On the Ethics of AI-based Algorithmic decision-making in Healthcare.

Co-design of Trustworthy AI. Best Practice: Deep Learning based Skin Lesion Classifiers.

In cooperation with

German Research Center for Artificial Intelligence GmbH (DFKI)

Approach

The team of Dr. Andreas Dengel at the German Research Center for Artificial Intelligence(DFKI) used a well-trained and high performing neural network for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space.

The result of their work is available here: IJCNN_Interpretability (1)

We are working with Andreas Dengel and his team and apply our Z-inspection® process to assess the ethicaltechnical and legal implications of using Deep Learning in this context.

Andreas Dengel

German Research Center for Artificial Intelligence (DFKI) Kaiserslautern, Germany

Together with the DFKI team we completed the first part of the Trustworthy AI co-design for this use case.

Ethically aligned co-design

The main contribution of our work is to show the use of an ethically aligned co-design methodology to ensure a trustworthiness early design of an artificial intelligence (AI) system component for healthcare. The system is aimed to explain the decisions made by deep learning networks when used to analyze images of skin lesions. For that, we use a holistic process, called Z-inspection®, which requires a multidisciplinary team of experts working together with the AI designers and their managers to explore and investigate possible ethical, legal and technical issues that could arise from the future use of the AI system. Our research work is addressing the need for the co-design of trustworthy AI using a holistic approach, rather then using static ethical checklists.  Our results can also serve as guidance for other early-phase AI-similar tool developments.