AI Testing: 7 Proven Ways to Ensure Accuracy and Fairness in AI Systems
Artificial intelligence (AI) has become a powerful tool for business. Across every industry from healthcare and finance to retail and manufacturing, AI systems now play a pivotal role in processes such as:
- Complex decision-making
- Automating customer interactions
- Preventing and detecting fraud
- Optimizing supply chains
- Personalizing marketing and product recommendations
- Enhancing diagnostics and healthcare treatment plans
As AI continues to evolve, it’s important to ensure it remains accurate and fair. Specifically, AI systems should never favor or discriminate against a particular demographic group.
When AI models go wrong or are flawed, they can cause biased results, incorrect predictions, and negative business outcomes.
This blog dives into seven proven methods to keep your AI systems accurate and fair. You’ll also discover how making testing your AI systems a top priority can build trust and prevent errors or bias.
Why is testing AI systems crucial?
AI systems learn from data. But the decisions they make are only as good as the data they’re trained in. If that data is incomplete, biased, or skewed, the AI model will reflect those imperfections in its results. This can cause inaccurate predictions or recommendations that can:
- Harm users
- Perpetuate bias
- Fail to deliver the expected results
Testing is key to avoiding these pitfalls. It ensures your system:
- Performs as expected in real-world scenarios
- Handles edge cases properly
- Operates without unfair bias
Here are the top four reasons why AI software testing is crucial:
1. Prevents bias
AI systems can unintentionally develop biases based on the data they’re trained on. Testing can help identify and eliminate these biases before the system is deployed.
2. Improves accuracy
AI models need continuous validation to ensure their predictions remain accurate over time.
3. Enhances trust
Users are more likely to trust AI systems if they’re known to be reliable, accurate, and fair.
4. Ensures compliance with relevant regulations
Many industries have strict regulatory requirements, especially around privacy and data. AI testing helps ensure compliance.
How to ensure accuracy and fairness in AI systems
Testing AI systems is not just about performance and security. It’s also about ensuring that decisions made by AI are unbiased, accurate, and can be trusted. The following seven strategies can help you create AI models that operate transparently, treat all users equitably, and maintain high levels of accuracy.
1. Test for data bias early and often
What’s the number one culprit behind bias in AI systems? Bad data. If the data used to train the AI is unbalanced or doesn’t reflect the real world, the AI will likely make skewed decisions.
Take facial recognition systems, for example. Amazon’s Rekognition, among others, was found to struggle with identifying darker-skinned faces because it was trained on a dataset skewed toward lighter skin tones. A study by MIT and the University of Toronto revealed that Amazon’s system misidentified darker-skinned women 31% of the time, compared to just 7% for lighter-skinned women. Men, regardless of skin tone, had virtually no misidentification issues.
To avoid this, test for bias in your data right from the start of the AI development process. Use tools that can analyze your dataset and check for underrepresented groups or outliers. It’s also important to update your dataset regularly to reflect the most current and diverse data available.
But there are also cases, where datasets appear biased but actually represent real-world scenarios — think of the many medical conditions that only affect one demographic, gender or ethnicity more than others. These datasets and their real-world biases should be factored in carefully. You need quality engineering approaches to address these situations without introducing unfairness.
In the Qualitest blog below, you can discover how to deal with real-world biases like these in your AI systems.
2. Use fairness-aware algorithms
Another effective way to reduce bias is by using fairness-aware algorithms. These algorithms are designed to balance fairness with performance to help ensure equitable outcomes.
For example, you can use techniques that prevent the AI from favoring one demographic group over another. Fairness constraints can also be built in to make sure the AI model treats everyone equally — regardless of race, gender, age, religion, sexual orientation, or disability.
Testing these fairness-aware algorithms involves:
- Looking at the outputs across different subgroups
- Ensuring there are no significant performance gaps between groups
If your AI system treats one group more favorably than another, you need to adjust your algorithm. You may also have to retrain your AI model with a more balanced dataset.
3. Cross-validate your AI models
Cross-validation is a common technique used in machine learning (ML) models. It can help you test the performance of a model on different data subsets. It divides the data into training and test sets. By rotating these sets, you can ensure that your model:
- Generalizes well
- Isn’t overfitting to a specific dataset
This helps you test the efficiency and accuracy of an AI system when it encounters unseen data.
