relies on cross-functional collaboration and a shared understanding of performance, business impact and uncertainty. Rather than striving for perfect accuracy, the focus should be on delivering meaningful value while effectively managing risks. By fostering a holistic evaluation framework among all participants in the AI development process, organizations can create intelligent systems that truly align with their strategic objectives.
“AI model evaluation is not just a technical exercise; it’s a strategic process. By understanding the broader context of the model’s outputs, we can ensure our AI systems create meaningful value while aligning with business goals and ethical standards.”
– Anna Samuelsson, AI leader
The complexity of model evaluation
Evaluating AI models is more nuanced than simply looking at a single metric. For instance, consider a fraud detection model that boasts 99% accuracy. At first glance, this seems impressive. However, if only 1% of transactions are fraudulent, the model could achieve this accuracy by simply classifying all transactions as legitimate, missing every actual fraud case. This example underscores the importance of comprehensive evaluation; accuracy alone never tells the whole story.
Beyond accuracy: the importance of comprehensive evaluation
While accuracy is often the go-to metric for evaluating AI models, it is crucial for business leaders, stakeholders and other non-technical roles to understand that a comprehensive evaluation requires a broader perspective. This approach involves asking deeper questions about model performance and its real-world implications.
Key questions for comprehensive evaluation
Error distribution: How often, and under which circumstances, does the model make overly optimistic predictions versus overly pessimistic ones? In forecasting models, are there any seasonal patterns in the error magnitudes? In classification models, what is the balance between false positives and false negatives?
Subgroup disparity: Models with good overall performance may still show inaccuracies in specific data subsets. For instance, a product recommendation engine might produce irrelevant suggestions for certain categories, while recruitment systems could demonstrate strong general performance but underperform for female candidates. Errors concentrated in specific demographic subgroups are especially critical, indicating potential model discrimination.
Error consistency: Is the magnitude of errors relatively consistent, or does the model alternate between highly accurate and significantly inaccurate predictions?
Data representativity: Are all relevant conditions adequately represented in the dataset? For example, sales predictions based solely on pandemic-era data would likely misrepresent normal market conditions. Similarly, medical studies that only include participants who complete all follow-up appointments can produce skewed results. Training data must reflect real-world variability to ensure accurate results.
Benchmark: What is the performance baseline for the problem your AI system aims to address? If you are currently relying on human judgment, what is the accuracy of these decisions? Consider also the time efficiency of human assessments; an AI model that performs comparably or slightly below human accuracy might still provide significant value when factoring in time savings, potentially allowing human resources to be allocated to other critical tasks.
Different types of models have different quantifiable output metrics. However, an AI system’s outcome and business value is not only about crunching numbers, but a subject for cross-functional discussion.
Addressing contextual impact
It is critical to assess the real-world implications of model errors; what are the consequences of different types of errors in your specific use case? For instance:
In a fraud detection system, what are the consequences of incorrectly flagging a legitimate transaction as fraudulent compared to missing an actual fraudulent transaction?
In a demand forecasting model, what are the business impacts of overestimating versus underestimating product demand?
In a medical diagnosis model, what are the implications of misdiagnosing a serious condition versus falsely identifying a healthy patient as ill?
Decisions of accepted error rates, and prioritizing some errors over others, is not a data scientist’s call; it is a strategic stakeholder decision, based on cross functional collaboration.
Quantifying uncertainty
Quantifying AI models’ uncertainty is a critical yet often overlooked aspect. This process involves various techniques, including confidence intervals, prediction intervals, and methods like conformal prediction. These approaches offer several key benefits:
Improved decision-making by providing a range of possible outcomes
Enhanced model selection by comparing the uncertainty of different models
Identification of potential biases or fairness issues across different demographic groups
Uncertainty quantification techniques can produce outputs such as:
"72% certainty that this is a fraudulent transaction"
"95% confidence that this month's sales of this specific product will be between 5,000 and 10,000 units"
These measures provide valuable context for end-users as well as decision-makers, offering insights into the reliability of each AI prediction.
Practical applications of uncertainty measures
Uncertainty measures can be utilized in two primary ways:
Direct presentation to end-users, providing transparency about the AI model's confidence level
Flagging predictions that fall below a certain confidence threshold, triggering human assessment. For example, a system might return a response like: "The prediction of this result is uncertain and therefore requires human assessment."
