2023-02
,
Poretschkin, Maximilian
,
Schmitz, Anna
,
Akila, Maram
,
Adilova, Linara
,
Becker, Daniel
,
Cremers, Armin B.
,
Hecker, Dirk
,
Houben, Sebastian
,
Mock, Michael
,
Rosenzweig, Julia
,
Sicking, Joachim
,
Schulz, Elena
,
Voß, Angelika
,
Wrobel, Stefan
,
Loh, Silke
,
Stolberg, Evelyn
,
Tomala, Annette Daria
Artificial Intelligence (AI) has made impressive progress in recent years and represents a a crucial impact on the economy and society. Prominent use cases include applications in medical diagnostics,key technology that has predictive maintenance and, in the future, autonomous driving. However, it is clear that AI and business models based on it can only reach their full potential if AI applications are developed according to high quality standards and are effectively protected against new AI risks. For instance, AI bears the risk of unfair treatment of individuals when processing personal data e.g., to support credit lending or staff recruitment decisions. Serious false predictions resulting from minor disturbances in the input data are another example - for instance, when pedestrians are not detected by an autonomous vehicle due to image noise. The emergence of these new risks is closely linked to the fact that the process for developing AI applications, particularly those based on Machine Learning (ML), strongly differs from that of conventional software. This is because the behavior of AI applications is essentially learned from large volumes of data and is not predetermined by fixed programmed rules.
2022-09-03
,
Schmitz, Anna
,
Akila, Maram
,
Hecker, Dirk
,
Poretschkin, Maximilian
,
Wrobel, Stefan
Artificial intelligence is increasingly penetrating industrial applications as well as areas that affect our daily lives. As a consequence, there is a need for criteria to validate whether the quality of AI applications is sufficient for their intended use. Both in the academic community and societal debate, an agreement has emerged under the term “trustworthiness” as the set of essential quality requirements that should be placed on an AI application. At the same time, the question of how these quality requirements can be operationalized is to a large extent still open. In this paper, we consider trustworthy AI from two perspectives: the product and organizational perspective. For the former, we present an AI-specific risk analysis and outline how verifiable arguments for the trustworthiness of an AI application can be developed. For the second perspective, we explore how an AI management system can be employed to assure the trustworthiness of an organization with respect to its handling of AI. Finally, we argue that in order to achieve AI trustworthiness, coordinated measures from both product and organizational perspectives are required.
2019-07
,
Cremers, Armin B.
,
Englander, Alex
,
Gabriel, Markus
,
Hecker, Dirk
,
Mock, Michael
,
Poretschkin, Maximilian
,
Rosenzweig, Julia
,
Rostalski, Frauke
,
Sicking, Joachim
,
Volmer, Julia
,
Voosholz, Jan
,
Voß, Angelika
,
Wrobel, Stefan
This publication forms a basis for the interdisciplinary development of a certification system for artificial intelligence. In view of the rapid development of artificial intelligence with disruptive and lasting consequences for the economy, society, and everyday life, it highlights the resulting challenges that can be tackled only through interdisciplinary dialog between IT, law, philosophy, and ethics. As a result of this interdisciplinary exchange, it also defines six AI-specific audit areas for trustworthy
use of artificial intelligence. They comprise fairness, transparency, autonomy and control, data protection as well as security and reliability while addressing ethical and legal requirements. The latter are further substantiated with the aim of operationalizability.