Cross-validation can also help you understand how the AI model behaves across different data groups. It helps ensure no single group or characteristic disproportionately affects the results.
4. Monitor AI performance over time
AI models can experience “drift”. This means their accuracy and performance degrade as the world and the data they interact with evolve.
For example, an AI system built for financial fraud detection may become ineffective as cybercriminals start using new strategies to bluff the system.
Regularly monitoring the performance of your AI system through A/B testing or by running live tests can help catch drift early. This is crucial for keeping your AI models accurate in dynamic environments. You should also set up feedback loops to quickly adapt the model based on user interactions or changes in the real world.
5. Stress test AI models for edge cases
AI systems often fail to deal with edge cases. These are situations that are not well-represented in the training data.
For example, an AI driving assistant might struggle with scenarios like an unexpected roadblock or unusual weather conditions.
To ensure robustness, stress test your AI system by simulating edge cases. You can introduce noisy, contradictory, or even fabricated data to see how the model responds. Identifying failure points before deployment allows you to build a more resilient AI that handles unexpected situations better.
6. Conduct fairness audits and accountability checks
A fairness audit allows you to review the performance of your AI system across different demographic groups. This involves analyzing how the AI behaves when dealing with various characteristics like:
- Race
- Gender
- Socioeconomic status
- Age
Accountability checks ensure transparency in how decisions are made. They also provide a clear audit trail for every decision made by the AI system.
These audits help find potential biases. They also demonstrate and provide evidence that the AI system works fairly and transparently
Top tip: Have an external third party perform the fairness audit. This brings objectivity and engenders greater trust in the results.
In the below case study, a leading tech innovator wanted Qualitest to capture diverse speech for next-gen NLP model training. The client wanted to keep the video outcomes neutral, without any racial or gender bias. Here’s how we helped:
- Captured 10,000 videos to meet 100% of the capture target.
- Helped the client achieve a whopping 98% of their demographic target and 96% of their quality control goals.
Here are more details:
7. Explainability and interpretability of AI models
For effective AI testing, you need to understand the decision-making process. Explainability tools can show why a model made a certain prediction or decision. This can help you build trust with users and stakeholders.
For example, in healthcare, doctors need to understand why an AI system recommends one treatment over another. If the model’s decision-making process is a “black box,” it becomes difficult to trust or challenge its conclusions.
Incorporating explainability tools during testing helps ensure your model is interpretable. This will allow you to:
- Make informed tweaks to the model
- Identify areas where fairness or accuracy can be improved
Understanding AI decisions also fosters innovation and collaboration, leading to more effective solutions in healthcare and beyond.
In banking and financial services, for example, explainability of AI systems is essential for providing customers with transparency around loan approvals and debt management decisions.
The future of AI testing
As more industries use AI for critical processes, firms simply can’t afford to ignore the importance of rigorous AI testing. Future developments in AI regulation, such as the EU AI Act, may require:
- Mandatory fairness audits
- Bias detection protocols
- Transparency standards
If you decide to invest in thorough AI testing now, you will stay ahead of the curve. It will also make you better equipped to evolve with these changing standards.
These proven testing strategies can help you build powerful and efficient AI systems that are also trustworthy and fair. The combination of accuracy and fairness will ensure AI’s:
- Long-term success
- Widespread adoption across all industries
If you’re looking for a modern AI testing partner, Qualitest can help you derisk your AI systems. We can help you uncover the data issues, errors, and biases that can harm your reputation and business.
Discover more about how our data scientists-in-test can help you increase the accuracy of your AI model.
Conclusion
AI testing is now not just a technical task but an ethical responsibility. You need to ensure the accuracy and fairness of AI systems for long-term success and broader acceptance. By following the above-mentioned seven proven strategies, you can ensure your AI system:
- Performs as expected
- Avoids bias
- Builds trust among users.
Whether you’re developing AI for healthcare, finance, retail, or any other industry, rigorous AI testing is essential.
Act now to stay ahead of the curve. Don’t wait for regulations updates to force you to play catch up. Start testing today to unlock the true potential of your AI while cutting risks.
