The introduction of artificial intelligence (AI) at a company can lead to increased complexity as well as the greater potential

In my blog post, I will be exploring the topic of explainable AI in more detail and demonstrating why this technology will be critical for the acceptance of machine and deep learning applications and to the future as well.

After taking a bird’s eye view of the holistic framework of trustworthy AI in the first blog post of this series, I would now like to delve deeper into the topic. In this blog post, I will take a look at the core requirement in the trustworthy AI field, namely ‘trust’. I will also elaborate on aspects regarding the division of labour between humans and machines.

Artificial intelligence is set to transform our lives. The task of UX design is to strengthen trust in AI-driven software and, above all else, increase our quality of life in the future. The following blog post explores key questions and describes the best methods to find solutions to them.

The trustworthy characteristics of an artificial intelligence (AI) are addressed in the context of Trustworthy AI. The spectrum of requirements for Trustworthy AI ranges from the AI’s design to its functions, all the way to its operation. At this point, you’re justifiably asking yourself: where should we start, and where’s the end? A holistic framework is used to create an overview, which, in turn, can be broken down into small parts. In my blog post, I’ll briefly present such a framework and highlight individual aspects of it.

Sustainability and environmental protection are topics that are now part of our daily lives. The UN’s 17 Sustainable Development Goals have been designated by Germany’s current federal government as a guideline for political action. If we want to truly understand environmental problems, systematically analyse them and take action against them using IT systems, we must draw on our most crucial tool: environmental data. In my blog post, I’ll give you an overview of the possible ways to apply artificial intelligence (AI) to the field of environmental protection.

If we know what our customers will want tomorrow, we can make them the perfect offer today. Unfortunately, this is rarely successful, and products are usually just ‘dumped’ on thousands of people at a time. In my blog post, I will explain how the next-best-action approach can help banks anticipate the needs of customers and the next steps that would be best for them.

Whether it arrives on paper or as a PDF, vast amounts of information reach our banks via the documents they receive (digital or analogue). In order to make this information usable for further processing in digital processes, the relevant information must be read out correctly and passed on to the right place in the process. This blog post is about how we can reliably perform this process and others for our customers using text extraction.

At the beginning of the global pandemic, the switch to mobile working was another wake-up call for many companies to invest more in digitalisation. A complete integration of all company processes into a few central systems often requires very high investments and can rarely be realised in a short amount of time. One possible solution to this situation is robotic process automation (or RPA). We explain what this is all about in our blog post.