The golden datasets in AI refer to the purest and highest quality datasets that you can get to train your AI system. Being the highest standard of datasets, golden datasets are often referred to as “ground truth datasets,” and provide a benchmark for the AI systems.
The reason why the term “Golden Datasets” became popular is the AI boom. You see, the accuracy of any AI model is highly dependent on the quality of data. Sure, we have a plethora of data but most of it is unusable and can’t be used to train AI models without cleaning.
From here, organizations have started working on a dataset that is super precise, clean, and can be considered the benchmark for training your models. From here, the golden datasets became a thing.
Why Golden Datasets are so Important for AI?
There are many advantages when it comes to using a golden dataset in AI and ML. The greatest of them all is accuracy and reliability. Good data ensures that it trains high-quality models, meaning they can correctly make predictions and therefore more correct decisions.
That is possible because a golden dataset can minimize errors and biases, leading to results being more reliable. Golden datasets are used for benchmarking the model’s performance. These allow a comparison of different models for better objectivity while evaluating and comparing different algorithms and approaches
A golden dataset can be used as a reference during error analysis. It helps in understanding the kinds of errors a model is making and gives a direction on targeted improvements.
With the development of AI and ML, rules and regulations associated with them also are being redone by governments and other related authorities; a golden dataset is very likely to become a mandate to ensure models and all other deliverables of AI and ML for regulatory compliance.
Basic Characteristics of Golden Datasets
- Accuracy: Data should always be accurate or free from errors. All data entry in the dataset must be sourced or verified from credible sources.
- Consistency: Data should be organized in a way such that the chances of confusing the models because of inconsistencies are kept at bay. Thus, the data should be uniform in structure and format.
- Completeness: The dataset should describe all areas of the problem domain to cover aspects for thorough model training.
- Timeliness: The information should be up to date, reflecting the current status of the domain it stands for. Old information would be partially or false, depending upon the subject.
- Bias-Free: In generating the golden dataset, efforts should be made toward eliminating or at least reducing biases that may skew the model’s predictions.
How to Create a Golden Dataset
It is not an easy task to create a golden dataset. Most of the time, this requires the support and input of subject matter experts (SME).
Because of the difficulties in creating a golden dataset, some AI teams tend to use the support of automation tools that can create a golden dataset for accurate and automated assessment.
In some instances, an auto-generated silver dataset can be used to guide the development and initial retrieval of LLMs.
Here are the primary steps in producing a gold dataset without a generative tool.
Data gathering
Collect data from different, highly reliable sources from various geographies, ethnicities, and demographic groups to ensure diversity, accuracy, and comprehensive representation. Therefore, the collected data can help in the creation of an informative and unbiased dataset.
Cleaning of data
Cleaning all errors, duplicate records, and irrelevant information. Normalise formats, ensuring the results are uniform.
Annotation and labeling
It should be annotated and labeled very carefully. Domain experts should be consulted to ensure that the information is accurate.
Validation
It should be cross-checked from multiple sources for accuracy and reliability.
Maintenance
It should be updated regularly to keep it relevant. Continuous validation and cleaning are necessary to maintain quality.
Challenges of Creating Golden Datasets
When one wants to develop golden datasets, multiple challenges are involved in this process. Here are some of the most crucial challenges one has to go through to develop golden datasets:
- Resource intensive (Icon): Creating a golden dataset is a time-consuming process and requires a large number of resources, including domain expertise and computational power.
- Bias (Icon): The dataset must be unbiased. This requires careful selection and continuous monitoring. For example, if a healthcare organization is building a model that identifies skin cancer from images of skin lesions, it will collect data from hospitals and dermatology clinics. But mostly it would come from the hospitals of cities of developed countries and consequently, the majority of these pictures might be from the white population. As such this would lead to over-representation of white patients in the model and might contribute to under-representation bias toward minorities and a geographical bias. Both the latter will impact the model when trying to make a diagnosis concerning a patient who is not a white person.
- Evolving Domains (Icon): Maintaining the dataset might be a problem in rapidly evolving domains.
- Data privacy (Icon): Personal data usage requires strong measures to respect privacy and adhere to regulations such as GDPR and CCPA. Adherence to these regulations supports the organization/creators’ trust in data subjects and eliminates legal and ethical issues. In addition, strong data privacy practices reduce the probability of breaches and misuse which may lead to serious adverse effects on individuals and organizations.
Resource intensive
Creating a golden dataset is a time-consuming process and requires a large number of resources, including domain expertise and computational power.
Bias
The dataset must be unbiased. This requires careful selection and continuous monitoring. For example, if a healthcare organization is building a model that identifies skin cancer from images of skin lesions, it will collect data from hospitals and dermatology clinics. But mostly it would come from the hospitals of cities of developed countries and consequently, the majority of these pictures might be from the white population. As such this would lead to over-representation of white patients in the model and might contribute to under-representation bias toward minorities and a geographical bias. Both the latter will impact the model when trying to make a diagnosis concerning a patient who is not a white person.
Evolving Domains
Maintaining the dataset might be a problem in rapidly evolving domains.
Data privacy
Personal data usage requires strong measures to respect privacy and adhere to regulations such as GDPR and CCPA. Adherence to these regulations supports the organization/creators' trust in data subjects and eliminates legal and ethical issues. In addition, strong data privacy practices reduce the probability of breaches and misuse which may lead to serious adverse effects on individuals and organizations.
How Shaip can Help you Develop Golden Datasets?
When you have a problem, going to the subject expert is the most efficient decision you can ever make and when it comes to data, Shaip is the subject expert.
Shaip can provide you with datasets from various domains, including healthcare, speech, and computer vision which is crucial for creating golden datasets. These datasets are ethically collected and annotated so you won’t get into any privacy or legal trouble.
As mentioned earlier, to build you need to have an expert and we can provide you with expert guidance which will help you through the entire process of developing golden datasets and ensure that these datasets are compliant with industry standards and regulations.
