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Related Experiment Video

Updated: Jan 20, 2026

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

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Published on: June 29, 2021

Visual Interaction with Deep Learning Models through Collaborative Semantic Inference.

Sebastian Gehrmann, Hendrik Strobelt, Robert Kruger

IEEE Transactions on Visualization and Computer Graphics

|August 20, 2019

Summary

This summary is machine-generated.

This study introduces Collaborative Semantic Inference (CSI), a framework for human-AI collaboration. CSI enhances deep learning transparency, allowing users to understand and control AI decision-making processes.

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Area of Science:

Artificial Intelligence
Human-Computer Interaction
Machine Learning

Background:

Automation in decision-making poses risks when human agency is diminished.
Current deep learning models often function as 'black boxes,' lacking explainable reasoning.
Effective human-AI interaction requires integrating interaction design with model structure.

Purpose of the Study:

To propose a framework for co-designing interactions and models in deep learning systems.
To enable visual collaboration between humans and algorithms.
To enhance transparency and user control in AI decision processes.

Main Methods:

Development of a framework for Collaborative Semantic Inference (CSI).
Exposing intermediate reasoning processes of deep learning models.
Integrating interaction design principles with model architecture.

Main Results:

Demonstrated the feasibility of CSI through a co-designed document summarization system.
Enabled users to understand and control aspects of the model's reasoning.
Facilitated semantic interactions using visual metaphors.

Conclusions:

Collaborative Semantic Inference (CSI) offers a viable approach to address the 'black box' problem in deep learning.
Co-designing interactions and models is crucial for trustworthy and controllable AI systems.
CSI promotes a more collaborative and understandable relationship between humans and AI.