Give me 3W1H: A Bibliometric View on Accountable AI

2023 | International Conference on Information Systems | Citations: 0

Authors: Bartsch, Sebastian; Schmidt, Jan-Hendrik

Abstract: Accountability is crucial to make stakeholders of Artificial Intelligence (AI)- ... Expand

Abstract: Accountability is crucial to make stakeholders of Artificial Intelligence (AI)-based systems justify their actions, thereby explaining the harm such systems cause to AI users. Due to the importance of accountability in the context of AI, accountability was introduced into IS research through literature reviews. Therefore, while IS research's understanding of accountability covers the necessary depth, it comes at the expense of its essential breadth. Using a bibliometric analysis with 19,978 English-language papers, we shed light on the essential breadth posing three W-and one H-questions (When, What, Whereof, and How). Therefore, we contribute to IS research by highlighting the urgent need to revise existing definitions of accountability in the context of AI and establish them in IS research. We argue that a missing revision leads to non-transferrable findings within IS research. Accordingly, this study serves as a starting point for adapting definitions and creating a shared understanding in IS research. Collapse

Topics: artificial intelligence explainable artificial intelligence legal context database system IT security

Methods: word embedding word2vec bibliometric analysis machine learning supervised learning

Understanding Emojis for Financial Sentiment Analysis

2023 | International Conference on Information Systems | Citations: 0

Authors: Chen, Siyi; Xing, Frank

Abstract: ... Expand

Abstract: Collapse

Topics: application programming interface social media website cryptocurrency Python

Methods: sentiment analysis machine learning BERT deep learning experiment

Clustering and Topological Data Analysis: Comparison and Application

2023 | HICSS | Citations: 0

Authors: Combs, K.; Bihl, T.

Abstract: Clustering is common technique used to demonstrate relationships between data a ... Expand

Abstract: Clustering is common technique used to demonstrate relationships between data and information. Of recent interest is topological data analysis (TDA), which can represent and cluster data through persistent homology. The TDA algorithms used include the Topological Mode Analysis Tool (ToMATo) algorithm, Garin and Tauzin's TDA Pipeline, and the Mapper algorithm. First, TDA is compared to ten other clustering algorithms on artificial 2D data where it ranked third overall. TDA had the second-highest performance in terms of average accuracy (97.9%); however, its computation-time performance ranked in the middle of the algorithms. TDA ranked fourth on the qualitative "visual trustworthiness" metric. On realworld data, TDA showed promising classification results (accuracy between 80-95%). Overall, this paper shows TDA is a competitive algorithm performancewise, though computationally expensive. When TDA is used for visualization, the Mapper algorithm allows for unique alternative views especially effective for visualizing highly dimensional data. Collapse

Topics: Python cloud computing missing data

Methods: computational algorithm cluster analysis DBSCAN agglomerative hierarchical clustering BIRCH

Fine-grained classification of drug trafficking based on Instagram hashtags

2023 | Decision Support Systems | Citations: 0

Authors: Hu, Chuanbo; Liu, Bin; Ye, Yanfang; Li, Xin

Abstract: Social networks have become important platforms for the marketing and sale of il ... Expand

Abstract: Social networks have become important platforms for the marketing and sale of illicit drugs. Hashtags make it easier for users to engage in drug trafficking, further increasing the risk of drug abuse. However, there are significant challenges in the detection and management of drug trafficking activities. In addition, the rapid legalization of some drugs has required a fine-grained classification of drugs to distinguish them from those that are illegal. Motivated by these observations, in this paper, our aim is to develop a methodology using the latest advances in AI technology to classify hashtags from posts advertising illicit drugs for sale on social networks. We present a semi-supervised deep learning approach to classify hashtags from posts advertising illicit drugs. An elegant combination of Bidirectional Encoder Representations from Transformers (BERT) with Graph Convolu­ tional Network (GCN) allows us to analyze the characteristics (e.g., shipping region and platform self-regulation) of illegal drug trafficking. Our BERT+GCN model achieved the best performance with more than 75% accuracy compared to the other three baseline models. Then, fine-grained hashtags identified are applied to explore the characteristics of drug trafficking. Finally, we report our results for further exploration of shipping regions and self-regulation of drug trafficking on the platform in two analysis scenarios. Our developed approach has shown its effectiveness in detecting hashtags for different types of drugs from illegal drug sellers. Based on hashtag classification, we also provide two case studies that indicate that (1) there are differences in self-regulation for different types of drugs on social media, (2) there are regional differences in the demand for different types of drugs. Collapse

