What Is AI Quality?
AI quality refers to the evaluation of AI systems, taking into account their functionality, performance, operational characteristics, and data quality. AI quality should check all relevant aspects of an AI system to ensure that it is robust, fair, reliable, and beneficial. Organizations developing AI systems must evaluate AI quality to ensure their products are useful, effective, and safe for use.
However, the concept of AI quality is not only significant for AI developers. It is crucial for anyone who uses AI systems. AI quality information empowers users to make informed decisions and ensure that AI systems are beneficial, and can also help them compare and evaluate AI systems before using them.
This is part of a series about Explainable AI.
4 Dimensions of AI Quality
Model Performance
Model performance is the measure of an AI system's ability to produce accurate and reliable results, or in other words, how good the model is at its stated purpose. The better an AI model performs, the higher its quality.
However, high performance is not just a measure of how the model performs at a specific point in time. It’s important for an AI model to be robust, which means it should be able to handle a variety of situations, and perform well over a long period of time.
Societal Impact
The societal impact of an AI system refers to the effects it has on individuals and society as a whole. This could be in terms of job displacement, privacy concerns, or even social biases. A high-quality AI system should have a positive societal impact, or at the very least, mitigate any negative impacts.
This is a complex area to measure as societal impacts can be subjective and can vary from one individual or group to another. Nevertheless, it is an essential aspect of AI quality as it ensures that AI systems are developed and used responsibly.
Operational Compatibility
Operational compatibility refers to how well an AI system integrates with existing systems and processes and its environment. A high-quality AI system should be easy to implement and use, without causing significant disruptions, and should work well as part of existing organizations and processes.
This category also includes factors like scalability and maintainability. A scalable AI system can handle increased workloads efficiently, while a maintainable system is easy to update and improve over time.
Data Quality
Data quality is a critical aspect of AI quality, and directly influences the other categories. The quality of data used to train an AI model affects its performance, societal impact, and operational compatibility.
High-quality data should be accurate, consistent, and relevant. Furthermore, it should aim to minimize biases that could lead to unfair or discriminatory outcomes. Ensuring data quality is a continuous process, and models may have to be frequently retrained on new data or previous data that has been cleaned or improved.
Challenges in Achieving High-Quality AI
Achieving high-quality AI involves overcoming numerous challenges. Here are some of the significant challenges:
Data Biases
Data biases can stem from the data collection process, during which certain groups may be overrepresented or underrepresented (a concept known as class imbalance). Biases in data can lead to unfair outcomes, such as discriminatory hiring practices, biased financial decisions, or even life threatening outcomes, as in the fields of autonomous vehicles and medical diagnosis. Overcoming data biases requires a concerted effort to collect diverse and representative data.
Overfitting and Underfitting
Overfitting and underfitting are common problems in machine learning models, which are a core element in AI systems. Overfitting occurs when a model is too complex and performs well on training data but poorly on new or unseen data. Underfitting occurs when a model is too simple and fails to capture the complexity of the data.
Both overfitting and underfitting can lead to poor model performance and, consequently, low AI quality. To overcome these problems, it's important to strike a balance between model complexity and simplicity.
Explainability and Transparency
Explainability and transparency are crucial for AI quality, but they are also challenging to achieve. Explainability refers to the ability to understand how an AI model makes decisions, while transparency refers to the openness and accountability of AI systems.
Explainability and transparency not only increase trust in AI systems but also help in identifying and rectifying errors or biases. However, achieving them can be difficult, especially with complex models like deep neural networks, which are often described as "black boxes."
Evolving Standards and Compliance
Standards and compliance in the field of AI are still evolving. These standards and regulations aim to ensure that AI systems are safe, ethical, and beneficial. However, due to the rapid pace of AI development, these standards often struggle to keep up. Furthermore, there is a lack of global consensus on what these standards should be, leading to disparities and inconsistencies.
