Machine learning solutions take a vital place in our lives. It is just not only about performance anymore but in addition about responsibility.

Within the last many years, many AI projects focused on model efficiency and performance. Results are documented in scientific articles, and the best-performing models are deployed in organizations. Now it’s the time to place one other necessary part into our AI systems; responsibility. The algorithms are here to remain and nowadays accessible for everybody with tools like chatGPT, co-pilot, and prompt engineering. Now comes the tougher part which incorporates moral consultations, ensuring careful commissioning, and informing the stakeholders. Together, these practices contribute to a responsible and ethical AI landscape. On this blog post, I’ll describe what responsibility means in AI projects and the best way to include it in projects using 6 practical steps.

Before I deep dive into responsible AI (rAI), let me first outline a few of the necessary steps which might be taken in the sphere of knowledge science. In a previous blog, I wrote about what to learn in Data Science [1], and that data science products can increase revenue, optimize processes, and lower (production) costs. Currently, most of the deployed models are optimized by way of performance, and efficiency. In other words, models must have high accuracy of their predictions and low computational costs. But higher model performance normally comes with the side-effect that model complexity progressively increases too. Some models are was so-called “black box models”. Examples might be present in the sphere of image recognition and text mining where neural networks are trained on lots of of tens of millions of parameters using a selected model architecture. It has develop into difficult and even unknown to grasp why particular decisions are made by such models. One other example is in finance where many core processes readily run on algorithms and decisions are made each day by machines. It’s most significant that such machine-made decisions might be fact-checked and re-evaluated by human hands when required.