Topics: Instagram social media Google+ artificial intelligence advertising management

Methods: BERT case study machine learning deep learning semi-supervised learning

Facilitators and Barriers of Artificial Intelligence Adoption in Business – Insights from Opinions Using Big Data Analytics

2023 | Information Systems Frontiers | Citations: 0

Authors: Kar, Arpan Kumar; Kushwaha, Amit Kumar

Abstract: Data-driven predictions have become an inseparable part of business decisions. A ... Expand

Abstract: Data-driven predictions have become an inseparable part of business decisions. Artificial Intelligence (AI) has started helping the product and support teams perform more accurate experiments in various business settings. This study proposes a framework for businesses based on inductive learnings related to success and barriers shared on social media platforms. Our goal is to analyse the signals emerging from these conversational opinions from the early adoption of AI, with a focus towards facilitators and barriers faced by teams. Factors like efficiency, innovation, business research, product novelty, manual intervention, adaptability, emotion, support, personal growth, experiential learning, fear of failure and fear of upgradation have been identified based on an exploratory study and then a confirmatory study. We present the learnings through a roadmap for practitioners. This study contributes to the IS literature by delineating AI as a determinant of success and introduces a lot of organizational factors into the model. Collapse

Topics: artificial intelligence Twitter innovation management big data social media

Methods: topic model literature study synthesis regression analysis method data transformation

Theories: organizational theory organizational behavior theory management theory technology acceptance model organizational learning theory

Improved learning performance for small datasets in high dimensions by new dual-net model for non-linear interpolation virtual sample generation

2023 | Decision Support Systems | Citations: 0

Authors: Lin, Liang-Sian; Lin, Yao-San; Li, Der-Chiang; Liu, Yun-Hsuan

Abstract: The number of reliable samples obtained in early decision-making activity is usu ... Expand

Abstract: The number of reliable samples obtained in early decision-making activity is usually relatively small. Due to variable distribution and incomplete structure of tiny datasets, it is challenging to create reliable and robust predictive modeling using classic statistical and machine learning models in small sample settings. The virtual sample generation (VSG) technique improves model learning accuracies for minimal datasets across diverse applications. Virtual samples on independent variables were generated using established VSG methods predi­ cated on the assumption of a probability distribution or a membership function to fill data gaps. However, in the actual world, non-linear function interactions between variables are common. To address this issue, this paper developed a novel VSG method called Dual-VSG, which generates non-linear interpolation virtual samples using a self-supervised learning (SSL) framework to improve learning performance on small datasets. We generated non-linear interpolation virtual samples without labels by estimating non-linear functions and transforming them into a high-dimensional space using the proposed dual-net model. The weights of the dual-net model are transferred to a downstream task to generate virtual sample labels. To demonstrate the effectiveness of the suggested strategy, this research employed five datasets. On the Backpropagation Neural Networks (BPNN) predictive model, we compared the suggested method’s prediction performance to two state-of-the-art VSG approaches. To assess prediction performance on a regression dataset, the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) are used. Furthermore, the classification accuracy (ACC) and the Fl measure are used to assess classification capability on classification datasets. In addition, the paired t-test was utilized to see if the suggested Dual-VSG approach differed significantly from the other VSG methods in terms of RMSE, MAPE, accuracy (ACC), or F1 score. For short datasets, the suggested Dual-VSG method out­ performs those VSG methods, according to our experimental results. Collapse

Topics: Python programming language

Methods: data transformation t-distributed stochastic neighbor embedding experiment machine learning parametric test

A decision support framework to incorporate textual data for early student dropout prediction in higher education

2023 | Decision Support Systems | Citations: 0

Authors: Phan, Minh; De Caigny, Arno; Coussement, Kristof

Abstract: Managing student dropout in higher education is critical, considering its substa ... Expand

Abstract: Managing student dropout in higher education is critical, considering its substantial impacts on students’ lives, academic institutions, and society as a whole. Using predictive modeling can be instrumental for this task, as a means to identify dropouts proactively on the basis of student characteristics and their academic performance. To enhance these predictions, textual student feedback also might be relevant; this article proposes a hybrid decision support framework that combines predictive modeling with student segmentation efforts. A real-life data set from a French higher education institution, containing information of 14,391 students and 62,545 feedback documents, confirms the superior performance of the proposed framework, in terms of the area under the curve and top decile lift, compared with various benchmarks. In contributing to decision support system research, this study (1) proposes a new framework for automatic, data-driven segmentation of students based on textual data; (2) compares multiple text representation methods and confirms that incorporating student textual feedback data improves the predictive performance of student dropout models; and (3) establishes useful insights to help decision-makers anticipate and manage student dropout behaviors. Collapse

