Strategies for Creating a Visionary Organisational Culture

whitePaper | March 22, 2023

Artificial intelligence is increasingly becoming a part of our everyday lives, whether at home, in industry or in the public sector. The technology poses risks, but also opens up new opportunities. This presents the institutions in Brussels with the challenge of finding a balance between innovation and regulation for AI in the EU.

whitePaper | July 20, 2022

The growing diversity of actors and circumstances involved in AI/ML deployment makes establishing a set of ethical participatory practices especially difficult for this field. Even among practitioners working at the same organization, there can be substantial differences in both their knowledge about how AI/ML systems were created or will be used and their ability to incorporate inclusive practices. Additionally, the greater availability of AI development platforms, including “no-code” platforms, means that algorithmic models can be deployed without having deep expertise, expanding the number of circumstances automated systems are deployed in. This means many more instances where automated systems are being deployed without consideration of how the algorithms were developed, the provenance of the datasets and nature of the bias on which they were trained and tested, and the ethical implications of their development and deployment.

whitePaper | January 5, 2020

Machine learning (ML) is an application of artificial intelligence (AI) that allows systems to automatically learn and improve from exposure to more data without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to learn for themselves.1 While AI represents the broader concept of machines being able to carry out tasks in an intelligent way, machine learning is a current application of AI based on the idea that we can give machines access to data, and they can use that data to learn for themselves.

whitePaper | August 14, 2020

As consumers and customers, we are accustomed to easy, intuitive and superfast transactions driven by data. Therefore, businesses have the various challenges of managing and mining the data they produce and use, but also ensuring that their digital experience is 100% reliable for their customers — so the chain of trust is never broken.

whitePaper | April 16, 2020

The Iron Triangle or, as it is more commonly known, the project management triangle has influenced project teams for years. Summed up as “fast, good, or cheap.pick two,” the concept asserts that there are firm boundaries on what is possible to produce. The triangle metaphor implies there is no way to provide goods or services that are simultaneously on budget, on time, and of high quality because of the competing nature of these priorities.

whitePaper | March 30, 2022

Artificial Intelligence is one of the fastest growing technologies of the 21st century and accompanies us in our daily lives when interacting with technical applications. However, reliance on such technical systems is crucial for their widespread applicability and acceptance. The societal tools to express reliance are usually formalized by lawful regulations, i.e., standards, norms, accreditations, and certificates. Therefore, the TÜV AUSTRIA Group in cooperation with the Institute for Machine Learning at the Johannes Kepler University Linz, proposes a certification process and an audit catalog for Machine Learning applications.