Process Knowledge-infused Learning for Suicidality Assessment on Social Media

• URL: http://arxiv.org/abs/2204.12560v1
• Date: Tue, 26 Apr 2022 19:43:41 GMT
• Title: Process Knowledge-infused Learning for Suicidality Assessment on Social Media
• Authors: Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth
• Abstract summary: Current methods rely on the traditional pipeline of predicting labels from data. Post hoc explanations on the data to label prediction using explainable AI (XAI) models leave much to be desired to the end-users. PK-iL utilizes a structured process knowledge that explicitly explains the underlying prediction process that makes sense to end-users.
• Score: 14.362199192484006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world. In several domains, such as healthcare, such technology has significant potential to reduce the burden on humans by providing quality assistance at scale. However, current methods rely on the traditional pipeline of predicting labels from data, thus completely ignoring the process and guidelines used to obtain the labels. Furthermore, post hoc explanations on the data to label prediction using explainable AI (XAI) models, while satisfactory to computer scientists, leave much to be desired to the end-users due to lacking explanations of the process in terms of human-understandable concepts. We \textit{introduce}, \textit{formalize}, and \textit{develop} a novel Artificial Intelligence (A) paradigm -- Process Knowledge-infused Learning (PK-iL). PK-iL utilizes a structured process knowledge that explicitly explains the underlying prediction process that makes sense to end-users. The qualitative human evaluation confirms through a annotator agreement of 0.72, that humans are understand explanations for the predictions. PK-iL also performs competitively with the state-of-the-art (SOTA) baselines.

Related papers

• Concept Induction using LLMs: a user experiment for assessment [1.1982127665424676]
  This study explores the potential of a Large Language Model (LLM) to generate high-level concepts that are meaningful as explanations for humans. We compare the concepts generated by the LLM with two other methods: concepts generated by humans and the ECII concept induction system. Our findings indicate that while human-generated explanations remain superior, concepts derived from GPT-4 are more comprehensible to humans compared to those generated by ECII.
  arXiv  Detail & Related papers  (2024-04-18T03:22:02Z)
• Explaining Explainability: Towards Deeper Actionable Insights into Deep Learning through Second-order Explainability [70.60433013657693]
  Second-order explainable AI (SOXAI) was recently proposed to extend explainable AI (XAI) from the instance level to the dataset level. We demonstrate for the first time, via example classification and segmentation cases, that eliminating irrelevant concepts from the training set based on actionable insights from SOXAI can enhance a model's performance.
  arXiv  Detail & Related papers  (2023-06-14T23:24:01Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI) [68.8204255655161]
  The emphasis of XAI research appears to have turned to a more pragmatic explanation approach for better understanding. An extensive area where cognitive science research may substantially influence XAI advancements is evaluating user knowledge and feedback. We propose a framework to experiment with generating and evaluating the explanations on the grounds of different cognitive levels of understanding.
  arXiv  Detail & Related papers  (2022-10-31T19:20:22Z)
• Neuro-Symbolic Learning of Answer Set Programs from Raw Data [54.56905063752427]
  Neuro-Symbolic AI aims to combine interpretability of symbolic techniques with the ability of deep learning to learn from raw data. We introduce Neuro-Symbolic Inductive Learner (NSIL), an approach that trains a general neural network to extract latent concepts from raw data. NSIL learns expressive knowledge, solves computationally complex problems, and achieves state-of-the-art performance in terms of accuracy and data efficiency.
  arXiv  Detail & Related papers  (2022-05-25T12:41:59Z)
• An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability [3.04585143845864]
  We present a new model-agnostic metric to measure the Degree of eXplainability of correct information in an objective way. We designed a few experiments and a user-study on two realistic AI-based systems for healthcare and finance.
  arXiv  Detail & Related papers  (2021-09-11T17:44:13Z)
• LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information [6.570220157893279]
  Interpretable machine learning (IML) is an urgent topic of research. This paper focuses on a local-based, neural-specific interpretation process applied to textual and time-series data.
  arXiv  Detail & Related papers  (2021-04-13T09:39:33Z)
• Teaching the Machine to Explain Itself using Domain Knowledge [4.462334751640166]
  Non-technical humans-in-the-loop struggle to comprehend the rationale behind model predictions. We present JOEL, a neural network-based framework to jointly learn a decision-making task and associated explanations. We collect the domain feedback from a pool of certified experts and use it to ameliorate the model (human teaching)
  arXiv  Detail & Related papers  (2020-11-27T18:46:34Z)
• Explainable AI for Classification using Probabilistic Logic Inference [9.656846523452502]
  We present an explainable classification method. Our method works by first constructing a symbolic Knowledge Base from the training data, and then performing probabilistic inferences on such Knowledge Base with linear programming. It identifies decisive features that are responsible for a classification as explanations and produces results similar to the ones found by SHAP, a state of the artley Value based method.
  arXiv  Detail & Related papers  (2020-05-05T11:39:23Z)
• Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
  We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches. Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
  arXiv  Detail & Related papers  (2020-03-09T15:04:07Z)
• A general framework for scientifically inspired explanations in AI [76.48625630211943]
  We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
  arXiv  Detail & Related papers  (2020-03-02T10:32:21Z)

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.