Trustworthiness: An even greater challenge for the “no-code” AI models

According to Forbes, 83% of businesses say Artificial Intelligence (AI) is a strategic priority for their businesses. However, there is not enough data science talent and thus not enough use cases of AI. Building AI models, such as training machine learning (ML) models, require time, effort, and experience. “No-code AI” reduces the time to build AI models to minutes enabling companies to easily adopt ML models in their processes.

Although the interest in no-code AI has started to increase, since building custom AI solutions, requires writing code, cleaning and structuring, categorizing, training and debugging the model. These processes take an even longer time for those who are not familiar with data science. Studies claim that low code/no-code solutions have the potential to reduce the development time up by 90%.

AutoML solutions are focused on empowering data scientists to be more efficient. They provide transparency on the whole ML pipeline, which increases complexity but also allows data scientists to refine how models are built. No-code AI solutions are focused on helping non-technical users build ML models without getting into the details of every step in the process of building an ML model. This makes them easy to use but harder to customize. The number of no-code AI platforms and software, that allows people without specialized skills to build algorithms, is proliferating rapidly.

The companies that market no-code ML platforms include Akkio,, DataRobot, Levity, Clarifai, Teachable Machines, Lobe, Peltarion and Veritone, to name a few. They allow non-AI experts to create AI systems using simple visual interfaces or drag-and-drop menus. Some of the software is designed specifically for computer vision, some for natural language processing, and some for both. In the future people would not just want to deploy different models, but potentially thousands of pieces of AI software. They would be able to design and create their own algorithms.

Empowering every employee to build and train AI algorithms will be impossible to assess the trustworthiness of these algorithms in terms of transparency, ethics, data privacy, non-bias, or governance pitfalls. The rise of no-code AI makes it imperative to develop strong auditing tools and policies around the use of AI and have systems in place to ensure everyone using the no-code software understands and audits the applicability of these policies. We need advanced tools to audit how these no-code AI models have been trained.