3 Practices For Building Reliable AI Models

With the advent of Big Data, data-driven technologies like Artificial Intelligence (AI) flourished. AI and its subsets Machine Learning (ML), Deep Learning (DL), and others are instrumental in creating new products and services. These technologies have continued to improve productivity and drive economic growth. In fact, the market size of Artificial Intelligence (AI) as of 2021 was almost USD$60 billion. And it’ll continue to grow—AI is rapidly becoming among the most prolific job creators of this century.

AI’s potential to impact society is virtually limitless. However, not all AI systems lived up to their expectations. Some, unfortunately, behaved erratically and unexpectedly. Other AI systems developed biases in facial recognition technology, criminal justice algorithms, and online recruiting tools.

That’s why it’s critical to have built-in guard rails when building an AI model to keep it ethical and reliable.

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An Overview Of AI Models

AI models are programs trained using data sets, also called training sets, to recognize and learn from specific patterns. A significant part of training data sets is data labeling, using a data labeling company, for example, or an annotation tool.

AI models can do this through various algorithms created by engineers, computer scientists, and so on. These algorithms help AI models to learn and gain insights from the data. The AI model’s ability to learn from data sets help it solve various problems, often through predictions based on the patterns the model found on the data set.

AI models are used in different fields, with different purposes and varying levels of complexity, like robotics, natural language processing (NLP), and computer vision. Engineers use many data sets to train an AI model. And as this is the age of Big Data, where data is created at an incredible 2,500 petabytes daily, data sets for training AI models aren’t in short supply.

Building A Reliable AI Model

An AI model can degrade over time, however. And sometimes, what works in the lab doesn’t necessarily mean it’ll work in the real world. At least, not consistently. But there are a few recommended practices to ensure reliability degradation is kept at a minimum.

These practices include the following:

1. Using Quality Data

An AI model’s reliability and accuracy depend on the quality of the data from which it was trained. It’s vital to ensure that the data for training an AI model is comprehensive, clean, and valuable. Data sources should be compatible and appropriate with the industry in which the model is being trained. No matter how sophisticated, algorithms won’t be able to carry the project without quality data.

Historical data is critical for an AI model to be accurate and reliable. AI models need something to work on, like previous data on specific instances, to make a reliable prediction or to spot a trend. Again, algorithms alone won’t overcome limited, insufficient data. That’s why data scientists spend about 80% of their time ensuring they have quality data for their AI projects.

2. Using Multiple Algorithms

Using only one algorithm works best if you have many data sets, primarily if the algorithm can efficiently process the data. But data sets from the real world aren’t as straightforward. Unpredictable variables affect your data sets and make everything more complex, so you must adjust.

There may be instances when some features in the data sets you’re using seem to be useless, but removing them could do more harm. In cases like these, trying out multiple algorithms can help you identify which algorithm fits your data sets.

There are several algorithm types, and getting the right one that’ll work for your data set may be challenging. But techniques like cross-validation with different algorithms can help you find the suitable algorithm for your data set.

3. Dealing With Outliers And Missing Values

Handling missing values and outliers may be the simplest way to improve your AI model’s reliability. Outliers refer to values far from the norm or main group. Missing value, on the other hand, represents the value of blank. You’ll often encounter them in large data sets. These two can cause the model’s output to err, skewing the result of an otherwise beautiful statistical model.

Ideally, they are removed, but their removal can occasionally cause the removal of necessary data. Outliers and missing values, after all, still represent facts. Therefore, understanding why they occurred is crucial.

There are several causes for getting outliers in your data set:

  • Deliberate outlier: This type is intentional and is used for testing recognition techniques.
  • Natural outlier: This outlier occurs naturally and may signify sudden data change. The change can be inspected to pinpoint the cause of the outlier’s occurrence.
  • Measurement error: An incorrect input from the tools used to measure data.
  • Data entry error: Simple clerical error; for example, entering 100 instead of 1000.

Missing values, on the other hand, can happen due to:

  • No data stored for some participants or variables
  • Incomplete data entry
  • Equipment error
  • Lost files

Treating outliers and missing values is essential in ensuring your data is clean and well-prepared.


AI models are used in various industries to solve problems and predict trends using data sets. Data sets are used to train AI models; however, AI models invariably become degraded, and their results can become unreliable.

The practices suggested here, like using quality data and multiple algorithms, help ensure a reliable AI model.

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