A World Economic Forum study estimates that by 2020, the digital world will increase to 44 zettabytes of data. That number is continually increasing as more people and devices are connected to the Internet. While some of this data is proprietary, much of it is freely available to users themselves or the broader public.
Open-source data has the potential to drastically influence the development of Machine Learning (ML) and Artificial Intelligence (AI). ML and AI both require significant amounts of data to train; data that can be difficult and time consuming to collect. Open-source data can help minimize these difficulties.
In this article, you’ll learn what is open-source data, and some considerations for using OS data to train machine learning algorithms.
What are Open-Source Datasets?
Open-source datasets, also called open data, are data collections that are freely available for access, use, modification, and sharing. This data is often collected and released by governments, academic institutions, or independent agencies.
Open data is made available based on the idea that some data should be freely available. Freely available data helps ensure equal opportunities and fosters democratic existence. The argument is that if data is collected from the public or is collected using government funds, it should be accessible to all.
Benefits of Open-Source Data
- Can enable a greater understanding of global trends and issues
- Can help even market advantages and reduce monopolies
- Can advance machine learning outside of business and academic worlds
- Can save you significant time and effort that would be dedicated to data collection
- Can enable accountability of public dollars and efforts
Considerations for Machine Learning
There are three main considerations for the use of open-source data in machine learning.
Increased Access
Open-source data combined with open-source tools can enable you to perform data analysis and algorithm training at scale. Cost-effective access to data allows citizen scientists, as well as smaller organizations, to contribute to the field of machine learning when they otherwise could not. This increased access can also provide significant private benefits.
Speed of Innovation
The rate of ML development increases as more individuals and groups contribute to machine learning. More work hours are spent on problems, in a shorter period of time, than could otherwise be achieved. Additionally, a broader range of participants can contribute to a broader range of backgrounds, ideas, and methods. This typically leads to greater innovation.
Security Concerns
The same security concerns that apply to any open-source projects apply to open-source machine learning. Since projects and data are community-driven, it is up to you to ensure tools and data are not vulnerable or infected. It is important to ensure that basic standards of secure coding are upheld for tools you create and methods you use to import data. It is also important that both datasets and tools are scanned before use.
Considerations for Using Open-Source Datasets
Open-source datasets can enable you to refine and train your machine learning algorithms on a scale that might otherwise be impossible. Open data significantly increases the amount of data available to you. It can also help significantly reduce costs by eliminating collection efforts. However, when using open-source data, there are a few considerations you should keep in mind.
Choose Your Sources Carefully
When selecting the data you are going to use, it is important to verify that data is as relevant as possible. Depending on your project, you need to know how data was collected and its reliability. Biases in data create biases in projects. These biases can negatively affect the accuracy and usefulness of your work.
For example, if you are trying to train a translation algorithm on language A but a dataset contains only a specific dialect of A, your training is rendered ineffective. You may think you can translate all of language A, but your translations won’t be accurate for all dialects.
Additionally, just because a dataset is available doesn’t mean it’s open-source. You need to verify that any data you’re using is released under a commons license. If you use data that is not supposed to be used by the public or has a restricted license, your work may be restricted. For example, you may not be able to publish your studies or use the algorithm you trained in a product.
Protect Data Integrity
The issue with open-source datasets which is not present with proprietary data is that anyone can modify sets. Depending on where you retrieve data from, and how, you may be getting a set that has been tampered with. Tampering could include modification of values, processing which degraded data quality, or insertion of malicious code.
It’s important to take datasets from the original source, to reduce the risk of your experiments being affected by modified data. Once the source is identified, you can download the data you wish to use. This can prevent other people’s modifications from having an impact on your work. It can also ensure that your own processing and modifications remain intact.
Lack of Data Standards
Open data is not standardized like the datasets you may be accustomed to. Various data formats can make datasets unusable or less useful. For example, if the data is stored in a proprietary format, you may be unable to process it. This lack of standardized formatting limits the functionality of data and can make it more time consuming to use.
Often, open-source data is stored in poorly identified ways, and it’s hard to find the exact sets you need. Since organizations are not profiting from the release of data, they are less likely to spend resources to improve its accessibility.
As a result, you often need to download and process datasets to determine what sets contain. Processing this data can result in significant wasted work. You may find the dataset you processed aren’t what you were expecting.
Be Aware of Privacy Concerns
Open data can create a grey area when it comes to privacy concerns. You can potentially piece together multiple datasets to reveal confidential information. This more complete dataset can be helpful for more accurate analyses but it presents risks.
If you combine and store datasets, and the data you hold is breached, you could potentially be held liable for breach of private information. To avoid this, make sure to use encryption and implement the same security measures you would for any other private data. Alternatively, you can anonymize combined datasets to eliminate personally identifiable information.
Conclusion
Open-source data has the potential to significantly advance the field of machine learning, provided it’s used carefully. As long as you keep in mind that open data is only as good as those who collect and use it, you can benefit from its availability. Remember that inconsistently or inaccurately collected data is of little benefit.
Hopefully, this article helped you understand what open-source data is and how you can derive the greatest benefit from it. As a next step, consider exploring some of these open-source datasets to get a better idea of what’s available to you.
