How does Open Source Software build AI policy?

By Srikanth
6 Min Read
How an Open Source Software builds AI policy

Firefox, the Apache web server, and Linux, the operating system that powers 86% of smartphones worldwide, are all examples of innovations born of open-source software. In addition, it led to a sense of continuous improvement for tools that can be shared, enhanced, and distributed collaboratively. AI and ML are becoming more prominent in the open-source community today. Is it likely to be as influential as AI and ML? AI policy is shaped by open-source software, but OSS is rarely discussed. Legislators need to consider OSS’s role in AI policy more actively.


What are the benefits of open-source AI for businesses?

As a result of open-source AI, companies have greater freedom in innovating with AI, as well as being able to leverage peers’ ideas for implementing a fresh scope business.

The result of a closed approach is stifled growth in AI applications. The approach doesn’t contribute to solving problems or improving products, nurturing AI talent, or inspiring trust in AI models. A community around open-source AI can accomplish these goals faster, with lower licensing fees and fewer barriers to success.

In addition to helping you develop empathy for your customers, an open-source AI community also offers you outreach to their community. Bringing a sense of community together is a way to balance the giant forces facing communities, small organizations, and even nations.

How does Open Source Software build AI policy?

Let’s explore how the OSS helps in modeling a strong AI policy across industries. 

AI Adoption speeds up with OSS

A reduction in the level of knowledge needed for using AI is how OSS enables and increases AI adoption. A data scientist can greatly benefit from an open-source alternative to equation implementation, as data scientists find it difficult and time-consuming to implement complex equations into code. Building popular open-source software carries prestige, as well as fostering skills and community feedback. The best OSS code (which is faster, more versatile, or better documented) often wins out among multiple versions of the same algorithm. 

AI biases are reduced through OSS

Private companies often operate in competitive markets and have time constraints for their data scientists and machine learning engineers. Developing models and creating products is important to do their jobs, but not necessarily as important as thoroughly examining models to determine bias. Research and journalism have done an admirable job of exposing the dangers of AI bias. Data scientists have a keen interest in developing ethical AI systems and are aware of these concerns. The open-source community is a great resource for data scientists who want to discover and mitigate the risks of machine learning.

AI Tools enhances the science with OSS

Scientists and developers usually work separately to generate better scientific research and tools. In most scientific fields, there is no way for a researcher to produce new knowledge and implement cutting-edge statistical methods simultaneously. OSS has always been valuable to science, regardless of machine learning’s modern revival. There is nothing unusual about whole OSS ecosystems growing around a single scientific endeavor. It should not be overlooked that scientific OSS is not a new phenomenon, nor is it advisable to let it give a false impression that proliferating OSS AI tools were inevitable.

Technology sector competition is helped and hindered by OSS AI

The policy implications of OSS extend to competition as well. It may seem on the surface that open-source code allows more market competition, but that’s not the case. As a result of openly releasing machine learning code, a much greater number of people can make use of it. There are likely to be many industries that benefit from this, and there will be less AI talent needed. While OSS AI tools can prevent some anti-competitive behavior by large technology companies, they are unlikely to stop the continued rise of their influence. With that as evidence, research, ethics, and innovation are all impacted by open-source code. As AI’s source code is open-source, its governance objectives and challenges are certainly linked. AI policy makers can better understand the impact of OSS software in pursuit of just, inclusive development of artificial intelligence by involving more OSS AI developers. Who can ask the real scenario questions like – What does it feel like to have AI software entirely controlled by a corporation, but it’s open-source? What do governments need to do to grow the use of AI? In an OSS-powered world, what role should standards play? 

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