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Machine Learning in Production Engineering: Potentials for Efficiency and Innovation

26. February 2025

Machine learning is transforming production engineering through data-driven optimization, hybrid models and expert knowledge. Innovation, efficiency and interdisciplinary collaboration enable new opportunities for economically sustainable, intelligent manufacturing systems for various industrial applications. ...

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AI and ML applied to Life Sciences

06. November 2024

Explore how AI and machine learning transform life sciences by enhancing transparency in drug discovery and advancing chemical and biological knowledge through interdisciplinary research at the Lamarr Institute....

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Application

Insights from the AAAI XAI4Sci Explainable Machine Learning for Sciences Workshop

02. April 2024

Enabling new discoveries with explainable machine learning – how the sciences engage with explainability is described in our blog entry which summarizes the key insights of the AAAI XAI4Sci workshop. ...

Erklärbarkeit von KI-Systemen für Verbraucher*innen | Explainability of AI systems for consumers

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Explanation good, all good? How consumers can be informed about the deployment and functioning of AI systems

08. November 2023

Explainable AI is primarily aimed at experts. An expert group of the CTAI has determined: Explainable AI - in combination with other measures - can also be helpful for the information of consumers and citizens....

Menschenzentriertes maschinelles Lernen verknüpft menschliche und Computerintelligenz mithilfe von Visual Analytics.|Schematische Darstellung der Rolle von VA in der Kommunikation zwischen Mensch und Computer.|Visuelle Darstellung einer Regel als rechteckiges Symbol (Glyphe) mit mehreren vertikalen Achsen für die im Modell verwendeten Merkmale.|Gruppe ähnlicher Darstellung von Regeln auf der rechten Seite gegenüber einer Darstellung der allgemeineren Vereinigungsregel auf der linken Seite.

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Towards human-centred Machine Learning

25. October 2023

Human-centered machine learning merges human and computer intelligence with visual analytics (VA). This enhances collaboration between experts and machines and promises a deeper understanding and a more effective use of ML models....

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Research

Explainability Methods for Point Clouds II

14. June 2023

Two different approaches to point cloud interpretability are proposed, which elucidate the properties of the model from global and model-internal perspectives, respectively. All toolkits are now available on GitHub....

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(No) opinion on Artificial Intelligence? The public’s view on AI

22. February 2023

Have you had a conversation recently about how Artificial Intelligence is influencing the way we live together in society? The new data dashboard of the Artificial Intelligence Opinion Monitor visualizes how people assess the use of AI....

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Research

An Explainability Method for Point Clouds

04. May 2022

A surrogate model-based explainability approach is proposed for point cloud classifiers together with two different evaluation metrics validating the plausibility of the approach. The toolkit is now available online....

00 Blog Schmidt KI Ethik 3 - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Basics

That wasn’t me! – Who is responsible for AI-supported decisions?

08. December 2021

The use of Artificial Intelligence systems makes it more difficult for us to hold people responsible for wrong decisions. The integration of human actors and explainable AI systems makes it possible to attribute responsibility in AI-supported decision-making processes....