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What is Data Validation and Why Does It Matter for Al?
Put simply, data validation refers to the practice of checking the accuracy and quality of any source data you plan to use. While this seems quite straightforward, many people find themselves confused about how it applies to AI.
The first thing most people think of when they hear “artificial intelligence” or “AI” is one of the Large Language Models (such as ChatGPT) that are frequently mentioned in the news. If those models are trained on “data from the entire internet”, as is commonly claimed, how does Data Validation even come into play? Well, most LLMs require additional specific training for businesses to take full advantage of their features. That involves introducing firm-specific and industry-specific input data to the models. That data needs to be validated before it is used to train the model. Data validation plays a crucial role in ensuring the quality, relevance, and accuracy of the data used to train tailor-made AI models, helping to filter out misinformation, bias, and inconsistencies from the vast and unregulated data available on the internet.
But those are not the only AI models available – there are many machine learning and AI models that are purpose-built for various tasks or for specific firms. When choosing to deploy one of those models, firms must be cautious about the training data used, checking for accuracy, quality, and bias. For instance, a model trained solely on the profiles of current employees to select ideal job candidates might inadvertently learn and perpetuate existing hiring biases.
How to Pinpoint Data Validation Issues in Al Models
Clearly, the data validation process is vital but checking for “quality” and “accuracy” is a vague task. What kind of data validation issues should modelers be looking for? How does one go about identifying these issues in AI models?
We explore the most common issues in a bit more detail below, but for the most part the answer to this is testing, testing, testing. It’s crucial to employ a variety of testing methodologies, such as cross-validation and holdout testing, to detect issues like overfitting, bias, and data leakage. Robust and effective data validation also involves continuous monitoring and updating of the model as new data becomes available, ensuring that the model’s performance remains consistent over time and adaptable to evolving conditions. Therefore, the most crucial piece in the puzzle to safeguard against data validation issues in AI models are employees with data domain expertise – individuals who understand the data in depth and detail. Firms also benefit from outside expertise – consultants with experience in designing and deploying AI models, who might bring a different perspective to challenging the training data.
Common Al Data Validation Issues
So, let’s discuss the most common data validation issues briefly.
Data Quality and Consistency
This is the most common issue for data being used for modeling or exploration in general. A huge amount of data being collected is being done in an ad hoc manner, with no real design or purpose for the collection. This leads to poor AI data quality that has very little signal within the noise. Such data also often has gaps in data collection or underwent changes in structure without good documentation surrounding those changes. A model is only as good as its data pipeline – good data governance and data management is key.
Biases and Representativeness
Suppose your data is high-quality and complete, with clear documentation. The data still needs to be checked for bias and representativeness, depending on its intended use. Intended use is key: if you’re training a hiring algorithm on resumes from your current employee pool, but your workforce lacks diversity in terms of race or gender, the model may unintentionally replicate this bias by favoring similar candidates, thereby excluding individuals of different races or genders. However, say you are a gaming company trying to appeal to a userbase similar to your current userbase – even if your current userbase is relatively homogenous, it doesn’t matter because you aren’t trying to create a model that understands the desires of all potential customers, only ones similar to the customers you already serve. Representativeness issues can create broader societal or ethical concerns in how different groups are treated or portrayed. Some issues related to representativeness and bias are worth noting:
Data Imbalances
Assuming your model defines different classes, imbalanced data suggests that the representation of samples across those classes is uneven. This can lead to biased and unreliable results – the model may perform poorly for the underrepresented category. Data imbalance could potentially be corrected by changing your sampling methodology or data selection technique or generating synthetic data, but this must be done with extreme caution.
Domain Shift and Concept Drift
Domain shift is when the data the model is trained on does not accurately capture the data the model is deployed on. An example of this would be training your model on advertising campaigns that have been successful on your current customer base and then using it to design an advertising model that is deployed in a different country. Because the target users are different from the training users, the model will return poor results. Concept drift is a similar idea but refers to the inability of models to update in real-time, leading to a disconnect between current information and the information that was used to train the model. This is currently seen in nearly all major LLMs, which warn users what the data cutoff for the training dataset was.
Adversarial Attacks
Adversarial attacks refer to attacks on AI models. Attacks can take many forms, such as data poisoning attacks which seek to contaminate the data used for training models (example: disinformation campaigns on social media). Another adversarial attack is evasion, which finds loopholes to work around the model (example: misspelling words to avoid spam filters).
Rule Completeness and Consistency
When designing the model, the set of rules must be complete and consistent. However, that completeness and consistency must be constructed with an eye to the data being used to train the model – what qualifies as complete and consistent changes according to the dimensions and limitations of the data being used to train the model. For instance, if the data lacks coverage in certain areas (e.g., missing demographic groups or underrepresented conditions), the rules may need to account for these gaps and avoid overfitting to incomplete information. Similarly, the complexity of the rules should match the richness of the data—rules that are too complex for sparse or limited data may lead to overfitting, while overly simplistic rules for complex data may reduce the model’s performance in terms of accuracy and generalization.
Knowledge Base Relevance
It is critical to ensure that the application of your model lines up with the knowledge base used to train it. This is similar to domain shift: if a model is trained on physics press releases but is used to analyze academic papers about French literature, the results will be close to garbage.
Uncertainty and Exceptions
As with any model design, uncertainty and exceptions are an issue with AI models. Low-quality noisy data creates estimates with high uncertainty that can propagate through continuous iterations of the model until the model itself collapses. Similarly, the model must be aware of data exceptions and how to handle such exceptions, otherwise it will integrate those data exceptions into the model estimates and produce biased results.
Dynamic Adaptation to Changing Rules or Conditions
Finally, if your data is expected to change in such a way that would alter the rules or conditions of your model, this must be taken into account in the model design. Models can be designed to dynamically adapt to changing rules or conditions, but this should be done in the early phases, with some vision of how the data might change over time.
Tactics in Resolving Al Data Validation Issues
Planning and testing, ad nauseum. The old saying “An ounce of prevention is worth a pound of cure” applies here: most of the problems listed above can be solved through careful planning and thorough testing before deploying the model. If issues are discovered after the fact, there are various solutions for each of the issues above, which an experienced practitioner can help implement and test.
Methods to Ensure AI Models Have Consistent and Accurate Data
The best method to ensure AI models have consistent and accurate data is to have strong data governance in place. Data governance refers to the ability to ensure high data quality throughout the data lifecycle in your firm. This includes availability, consistency, usability, security, integrity, and compliance. Without strong data governance in place, AI models can be challenging to implement since they are heavily dependent on the data used for training.
Summary
Like any data models, AI models depend on the quality of the data being used for training. Without high-quality data, models are difficult to implement and produce biased or unreliable results. Thus, validating data prior to AI implementation is a critical step. Where data weaknesses are discovered, there are several
potential solutions that are most easily implemented in the model planning stage. Before embarking on a wide-scale AI push, companies should first focus their attention on having strong data governance in place.
In our next blog in this series, we delve deeper into the first data validation issue – Data Quality and Consistency – which forms the bedrock of data integrity in AI models. Stay tuned!