Tailoring uncertainty thresholds to use cases
The appropriate use of uncertainty measures depends heavily on the specific use case. For instance; medical diagnostics, such as cancer classification, require a very high certainty level, while retail customer segmentation tolerates lower certainty.
Business leaders and SMEs should consider the potential impact and risks associated with incorrect predictions when determining acceptable uncertainty levels for their specific AI applications. By incorporating uncertainty quantification into AI evaluation processes, organizations can develop more robust, reliable, transparent and trustworthy AI systems.
Cross-functional collaboration: the key to meaningful evaluation and effective AI systems
To truly understand the implications of your AI model's performance, it is essential to facilitate cross-functional discussions on accuracy metrics. This team should include project leaders, stakeholders, subject matter experts, data scientists, and in some cases end users.
Together, they should explore critical questions such as:
What are the real-world implications of model errors or inaccuracies?
How do different types of errors (e.g., overestimation vs. underestimation in regression models, or false positives vs false negatives in classification models) impact our end users and business outcomes?
What is the acceptable performance and confidence level in our specific use case?
How do the model's incorrect predictions translate to tangible consequences for our business and customers?
What strategies can we implement to mitigate risks associated with model uncertainties or limitations?
How can we effectively communicate model confidence or uncertainty measures to end-users?
These discussions ensure that technical metrics are interpreted within the context of your business realities, leading to more informed decision-making, better-aligned AI solutions and ultimately maximized business value.
From technical metrics to business outcomes
Remember to align your AI system's development with its ultimate objective, which often extends beyond maximizing technical performance. For instance, if the goal is profit maximization, conduct a comprehensive cost-benefit analysis. This should include:
Quantifying gains from correct predictions
Calculating losses from incorrect predictions
Estimating the value of time saved through automation
By balancing these factors, you can more accurately assess the AI system's true impact on your organization's bottom line.
How is this applied to LLM based systems?
Large Language Models (LLM) based systems has been a hot topic for years and are widely being implemented across a large variety of industries and organizations. This is an interesting topic to highlight when talking about AI model evaluation, as it can be very challenging to evaluate LLMs with quantifiable metrics. It is difficult to assign a relevant numerical value to such systems' performance - for example rating how good an auto generated email answer is on a scale from zero to ten.
Evaluating LLMs with quantifiable metrics can be challenging, as it is difficult to assign a relevant numerical value to such systems' performance - for example rating how good an auto generated email answer is on a scale from zero to ten. While measurable metrics do exist, traditional evaluation methods for LLMs often fall short in providing meaningful insights for business leaders. Instead of fixating on abstract performance measures, it is crucial to align the assessment with the system's intended purpose and end goals. The more important question lies in assessing how well the overall system — comprising the LLM — fulfills its intended purpose. The key to successful implementation is always defining the system’s objective and end goal clearly.
Consider the process of integrating an AI system as analogous to hiring a new employee. When hiring, you ask yourself: What competencies are required? Why do you need this specific expertise? And how will you measure whether this new hire meets the expected objectives? Similarly, when implementing an AI system, it is essential to define the capabilities needed and set measurable success indicators based on the system's intended outcomes.
For instance, if the AI system is designed to write marketing emails with the aim of boosting sales and customer engagement, success could be measured through metrics such as website visits from email links and subsequent purchases. Alternatively, if the AI system serves as customer service personnel responding to customer inquiries, the goal may be to reduce response time while ensuring accurate answers. Key success metrics could include the average response time, whether customers follow up with additional inquiries (indicating unresolved issues), and possibly customer satisfaction feedback.
Ultimately, the relevant success metric is the outcome of the entire system, rather than focusing on the performance of the LLM alone. While LLM performance metrics are important for optimizing the system, they should be viewed as a means to achieve the broader objective — fulfilling the system’s purpose effectively and efficiently.
Evaluation is a team effort and a business leader’s responsibility
AI model evaluation is both numerically measurable and subject to interpretation. While quantitative metrics provide valuable insights, their ultimate purpose is to understand their real-world implications in your specific context. By treating AI evaluation, including how to handle the model’s uncertainty, as a cross-functional, strategic decision making process, you are setting yourself up for successful AI operations. A holistic approach ensures that AI systems are not just accurate, but also aligned with business goals and risk tolerances.
Remember, the goal is not to optimize the accuracy of your model’s output; it is about maximizing your AI system’s outcome - providing tangible business value while minimizing potential negative impacts.
