How to Choose the Right AI Training Data & Why Is It Important?

Guidelines for Selecting the Right AI Training Data

Most companies prefer using secondary data for AI training because it is commercially or publicly available, cost-effective, time efficient, and scalable.

Primary data comes into the picture when secondary data is unavailable, there’s a need to address a gap in secondary data, or the AI model needs dynamic or real-time data. In this case, you don’t need to choose AI training data, you collect it from scratch.

Despite the advantages that come with secondary data, there are limitations including quality inconsistencies and biases.

That’s why you must be extra careful when choosing secondary data from various sources, including previously used datasets, public repositories, or web scraping. Do this!

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1. Verify compatibility with your objectives

Before getting data for AI, you ought to have prepared a model design document (MDD). Some call it an AI project charter or problem statement and scope document.

Either way, an MDD defines the problem, data requirements, model architecture, evaluation criteria, and other related project details.

Compare the objectives in your model design document with those in the dataset creation reports of varied datasets. The dataset creation report details the rationales behind constructing a specific dataset.

So, rather than waste time comparing most of the details in your documentation with those in dataset creation reports, focus on the objectives or problem statement.

If the objectives closely align, proceed with the next step. Else, deem the dataset unsuitable for the project and proceed to evaluate the next dataset.

2. Evaluate alignment with data requirements

Once you’ve narrowed down to a select number of datasets that align closely to your objectives, proceed to compare how well the data requirements align.

From the target variables or features, data type, data volume, data format, to data distribution and diversity needs — compare what’s in your model design document with the details in the dataset creation report.

Hone in on the datasets that align with the data requirements in your model design document.

The data requirements do not have to precisely align with your AI model’s data needs. Target datasets with few data requirement gaps when compared to the MDD to avoid compromising your model’s performance.

Later, you can address the data requirement gaps through techniques, including data augmentation, feature engineering, and dataset combination.

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3. Assess defined data quality and precision needs

So far, you have a group of datasets that align closely with your model’s objectives and data requirements. Now, focus on the datasets’ defined data quality and precision needs.

Analyze each dataset’s creation report and evaluate the accuracy with which the data was collected, cleaned, labeled/annotated, and used. Undertake a fairness audit to assess the strategies used to eliminate biases and errors, including missing data and outliers.

Overall, aim to assess how the initial data collector went about the process of collecting the data and ensuring it served its purpose.

To save time, look through the dataset’s metadata. Well-documented metadata ensures that the collected data is understandable and usable by third-party entities.

4. Examine temporal requirements (data time frame)

Data time frame refers to the period in which a specific dataset remains relevant.

Due to factors such as data aging, seasonal variations, relevance to current conditions, and temporal drift, data from older time frames becomes less accurate or irrelevant progressively. Take time to analyze the relevance of each dataset to your AI model’s needs.

Remember, some AI models work great with real-time data, including fraud detection and stock price prediction models. So, to have an AI model deliver relevant predictions, you must acknowledge the relevancy of time validity.

5. Assess ethical constraints

You should assess the ethical grounds under which each dataset in the selection pool was created. Do this to avoid public backlash or legal issues.

For starters, go through the ownership rights and fair use disclaimer. Avoid using datasets that are not legally owned, licensed, or permitted for AI training. Ensure the data complies with copyright laws and necessary licensing agreements.

Then, proceed to check for other ethical aspects such as biases, transparency and accountability. Avoid datasets that perpetuate biases based on protected attributes such as, disability, age, gender, race, and more.

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Why Is It Important to Choose the Right AI Training Data?

1. The quality of AI training data has direct impact on model performance

Training data defines what your select AI model learns. Therefore, if you provide it with low-quality or incorrect data, it yields unreliable and inaccurate results. Moreover, if you provide biased data, you’ll have discriminatory results.

2. To prevent or avoid ethical and legal issues

Ignoring ethical and legal constraints may harm the intended users or damage the company’s reputation. And, if you are training an AI model to help solve problems in fields like finance or healthcare, you must use accurate and relevant data that complies with industry regulations to avoid legal repercussions.

3. To optimize cost

Use the right AI training data and you won’t have to worry about repeated training iterations and model adjustments.

Clear objectives coupled with the right training data also yields the expected outcome, encouraging stakeholders to support budget allocations to cover scaling.

Closing Words

The path to choosing the right AI training data starts with using your model design document as a data acquisition checklist. With this document, you are in a better position to pinpoint datasets that align with your AI model’s objectives and data requirements.

Other than the model design document, you need the data creation reports of all the datasets you are assessing for suitability. And, by following the five steps outlined in this piece, you should zero in on the dataset with the right data for your model.

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