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Found 67 papers, 10 papers with code
Towards a Problem-Oriented Domain Adaptation Framework for Machine Learning
Domain adaptation is a sub-field of machine learning that involves transferring knowledge from a source domain to perform the same task in the target domain.
Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration
With our work, we contribute to HCI by providing empirical evidence for the negative consequences of incorrect explanations on humans post-collaboration and outlining guidelines for designers of AI.
Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen Datasets
The selection of algorithms is a crucial step in designing AI services for real-world time series classification use cases.
A Multivocal Literature Review on Privacy and Fairness in Federated Learning
We highlight the need to explore the relationship between privacy, fairness, and performance, advocating for the creation of integrated federated learning frameworks.
A Survey of AI Reliance
Artificial intelligence (AI) systems have become an indispensable component of modern technology.
CollaFuse: Collaborative Diffusion Models
In response to these challenges, we introduce a novel approach for distributed collaborative diffusion models inspired by split learning.
Investigating the Role of Explainability and AI Literacy in User Compliance
AI is becoming increasingly common across different domains.
Transferring Domain Knowledge with (X)AI-Based Learning Systems
Thus, we take the first steps to reveal the impact of XAI on human learning and point AI developers to future options to tailor the design of (X)AI-based learning systems.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle
The widespread use of artificial intelligence (AI) systems across various domains is increasingly surfacing issues related to algorithmic fairness, especially in high-stakes scenarios.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Interactive Generation of Laparoscopic Videos with Diffusion Models
Generative AI, in general, and synthetic visual data generation, in specific, hold much promise for benefiting surgical training by providing photorealism to simulation environments.
Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness
The topic of fairness in AI, as debated in the FATE (Fairness, Accountability, Transparency, and Ethics in AI) communities, has sparked meaningful discussions in the past years.
Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence
Artificial intelligence (AI) has the potential to significantly enhance human performance across various domains.
CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AI
Tailored for efficient and collaborative use of denoising diffusion probabilistic models, CollaFuse enables shared server training and inference, alleviating client computational burdens.
Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Information
One promising approach to leverage existing complementary capabilities is allowing humans to delegate individual instances of decision tasks to AI.
Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation
We demonstrate the usage of state-of-the-art text-to-image architectures in the context of laparoscopic imaging with regard to the surgical removal of the gallbladder as an example.
Redefining the Laparoscopic Spatial Sense: AI-based Intra- and Postoperative Measurement from Stereoimages
A significant challenge in image-guided surgery is the accurate measurement task of relevant structures such as vessel segments, resection margins, or bowel lengths.
Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice
We hypothesize that, in addition to the mental model, human learning is a key mediator of appropriate reliance and, thus, CTP.
AB2CD: AI for Building Climate Damage Classification and Detection
To achieve robust and accurate evaluations of building damage detection and classification, we evaluated different deep learning models with residual, squeeze and excitation, and dual path network backbones, as well as ensemble techniques.
The Impact of Imperfect XAI on Human-AI Decision-Making
Our findings reveal the influence of imperfect XAI and humans' level of expertise on their reliance on AI and human-AI team performance.
ML-Based Teaching Systems: A Conceptual Framework
We present our findings in the form of a review of the key concepts, themes, and dimensions to understand and inform on ML-based teaching systems.
On the Perception of Difficulty: Differences between Humans and AI
With the increased adoption of artificial intelligence (AI) in industry and society, effective human-AI interaction systems are becoming increasingly important.
Learning to Defer with Limited Expert Predictions
In this paper, we propose a three-step approach to reduce the number of expert predictions required to train learning to defer algorithms.
Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learning
While it is often feasible to generate larger data pools within organizations, the application of analytics within (inter-organizational) business networks is still severely constrained.
Artificial Intelligence for Sustainability: Facilitating Sustainable Smart Product-Service Systems with Computer Vision
We demonstrate our approach on two products: machining tools and rotating X-ray anodes.
Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series
We propose to support this anomaly investigation by providing explanations of anomaly detection.
Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations
In this paper, we propose Appropriateness of Reliance (AoR) as an underlying, quantifiable two-dimensional measurement concept.
Towards Avoiding the Data Mess: Industry Insights from Data Mesh Implementations
As the concept of data mesh is still novel, it lacks empirical insights from the field.
Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation
However, DL-based approaches are designed for one specific task in a single geographic region based on specific frequency bands of satellite data.
Data-Centric Artificial Intelligence
Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm emphasizing that the systematic design and engineering of data is essential for building effective and efficient AI-based systems.
Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images
The current manual analysis process is expensive and time-consuming, thus automated detection of bomb craters by using deep learning is a promising way to improve the UXO disposal process.
Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer
Across a multitude of work environments, expert knowledge is imperative for humans to conduct tasks with high performance and ensure business success.
A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making
However, we find no effect of explanations on users' performance compared to sole AI predictions.
Machine Learning Operations (MLOps): Overview, Definition, and Architecture
The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production.
BIG-bench Machine Learning
Cultural Vocal Bursts Intensity Prediction
On the Effect of Information Asymmetry in Human-AI Teams
Over the last years, the rising capabilities of artificial intelligence (AI) have improved human decision-making in many application areas.
Factors that influence the adoption of human-AI collaboration in clinical decision-making
Recent developments in Artificial Intelligence (AI) have fueled the emergence of human-AI collaboration, a setting where AI is a coequal partner.
