AI Trust in business processes: The need for process-aware explanations
- URL: http://arxiv.org/abs/2001.07537v1
- Date: Tue, 21 Jan 2020 13:51:36 GMT
- Title: AI Trust in business processes: The need for process-aware explanations
- Authors: Steve T.K. Jan, Vatche Ishakian, Vinod Muthusamy
- Abstract summary: Business process management (BPM) literature is rich in machine learning solutions.
Deep learning models including those from the NLP domain have been applied to process predictions.
We assert that a large reason for the lack of adoption of AI models in BPM is that business users are risk-averse and do not implicitly trust AI models.
- Score: 11.161025675113208
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Business processes underpin a large number of enterprise operations including
processing loan applications, managing invoices, and insurance claims. There is
a large opportunity for infusing AI to reduce cost or provide better customer
experience, and the business process management (BPM) literature is rich in
machine learning solutions including unsupervised learning to gain insights on
clusters of process traces, classification models to predict the outcomes,
duration, or paths of partial process traces, extracting business process from
documents, and models to recommend how to optimize a business process or
navigate decision points. More recently, deep learning models including those
from the NLP domain have been applied to process predictions.
Unfortunately, very little of these innovations have been applied and adopted
by enterprise companies. We assert that a large reason for the lack of adoption
of AI models in BPM is that business users are risk-averse and do not
implicitly trust AI models. There has, unfortunately, been little attention
paid to explaining model predictions to business users with process context. We
challenge the BPM community to build on the AI interpretability literature, and
the AI Trust community to understand
Related papers
- The Danger Within: Insider Threat Modeling Using Business Process Models [0.259990372084357]
This paper develops a novel insider threat knowledge base and a threat modeling application that leverages Business Process Modeling and Notation (BPMN)
The results indicate that even without annotation, BPMN diagrams can be leveraged to automatically identify insider threats in an organization.
arXiv Detail & Related papers (2024-06-03T09:26:53Z) - Process Modeling With Large Language Models [42.0652924091318]
This paper explores the integration of Large Language Models (LLMs) into process modeling.
We propose a framework that leverages LLMs for the automated generation and iterative refinement of process models.
Preliminary results demonstrate the framework's ability to streamline process modeling tasks.
arXiv Detail & Related papers (2024-03-12T11:27:47Z) - SNAP: Semantic Stories for Next Activity Prediction [4.5723650480442535]
Predicting the next activity in an ongoing process is one of the most common classification tasks in the business process management domain.
Current state-of-the-art AI models for business process prediction do not fully capitalize on available semantic information within process event logs.
We propose a novel SNAP method that leverages language foundation models by constructing semantic contextual stories from the process historical event logs.
arXiv Detail & Related papers (2024-01-28T10:20:15Z) - Combatting Human Trafficking in the Cyberspace: A Natural Language
Processing-Based Methodology to Analyze the Language in Online Advertisements [55.2480439325792]
This project tackles the pressing issue of human trafficking in online C2C marketplaces through advanced Natural Language Processing (NLP) techniques.
We introduce a novel methodology for generating pseudo-labeled datasets with minimal supervision, serving as a rich resource for training state-of-the-art NLP models.
A key contribution is the implementation of an interpretability framework using Integrated Gradients, providing explainable insights crucial for law enforcement.
arXiv Detail & Related papers (2023-11-22T02:45:01Z) - Benchmarking Automated Machine Learning Methods for Price Forecasting
Applications [58.720142291102135]
We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions.
Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part.
We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
arXiv Detail & Related papers (2023-04-28T10:27:38Z) - Just Tell Me: Prompt Engineering in Business Process Management [63.08166397142146]
GPT-3 and other language models (LMs) can effectively address various natural language processing (NLP) tasks.
We argue that prompt engineering can help bring the capabilities of LMs to BPM research.
arXiv Detail & Related papers (2023-04-14T14:55:19Z) - Generating Hidden Markov Models from Process Models Through Nonnegative Tensor Factorization [0.0]
We introduce a novel mathematically sound method that integrates theoretical process models with interrelated minimal Hidden Markov Models.
Our method consolidates: (a) theoretical process models, (b) HMMs, (c) coupled nonnegative matrix-tensor factorizations, and (d) custom model selection.
arXiv Detail & Related papers (2022-10-03T16:19:27Z) - Extending LIME for Business Process Automation [2.5470840043956886]
Business process applications have ordering or constraints on tasks that cause lightweight, model-agnostic, existing explanation methods like LIME to fail.
We propose a local explanation framework extending LIME for explaining AI business process applications.
arXiv Detail & Related papers (2021-08-09T21:30:46Z) - Explanations of Machine Learning predictions: a mandatory step for its
application to Operational Processes [61.20223338508952]
Credit Risk Modelling plays a paramount role.
Recent machine and deep learning techniques have been applied to the task.
We suggest to use LIME technique to tackle the explainability problem in this field.
arXiv Detail & Related papers (2020-12-30T10:27:59Z) - AI-based Modeling and Data-driven Evaluation for Smart Manufacturing
Processes [56.65379135797867]
We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes.
We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
arXiv Detail & Related papers (2020-08-29T14:57:53Z) - Process Discovery for Structured Program Synthesis [70.29027202357385]
A core task in process mining is process discovery which aims to learn an accurate process model from event log data.
In this paper, we propose to use (block-) structured programs directly as target process models.
We develop a novel bottom-up agglomerative approach to the discovery of such structured program process models.
arXiv Detail & Related papers (2020-08-13T10:33:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.