Related content: Read our guide to trustworthy AI
7 Examples of AI Performance Evaluation Metrics
Of the four categories of AI quality we reviewed above, only one has widely accepted metrics—model performance. Here are a few commonly used metrics for evaluating the performance of AI models.
1. Classification Accuracy
Classification accuracy is one of the most straightforward and commonly used evaluation metrics in AI. It measures the proportion of correct predictions made by the model against the total number of input samples. It is mostly used in binary or multiclass classification problems. However, it might not be the best measure in cases where the data is imbalanced, meaning there are unequal instances of each class.
2. Logarithmic Loss
Logarithmic Loss, also known as log loss, is a performance metric that considers the uncertainty of your prediction based on how much it varies from the actual label. This provides a more nuanced view into the performance of a model. It penalizes both types of errors, but especially those predictions that are confidently wrong.
Log loss is a 'loss function', meaning something we want to minimize, not maximize. The closer the log loss value is to 0, the higher the accuracy of the classifier under test. Log loss takes into account both the probability of the predicted class and the actual label.
3. Confusion Matrix
A confusion matrix is a performance measurement for machine learning classification problems. It is a table with four different combinations of predicted and actual values. These are True Positive, False Positive, False Negative, and True Negative. This matrix not only gives the accuracy of a model, but also shows the ways in which the model is getting things wrong.
A confusion matrix provides a matrix as output and describes the complete performance of the model. Each row of the matrix represents the instances of an actual class and each column represents the instances of a predicted class.
4. Area Under Curve
The Area Under the Curve (AUC) is an important metric for binary classification problems. It is a way of graphically representing the performance of a classification model. The higher the AUC, the better the model is at distinguishing between classes. The optimal value for AUC is 1.
The AUC-ROC curve is a performance measurement for classification problems at various thresholds settings. ROC is a probability curve and AUC represents degree or measure of separability. An excellent model has AUC near to 1, which means it has a good measure of separability.
5. F1 Score
The F1 score is a balanced measure for a test's accuracy. It is calculated from the precision and recall of the test, where precision is the number of true positive results divided by the number of all positive results, and recall is the number of true positive results divided by the number of positive results that should have been returned.
F1 score tries to find the balance between precision and recall. It is best if its value is close to 1 and worst if it's near 0. The F1 score is a useful metric when the dataset contains imbalanced classes.
6. Mean Absolute Error
Mean Absolute Error (MAE) is a measure of errors between paired observations expressing the same phenomenon. It is a popular metric for regression problems. It is the average of the absolute difference between the predicted and actual values. It gives an idea of how wrong the predictions were.
The smaller the MAE, the better the performance of the model. The MAE is a linear score which means that all the individual differences are weighted equally in the average. In other words, it is not very sensitive to outliers.
7. Mean Squared Error
Mean Squared Error (MSE) is another metric used for regression problems. It is more popular than Mean Absolute Error because the focus is geared more towards large errors. This is due to the squared term exponentially increasing larger errors in comparison to smaller ones.
MSE is calculated by taking the average of the squared differences between the predicted and actual values. It is a risk function, corresponding to the expected value of the squared error loss. It is always non-negative, and values closer to zero are better.
Processes for Managing AI Quality
Here are a few ways organizations can take control over AI quality and improve the quality of their AI systems.
Define Objectives and Stakeholders
In an AI system, stakeholders could include customers, employees, partners, regulators, and even society as a whole. It's crucial to have a clear understanding of what the AI system is intended to achieve and who will be impacted by its implementation.
The objectives of the AI system could range from improving customer service to automating repetitive tasks or predicting future trends. It's also important to consider the ethical implications of the AI system, as this could significantly impact its acceptance and adoption by stakeholders.
Assess Data Quality
Data is the fuel that powers AI systems. Poor quality data can lead to inaccurate predictions and decisions, undermining the credibility of the AI system and leading to unfair outcomes.
