Due to the requirement for high-quality, labeled, and massive training datasets for AI systems, it usually takes months or years to prepare them. Off-the-shelf datasets are an alternative option that provides ready-to-use datasets to jumpstart AI projects without any barrier of time, cost, or niche expertise. While this approach may seem like an easy solution, it has challenges that can undermine the very goals these datasets were intended to achieve. This blog dives into the limitations of off-the-shelf datasets and the comparative factors between custom and off-the-shelf datasets.
The Limitations of Off-the-Shelf Datasets
1. Contextual and Domain-Specific Irrelevance
Off-the-shelf (OTS) datasets are prepared for broad applications to fit diverse use cases. This creates a fundamental mismatch when applied to specialized domains. For instance,
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Healthcare: A dermatology AI trained on general skin condition datasets may struggle with pediatric cases and specific ethnic skin tones that are underrepresented in the training data.
- Manufacturing: A semiconductor defect detection system trained on generic ImageNet data may fail to identify hairline cracks in silicon wafers that are critical for chip reliability but appear as normal surface variations in general object detection datasets.
- Financial Services: A credit card fraud model trained on public transaction datasets from major retailers may overlook fraudulent patterns in small business point-of-sale systems where transaction amounts and merchant categories follow different behavioral patterns.
- Legal: An AI contract analyzer trained on general legal document datasets from LegalBench may fail to identify key indemnification clauses specific to software licensing agreements because the training data of AI model primarily contained real estate and employment contracts.
2. Poor Data Quality
OTS datasets are prone to poor data quality, primarily due to their generic, large-scale, and often static collection processes. These datasets might contain issues, such as:
- The process of preparing OTS dtasets often involves multiple annotators and vague guidelines. This causes inconsistencies across the dataset, such as the same objects may be labeled with different terms, while others have bounding boxes that vary from tightly cropped to including significant background.
- If the metadata is missing from the datasets, it causes contextual errors. For example, image datasets might lack details, such as camera settings, lighting conditions, or geographic data. These are important for developing applications such as computer vision models.
3. Representation Bias
The demographic skews, geographic restrictions, and biases of the source populations usually carry over into off-the-shelf datasets. Here are a few examples:
- An AI model may perform poorly on darker skin tones and non-Western facial features when the facial recognition datasets primarily contain lighter-skinned people from Western nations.
- Genetic variations that are typical in other regions are not included in medical datasets that are primarily gathered from hospitals in North America.
This representation bias in OTS datasets can become a serious threat in instances, such as loan approvals and medical diagnoses, which potentially expose organizations to legal liability and reputational damage.
4. Inflexibility and Limited Customization
OTS datasets are static and therefore are not able to adapt to an evolving landscape, such as changing regulations or business needs. For instance, a general-use image dataset might not be suitable to train a facial recognition AI model built for people of a certain ethnic group. Not only does it have the possibility to generate false responses, but it might also cause bias.
To address this, companies may attempt to add or fine-tune data, which often results in costly, makeshift solutions that introduce irregularities. This frequently results in the dataset being replaced entirely, wasting resources and delaying projects.
5. Intellectual Property and Licensing Risks
The collection process of off-the-shelf datasets doesn’t include documented ownership, but rather complex or ambiguous licensing terms. These datasets frequently breach privacy laws and terms of service, including the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), when they are created using the web scraping technique. Furthermore, the preparation of datasets from multiple sources adds complexity because the license terms from each source may differ and even conflict. This violation leads businesses to be subjected to legal action and restraining orders against their AI models.
Although off-the-shelf datasets are helpful for developing early-stage models, their limitations must be addressed by adding or substituting custom datasets. These are annotated, usually by
data annotation service providers with domain-specific relevance and verified datasets according to business needs.
5. Intellectual Property and Licensing Risks
The collection process of off-the-shelf datasets doesn’t include documented ownership, but rather complex or ambiguous licensing terms. These datasets frequently breach privacy laws and terms of service, including the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), when they are created using the web scraping technique. Furthermore, the preparation of datasets from multiple sources adds complexity because the license terms from each source may differ and even conflict. This violation leads businesses to be subjected to legal action and restraining orders against their AI models.
Although off-the-shelf datasets are helpful for developing early-stage models, their limitations must be addressed by adding or substituting custom datasets. These are annotated, usually by
data annotation service providers with domain-specific relevance and verified datasets according to business needs.
A human-in-the-loop (HITL) approach is followed by
data collection services, which not only ensures the accuracy and domain-specific relevance, especially in niche cases such as medical images, healthcare, or geospatial data.
To Wrap Up
ML model dataset selection must be in line with specific goals. Despite the availability, affordability, and volume of off-the-shelf datasets, their drawbacks may make it more difficult to match them with particular business requirements. Somewhere between OTS and custom datasets is the answer. While these off-the-shelf pre-configured datasets are perfect when general-purpose data is acceptable, budget constraints are there, or time is of the essence, custom datasets are a better option when domain-specific relevance is crucial.
