Interactive Machine Learning

Interactive Machine Learning (IML) is the design and implementation of algorithms and intelligent user interface (IUI) frameworks that facilitate machine learning (ML) with the help of human interaction.

With the convergence of Artificial intelligence and Machine Learning, IML is where the  Human-Computer Interaction (HCI) and IUI community meets the ML community. We also combine contributions from related fields such as data science, cognitive science, computer graphics, design or the arts, and natural language processing, data mining/data analytics, and knowledge representation and reasoning. Our focus is to improve the interaction between humans and machines to update ML models, by leveraging both state-of-the-art HCI and IUI approaches, as well as solutions that involve state-of-the-art ML techniques.

We can “assist” AI systems in becoming self-sustaining, “lifelong” learners. This includes  to (1) develop insights into the importance of the social and cultural contexts of machine learning; (2) create ML systems that actively seek information; (3) realise the need to pay attention to the incomplete context understandings and naive generalisations that ML systems, in particular end-to-end systems, bring with them to a given subject. We focus on multimodal multisensor interfaces for ML.


Mixed initiative interaction:
The ML system and the domain expert engage in a two-way dialogue to facilitate more accurate learning from less data compared to the classical approach of passively observing labeled data.

  • Crowdsourcing
  • Incremental learning
  • Integral / One-shot learning
  • Multimodal Multisensor Interfaces
  • Online learning
  • Speech-based ML
  • Textual entailment for ML
  • Argumentative active learning (from textual explanation feedback)

IML tools for enhancing human cognitive capabilities:

  • Help understand and foresee long-term implications of ML
  • Empower humans to make important decisions in a more informed way

Transparency and human interpretability in ML:

  • Mitigate bias in ML
  • Evaluate machine learning models
  • Render ML models more interpretable
  • Interpretation of black-box models (including deep neural networks)
  • Enhance trust in ML
  • Causality of predictive models
  • Visual analytics

Explainable artificial intelligence applications:

  • Medical decision making
  • Reliance on learned models in deployed applications
  • Legal requirements

General AI for ML:

  • Metalevel verification and validation
  • Metacognition and introspection
  • Super-human AI Reasoning
  • Reinforcement learning
  • Active Learning
  • Subgroups


Ongoing Research Projects


Funding period: 01/2023 – 06/2026 Many industries are transitioning to Industry 4.0 production models by adopting robots in their processes. In parallel, Extended Reality (XR) technologies have reached sufficient maturity to enter the industrial applications Read more…


Funding period 03/2021 – 03/2024 Ophthalmo-AI focuses on the two most common causes of blindness in people aged 50 and older: Age-related macula degeneration (AMD) and diabetic retinopathy (DR). An intelligent assistance system is under Read more…

The pAItient Project 

Funding Period: 10/2020 – 9/2023  To broaden the use of AI methods in medicine, networked, digital patient data is required, for example for use in disease diagnosis, personalized treatment, and faster drug discovery. Our project Read more…

Digital Pens in Education

Digital pen signals were shown to be predictive for cognitive states, cognitive load and emotion in educational settings. We investigate whether low-level pen-based features can predict the difficulty of tasks in a cognitive test and Read more…

Interactive Machine Learning Lab

German Research Center for Artificial Intelligence (DFKI)


Daniel Sonntag (DFKI, project lead)

Fax: +49(0)681-85775-5341


Deutsches Forschungszentrum für
Künstliche Intelligenz GmbH (DFKI)
Standort ​Niedersachsen
Interaktives Maschinelles Lernen
Marie-​Curie-Straße 1, 26129 Oldenburg, Germany

Deutsches Forschungszentrum für
Künstliche Intelligenz GmbH (DFKI)
Interaktives Maschinelles Lernen
Stuhlsatzenhausweg 3
Saarland Informatics Campus, Geb. D3 2
66123 Saarbrücken, Germany