Assessing data quality involves checking for accuracy, completeness, consistency, timeliness, and relevance. It's also important to consider the fairness and bias in the data, as this can significantly impact the outputs of the AI system. Data privacy and security are also crucial considerations, given the sensitive nature of some of the data used in AI systems.
Feature Engineering
Feature engineering is a critical step in preparing the data for use in AI models. It involves selecting, modifying, or creating features (variables) from raw data to improve model performance. Effective feature engineering can lead to more accurate, efficient, and robust AI models.
This process often requires domain expertise to identify the most relevant features for a particular problem. Techniques such as normalization, transformation, and encoding are used to make the data more suitable for machine learning algorithms. Identifying and removing irrelevant or redundant features can also improve model performance and reduce computational costs. Feature engineering is an iterative process, often involving experimentation to find the best combination of features for a given model.
Models Training
Once the features have been extracted, the next step in managing AI quality is to train the AI models. This involves using a portion of the data to teach the AI system how to make predictions or decisions. The model training process is crucial for the ultimate performance and reliability of the AI system.
Training AI models involves selecting an appropriate algorithm, setting the hyperparameters, and fitting the model to the data. It's important to monitor the training process to avoid overfitting or underfitting, which can lead to poor performance on unseen data.
Evaluating the performance of the trained models on a separate validation set can help to ensure that the models are generalizing well and not just memorizing the training data. This is a crucial aspect of managing AI quality, as it ensures that the AI system can perform well in real-world scenarios.
Evaluate and Refine Models
After the models have been trained, the next step in managing AI quality is to evaluate and refine the models. This involves testing the models on unseen data to assess their performance and making adjustments as necessary.
Evaluating models involves measuring their accuracy, precision, recall, F1 score, and other relevant metrics. It's also important to assess the fairness and bias of the models, as this can significantly impact their acceptance and adoption by stakeholders.
Refining models could involve tuning the hyperparameters, retraining the models with more data or different features, or even trying different algorithms.
Select Model, Review, and Deploy
Once the models have been evaluated and refined, the next step in managing AI quality is to select the best model, review it, and deploy it. The best model is typically the one that performs the best on the validation set, although other factors such as complexity, interpretability, and computational cost may also be considered.
Reviewing the model involves checking for any potential issues or concerns, such as overfitting, bias, or privacy violations. It's also a good idea to get feedback from stakeholders at this stage, as they may have valuable insights or concerns that need to be addressed.
Deploying the model involves integrating it into the existing systems and processes, and monitoring its performance in the real world. This ensures that the AI system is delivering value and meeting its intended objectives.
Retrain and Fine Tune
AI systems are not static. They need to be continually updated and refined to keep up with changing conditions and requirements. This involves retraining the models with new data, fine-tuning the hyperparameters, and even re-evaluating the features or algorithms used.
Retraining and fine-tuning the models is a crucial part of managing AI quality, as it ensures that the AI system remains accurate, reliable, and efficient. This requires a continuous feedback loop, where the performance of the AI system is regularly monitored, and adjustments are made as necessary.
Testing and Evaluating ML Models with Kolena
We built Kolena to make robust and systematic ML testing easy and accessible for all organizations. With Kolena, machine learning engineers and data scientists can uncover hidden machine learning model behaviors, easily identify gaps in the test data coverage, and truly learn where and why a model is underperforming, all in minutes not weeks. Kolena’s AI / ML model testing and validation solution helps developers build safe, reliable, and fair systems by allowing companies to instantly stitch together razor-sharp test cases from their data sets, enabling them to scrutinize AI/ML models in the precise scenarios those models will be unleashed upon the real world. Kolena platform transforms the current nature of AI development from experimental into an engineering discipline that can be trusted and automated.
Among its many capabilities, Kolena also helps with feature importance evaluation, and allows auto-tagging features. It can also display the distribution of various features in your datasets.
Reach out to us to learn how the Kolena platform can help build a culture of AI quality for your team.
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