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Found 23 papers, 3 papers with code
Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence
Our work provides researchers with a theoretical foundation of complementarity in human-AI decision-making and demonstrates that leveraging sources of complementarity potential constitutes a viable pathway toward effective human-AI collaboration.
On the Effect of Contextual Information on Human Delegation Behavior in Human-AI collaboration
In this work, we explore the effects of providing contextual information on human decisions to delegate instances to an AI.
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.
Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts
Hence, human-in-the-loop (HITL) extensions to ML models add a human review for instances that are difficult to classify.
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 Interdependence of Reliance Behavior and Accuracy in AI-Assisted Decision-Making
In AI-assisted decision-making, a central promise of putting a human in the loop is that they should be able to complement the AI system by adhering to its correct and overriding its mistaken recommendations.
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.
Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction
In an experimental study with 196 participants, we show that task performance and task satisfaction improve through AI delegation, regardless of whether humans are aware of the delegation.
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.
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.
Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts
In this work, we propose an approach that trains a classification model to complement the capabilities of multiple human experts.
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.
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.
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.
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.
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.
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.
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.
Handling Concept Drifts in Regression Problems -- the Error Intersection Approach
Machine learning models are omnipresent for predictions on big data.
Machine Learning in Artificial Intelligence: Towards a Common Understanding
The application of "machine learning" and "artificial intelligence" has become popular within the last decade.
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.
