After years of expensive AI development and underwhelming results, the ubiquity of big data and the ready availability of computing power are producing an explosion in AI implementations. As more and more businesses look to tap into the technology’s incredible capabilities, some of these new entrants are trying to get maximum results on a minimal budget, and one of the most common strategies is to train algorithms using free or discounted datasets.
There’s no way around the fact that open source or crowdsourced datasets are indeed cheaper than licensed data from a vendor, and cheap or free data is sometimes all an AI startup can afford. Crowdsourced datasets might even come with some built-in quality assurance features, and they are also more easily scaled, which makes them even more attractive to startups that imagine rapid growth and expansion.
Because open-source datasets are available in the public domain, they facilitate collaborative development between multiple AI teams and they allow for engineers to experiment with any number of iterations, all without a company incurring additional costs. Unfortunately, both open source and crowdsourced datasets also come with some major disadvantages that can quickly negate any potential upfront savings.
The True Cost of Cheap Datasets
They say that you get what you pay for, and the adage is particularly true when it comes to datasets. If you use open source or crowdsourced data as the foundation for your AI model, you can expect to spend a fortune contending with these major disadvantages:
- Reduced accuracy: Free or cheap data suffer in one particular area, and it’s one that has a tendency to sabotage AI development efforts: accuracy. Models developed using open-source data are generally inaccurate because of the quality issues that permeate the data itself. When data is crowdsourced anonymously, workers aren’t accountable for undesirable results, and different techniques and experience levels produce major inconsistencies with the data.
- Increased competition: Everyone can work with open-source data, which means many companies are doing just that. When two competing teams are working with the same exact inputs, they’re likely to end up with the same — or at least strikingly similar — outputs. Without true differentiation, you’ll be competing on a level playing field for every customer, investment dollar, and an ounce of media coverage. That isn’t how you want to operate in an already challenging business landscape.
- Static data: Imagine following a recipe where the quantity and quality of your ingredients were constantly in flux. Many open-source datasets are continuously updated, and while these updates could be valuable additions, they can also threaten the integrity of your project. Working from a private copy of open-source data is a viable option, but it also means you aren’t benefiting from updates and new additions.
- Privacy concerns: Open-source datasets aren’t your responsibility — until you utilize them to train your AI algorithm. It’s possible that the dataset was made public without the proper de-identification of data, meaning you could be violating consumer data protection laws by using it. Utilizing two different sources of this data could also make it possible for the otherwise anonymous data contained in each to be linked, exposing personal information.
Open-source or crowdsourced datasets come with an appealing price tag, but race cars that compete and win at the highest levels aren’t driven off the used-car lot.
When you invest in datasets that are sourced by Shaip, you’re buying the consistency and quality of a fully managed workforce, end-to-end services from sourcing to annotation, and a team of in-house industry experts who can fully grasp the end-use of your model and advise you on how best to achieve your goals. With data that’s curated according to your exacting specifications, we can help your model generate the highest-quality output in fewer iterations, accelerating your success and ultimately saving you money.