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Found 13 papers, 4 papers with code
Targeted Example Generation for Compilation Errors
We present TEGCER, an automated feedback tool for novice programmers.
Evaluating Explainable Methods for Predictive Process Analytics: A Functionally-Grounded Approach
Current explainable machine learning methods, such as LIME and SHAP, can be used to interpret black box models.
Developing a Fidelity Evaluation Approach for Interpretable Machine Learning
Although modern machine learning and deep learning methods allow for complex and in-depth data analytics, the predictive models generated by these methods are often highly complex, and lack transparency.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
+2
DeepProcess: Supporting business process execution using a MANN-based recommender system
Process-aware Recommender systems can provide critical decision support functionality to aid business process execution by recommending what actions to take next.
Joint Multi-Domain Learning for Automatic Short Answer Grading
On a large-scale industry dataset and a benchmarking dataset, we show that our model performs significantly better than existing techniques which either learn domain-specific models or adapt a generic similarity scoring model from a large corpus.
Development and Deployment of a Large-Scale Dialog-based Intelligent Tutoring System
There are significant challenges involved in the design and implementation of a dialog-based tutoring system (DBT) ranging from domain engineering to natural language classification and eventually instantiating an adaptive, personalized dialog strategy.
Exploring Interpretability for Predictive Process Analytics
The explanations allow us to gain an understanding of the underlying reasons for a prediction and highlight scenarios where accuracy alone may not be sufficient in assessing the suitability of techniques used to encode event log data to features used by a predictive model.
An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics
We see certain distinct features used for predictions that provide useful insights about the type of cancer, along with features that do not generalize well.
Decision Making
Interpretability Techniques for Deep Learning
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models
This has led to an increased interest in interpretable machine learning, where post hoc interpretation presents a useful mechanism for generating interpretations of complex learning models.
Explainable AI Enabled Inspection of Business Process Prediction Models
As an important branch of state-of-the-art data analytics, business process predictions are also faced with a challenge in regard to the lack of explanation to the reasoning and decision by the underlying `black-box' prediction models.
Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms
In this paper, we address the "black-box" problem in predictive process analytics by building interpretable models that are capable to inform both what and why is a prediction.
Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning
The results show that the recommendations from our proposed approach outperform in goal satisfaction and conformance compared to the existing state-of-the-art next best activity recommendation techniques.
AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data
Business and IT Observability (BizITObs) data fuses both Biz-KPIs and IT event channels together as multivariate time series data.