On the Influence of Explainable AI on Automation Bias
However, it may also evoke human bias, especially in the form of automation bias as an over-reliance on AI advice.
Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making
However, recent work has shown that AI advice is not always beneficial, as humans have shown to be unable to ignore incorrect AI advice, essentially representing an over-reliance on AI.
Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry
Computer-generated imagery of car models has become an indispensable part of car manufacturers' advertisement concepts.
Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence
To test this conceptualization, we develop hypotheses on the impacts of IDA and provide first evidence for their validity based on empirical studies in the literature.
What to Prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development
Managing large numbers of incoming bug reports and finding the most critical issues in hardware development is time consuming, but crucial in order to reduce development costs.
Deep Learning Strategies for Industrial Surface Defect Detection Systems
Deep learning methods have proven to outperform traditional computer vision methods in various areas of image processing.
Fake or Credible? Towards Designing Services to Support Users' Credibility Assessment of News Content
Fake news has become omnipresent in digitalized areas such as social media platforms.
Detecting Concept Drift With Neural Network Model Uncertainty
Structural changes over time are detected by applying the ADWIN technique on the uncertainty estimates, and detected drifts trigger a retraining of the prediction model.
A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems
For this purpose, we consider the design of such systems from a hybrid intelligence (HI) perspective and aim to derive prescriptive design knowledge for CV-based HI systems.
labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds
Within the past decade, the rise of applications based on artificial intelligence (AI) in general and machine learning (ML) in specific has led to many significant contributions within different domains.
Adjoint-based Shape Optimization for the Minimization of Flow-induced Hemolysis in Biomedical Applications
An optimized shape, leading to a potential improvement in hemolysis induction up to 22%, is identified.
Fluid Dynamics
Needmining: Designing Digital Support to Elicit Needs from Social Media
In a second cycle, we build on this artifact to additionally quantify the need information elicited, and prove its feasibility.
Adjoint Complement to the Universal Momentum Law of the Wall
As a direct consequence of the frequently employed assumption that all primal flow properties algebraically scale with the friction velocity, it is demonstrated that a simple algebraic expression provides a consistent closure of the adjoint momentum equation in the logarithmic layer.
Fluid Dynamics Optimization and Control
Utilizing Concept Drift for Measuring the Effectiveness of Policy Interventions: The Case of the COVID-19 Pandemic
As a reaction to the high infectiousness and lethality of the COVID-19 virus, countries around the world have adopted drastic policy measures to contain the pandemic.
Human vs. supervised machine learning: Who learns patterns faster?
We have designed an experiment in which 44 humans and three different machine learning algorithms identify patterns in labeled training data and have to label instances according to the patterns they find.
Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Sets
For efficient concept drift handling, we introduce the switching scheme which combines the two principles of retraining and updating of a machine learning model.
Adjoint Complement to the Volume-of-Fluid Method for Immiscible Flows
The dual scheme rigorously mirrors the primal Normalized-Variable-Diagram (NVD) stencils.
Fluid Dynamics Optimization and Control
Towards Leveraging End-of-Life Tools as an Asset: Value Co-Creation based on Deep Learning in the Machining Industry
Sustainability is the key concept in the management of products that reached their end-of-life.
DEAL: Deep Evidential Active Learning for Image Classification
By replacing the softmax standard output of a CNN with the parameters of a Dirichlet density, the model learns to identify data instances that contribute efficiently to improving model performance during training.
An Uncertainty-based Human-in-the-loop System for Industrial Tool Wear Analysis
Convolutional neural networks have shown to achieve superior performance on image segmentation tasks.
AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments
In the recently evolving serverless framework Knative a request-based policy is proposed, where the algorithm scales resources by a configured maximum number of requests that can be processed in parallel per instance, the so-called concurrency.
How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting
For the particular purpose of sales forecasting for similar entities, we propose a transfer machine learning approach based on additive regression models that lets new entities benefit from models of existing entities.
A network-based transfer learning approach to improve sales forecasting of new products
A network-based Transfer Learning approach for deep neural networks is designed to investigate the efficiency of Transfer Learning in the domain of food sales forecasting.
Handling Concept Drift for Predictions in Business Process Mining
However, current research lacks a recommendation which data should be selected for the retraining of the machine learning model.
A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs
Forecasts of product demand are essential for short- and long-term optimization of logistics and production.
Handling Concept Drifts in Regression Problems -- the Error Intersection Approach
Machine learning models are omnipresent for predictions on big data.
Half-empty or half-full? A Hybrid Approach to Predict Recycling Behavior of Consumers to Increase Reverse Vending Machine Uptime
Bin full events are the major reason for Reverse Vending Machine (RVM) downtime at the world leader in the RVM market.
Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability
As the number of data sets in business networks grows and not every neural net transfer is successful, indicators are needed for its impact on the target performance-its transferability.
Machine Learning in Artificial Intelligence: Towards a Common Understanding
The application of "machine learning" and "artificial intelligence" has become popular within the last decade.
Comfort-as-a-Service: Designing a User-Oriented Thermal Comfort Artifact for Office Buildings
This work addresses this research gap and aims to optimize individual environmental comfort in open office environments, taking advantage of changes in modern office infrastructure and considering actual user feedback without interfering with existing systems.
Needmining: Identifying micro blog data containing customer needs
The design of new products and services starts with the identification of needs of potential customers or users.