Topics: decision support decision support system electronic mail

Methods: machine learning doc2vec data transformation BERT computational algorithm

Examining Disease Multimorbidity in U.S. Hospital Visits Before and During COVID-19 Pandemic: A Graph Analytics Approach

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Srinivasan, Karthik; Jiang, Jinhang

Abstract: Enduring effects of the COVID-19 pandemic on healthcare systems can be preempted ... Expand

Abstract: Enduring effects of the COVID-19 pandemic on healthcare systems can be preempted by identifying patterns in diseases recorded in hospital visits over time. Disease multimorbidity or simultaneous occurrence of multiple diseases is a growing global public health challenge as populations age and long-term conditions become more prevalent. We propose a graph analytics framework for analyzing disease multimorbidity in hospital visits. Within the framework, we propose a graph model to explain multimorbidity as a function of prevalence, category, and chronic nature of the underlying disease. We apply our model to examine and compare multimorbidity patterns in public hospitals in Arizona, U.S., during five six-month time periods before and during the pandemic. We observe that while multimorbidity increased by 34.26% and 41.04% during peak pandemic for mental disorders and respiratory disorders respectively, the gradients for endocrine diseases and circulatory disorders were not significant. Multimorbidity for acute conditions is observed to be decreasing during the pandemic while multimorbidity for chronic conditions remains unchanged. Our graph analytics framework provides guidelines for empirical analysis of disease multimorbidity using electronic health records. The patterns identified using our proposed graph model informs future research and healthcare policy makers for pre-emptive decision making. Collapse

Topics: pandemic electronic health record decision making programming language database system

Methods: longitudinal survey longitudinal research deep learning machine learning ablation analysis

MDSCAN: AN EXPLAINABLE ARTIFICIAL INTELLIGENCE ARTIFACT FOR MENTAL HEALTH SCREENING

2023 | European Conference On Information Systems | Citations: 0

Authors: Tutun, Salih; Bhattacherjee, Anol; Topuz, Kazim; Tosyali, Ali; Li, Gorden

Abstract: This paper presents a novel artifact called MDscan that can help mental health ... Expand

Abstract: This paper presents a novel artifact called MDscan that can help mental health professionals quickly screen a large number of patients for ten mental disorders. MDscan uses patient responses to the SCL-90-R clinical questionnaire to create a full-color image, similar to radiological images, which identifies which disorder or combination of disorders may afflict a patient, the severity of the disorder, and the underlying logic of this prediction, using an explainable artificial intelligence (XAI) approach. While prior artificial intelligence (AI) tools have seen limited acceptance in clinical practice because of the lack of transparency and interpretability in their "black box" models, the XAI approach used in MDscan is a "white box" model that elaborates which patient feature contributes to the predicted outcome and to what extent. Using patient data from a mental health clinic, we demonstrate that MDscan outperforms current (expert-based) clinical practice by an average of 20%. Collapse

Topics: explainable artificial intelligence shapley additive explanation local interpretable model agnostic explanation IT security artificial intelligence

Methods: computational algorithm machine learning data transformation t-distributed stochastic neighbor embedding survey

Conversational Agents for Mental Health and Well-being: Discovering Design Recommendations Using Text Mining

2023 | HICSS | Citations: 0

Authors: Wahbeh, Abdullah; Al-Ramahi, Mohammad; El-Gayar, Omar; Elnoshokaty, Ahmed; Nasralah, Tareq

Abstract: Conversational agents are increasingly being used by the general population due ... Expand

Abstract: Conversational agents are increasingly being used by the general population due to shortages in healthcare providers and specialists, and limited access to treatments. They are also used by people to deal with loneliness and lack of companionship. As these apps are increasingly replacing real humans, there is a need to explore their design features and limitations for better design of conversational apps. Using text mining and topic modeling, this study analyzed a total of 126,610 reviews about Replika, a popular and well-established conversational agent mobile app. Our results emphasized current practices for designing conversational apps while at the same time sheds the light on limitations associated with these apps. Such limitations are related to the need for better conversations and intelligent responses, the need for advanced AI chatbots, the need to avoid questionable and inappropriate content, the need for inclusive design, and the need to address some technical limitations. Collapse

Topics: mobile application conversational agent artificial intelligence user experience Python

Methods: chatbot topic model natural language processing data transformation design principle