Democratizing artificial intelligence (AI) fosters innovation, efficiency, and engagement by expanding access to raw materials, tools, and data required to build AI systems. Tech giants like Microsoft, Amazon, and Google have created no-code AI tools that enable people to integrate AI into their applications without the need to build machine learning models. However, democratizing AI comes with some caution. Even highly advanced AI systems created by qualified engineers may still suffer from bias and be challenging to explain. When non-experts build and operate AI systems without appropriate controls, it can lead to serious errors and discrimination. Therefore, company leaders need to specify what they intend to democratize, who the users will be, and how to maximize the benefits and manage risks.
Technology vendors
Technology vendors must decide which part or parts of the value chain their tool or platform will democratize when democratizing AI. Tools and models grow more sophisticated across a spectrum that results in greater value generation, with data at one end of the spectrum. Data is easy to democratize, and data ingestion into data warehouses and lakes is straightforward. In the next stage, algorithms are relatively easy to democratize and widely accessible on open-source platforms. Storage and computing platforms are more complex, with cloud storage and computing platforms requiring specific training and certification by technology vendors.
Model development is where democratization is currently happening, with automated machine learning (AutoML) platforms and tools making the model development process faster and more accessible by automating the ability to ingest various data formats and run several algorithms on the same data set. However, users must be appropriately trained to avoid building bias into the model, being unable to explain the results, or making wrong decisions. Finally, we are beginning to create a marketplace for data, algorithms, and models. The danger of systemic misuse of models will increase.
Designing an AI System
Designing an AI system requires technical expertise and a firm grasp of data science. Just as you would want to ensure that a surgeon is qualified, trained, and experienced before performing necessary surgery, you should ensure that people with solid technical skills, an understanding of the critical components of an AI system, and a commitment to responsible AI are designing, testing, and maintaining the AI system.
Training is Required
While vendors claim to have democratized data ingestion, data cleansing, and data mining through drag-and-drop tools or democratized complex statistical and computational model development through automated machine learning or data science processes, company leaders must consider who is accessing these tools and models and whether those users have received appropriate training. Proper training and governance are necessary to manage the risks and maximize the benefits of AI democratization. Democratizing AI expands the possibilities of what businesses and governments can achieve and fuels competition, but it should not be taken lightly, as the potential for mistakes and errors can be dangerous and costly.
