Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities

2023 | Information Systems Frontiers | Citations: 0

Authors: Alahakoon, Damminda; Nawaratne, Rashmika; Xu, Yan; De Silva, Daswin; Sivarajah, Uthayasankar; Gupta, Bhumika

Abstract: The emerging information revolution makes it necessary to manage vast amounts of ... Expand

Abstract: The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the self-building AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications. Collapse

Topics: artificial intelligence smart city big data cloud computing distributed system

Methods: computational algorithm experiment unsupervised learning machine learning longitudinal research

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

Two Sides of the Same Pillow: Unfolding the Relationship between Objective and Subjective Sleep Quality with Unsupervised Learning

2023 | International Conference on Information Systems | Citations: 0

Authors: Biedebach, Luka; Óskarsdóttir, María; Arnardottir, Erna; Islind, Anna Sigríður

Abstract: ... Expand

Abstract: Collapse

Topics: mobile system smart watch mobile application smart wearable device healthcare data

Methods: descriptive statistic cluster analysis parametric test ANOVA longitudinal research

Adversarial Outlier Detection Methods for Health Care Fraud

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Ekin, Tahir

Abstract: Outlier detection is widely used for identification of unusual suspicious patte ... Expand

Abstract: Outlier detection is widely used for identification of unusual suspicious patterns in health care fraud detection. However, the impact of adversarial perturbations on outliers and the related decisions are not well studied. In the health care fraud detection domain, standard practice does not consider health care billings being subject to intentional data manipulations. This paper presents a decision theoretic approach for outlier detection in adversarial environments. Proposed adversarial risk analysis-based framework allows incomplete information and adversarial perturbations on the data inputs. While the work with actual health care fraud data is ongoing, the proposed novel adversarial outlier detection method has the potential to support health care fraud audit decision support systems. KeywordsAdversarial unsupervised learning, adversarial risk analysis, health care fraud, fraud analytics, Bayesian. Collapse

Topics: fraud detection

Methods: anomaly detection unsupervised learning Monte Carlo simulation machine learning

Theories: game theory

Cyber threat detection: Unsupervised hunting of anomalous commands (UHAC)

2023 | Decision Support Systems | Citations: 0

Authors: Kayhan, Varol O.; Agrawal, Manish; Shivendu, Shivendu

Abstract: The cyber security industry is rapidly adopting threat hunting as a proactive to ... Expand

Abstract: The cyber security industry is rapidly adopting threat hunting as a proactive tool for early and faster detection of suspected malicious actors. In this paper, we propose a machine learning-based method, Unsupervised Hunting of Anomalous Commands (UHAC), to detect text-based anomalous commands in security information and event management (SIEM) logs that are good candidates for threat hunting. A unique feature of the proposed method is that it first creates a feature set based on the augmentation of document-term and document-character matrices. Then, an autoencoder-based detector is trained on this feature set using a custom loss function. UHAC consis­ tently outperforms other feature sets and algorithms such as one-class support vector machine, density-based spatial clustering of applications with noise, and word-embedding based models such as word2vec. The UHAC detector identifies 84–89% of anomalies in the top 10% of the data. Findings have implications for cybersecurity analysts who perform threat hunting in SIEM logs for process auditing on endpoint devices. Collapse

Topics: IT security artificial intelligence Python decision support system email phishing

Methods: autoencoder data transformation DBSCAN anomaly detection machine learning

ROLEX: A Novel Method for Interpretable Machine Learning Using Robust Local Explanations

2023 | Management Information Systems Quarterly | Citations: 2

Authors: Kim, Buomsoo (Raymond); Srinivasan, Karthik; Kong, Sung Hye; Kim, Jung Hee; Shin, Chan Soo; Ram, Sudha

Abstract: Recent developments in big data technologies are revolutionizing the field of h ... Expand

Abstract: Recent developments in big data technologies are revolutionizing the field of healthcare predictive analytics (HPA), enabling researchers to explore challenging problems using complex prediction models. Nevertheless, healthcare practitioners are reluctant to adopt those models as they are less transparent and accountable due to their black-box structure. We believe that instance-level, or local, explanations enhance patient safety and foster trust by enabling patient-level interpretations and medical knowledge discovery. Therefore, we propose the RObust Local EXplanations (ROLEX) method to develop robust, instance-level explanations for HPA models in this study. ROLEX adapts state-of-the-art methods and ameliorates their shortcomings in explaining individual-level predictions made by black-box machine learning models. Our analysis with a large real-world dataset related to a prevalent medical condition called fragility fracture and two publicly available healthcare datasets reveals that ROLEX outperforms widely accepted benchmark methods in terms of local faithfulness of explanations. In addition, ROLEX is more robust since it does not rely on extensive hyperparameter tuning or heuristic algorithms. Explanations generated by ROLEX, along with the prototype user interface presented in this study, have the potential to promote personalized care and precision medicine by providing patient-level interpretations and novel insights. We discuss the theoretical implications of our study in healthcare, big data, and design science. Collapse

Topics: explainable artificial intelligence usability user interface local interpretable model agnostic explanation problem solving

Methods: machine learning design artifact computational algorithm longitudinal research ablation analysis

Theories: lemon market theory

Driving risk prevention in usage-based insurance services based on interpretable machine learning and telematics data

2023 | Decision Support Systems | Citations: 0

Authors: Li, Hong-Jie; Luo, Xing-Gang; Zhang, Zhong-Liang; Jiang, Wei; Huang, Shen-Wei

Abstract: Usage-based insurance (UBI) adjusts premiums based on an individual policyholder ... Expand

Abstract: Usage-based insurance (UBI) adjusts premiums based on an individual policyholder’s dynamic risk evaluation, incentivizing policyholders to maintain safe driving behavior in pursuit of a lower insurance premium. Although post-trip interventions can improve driving behavior significantly, most interventions do not provide risk mitigation strategies tailored to long-term driving behavior. This study proposes a novel approach for driving risk prevention in UBI services, providing risk analysis services and tailoring driving suggestions for policyholders. Advanced prediction models and model interpretation methods were adopted to assess and analyze personal driving risks, respectively. A multi-objective counterfactual model was developed to generate risk mitigation strategies for policyholders. The significance of driving behavior in risk mitigation and its actionability were assessed to provide realistic and actionable suggestions for policyholders. The case study, based on a real dataset from an insurance company in China, showed that the proposed approach can be directly embedded in the existing UBI service framework to provide personalized feedback and services to policyholders. Collapse

Topics: shapley additive explanation decision support system Python decision making logistics management

Methods: experiment computational algorithm machine learning XGBoost factor analysis

Understanding customer satisfaction in curated subscription services: Moderating roles of subscription time and information disclosure

2023 | Information & Management | Citations: 0

Authors: Li, Zhuo; Zhao, Xuefeng; Ou, Grace

Abstract: A new form of online retailing, curated subscription services, is attracting an ... Expand

Abstract: A new form of online retailing, curated subscription services, is attracting an increasing number of consumers with their surprise elements. Extant literature has explored individual adoption motivation but has provided limited attention to customer-perceived consumption behavior. This study investigates the determinants of customer satisfaction with curated subscription services across different subscription box categories based on expectation disconfirmation theory. Sentiment analysis technology is conducted to extract customers’ attributelevel experiences from 22,551 customer reviews associated with 676 subscription boxes. Results show that sentiment toward utilitarian and hedonic attributes of products and services is positively associated with customer satisfaction, with the sentiment toward hedonic attributes exerting a stronger influence on satisfaction compared to utilitarian attributes. Findings also indicate that subscription time and information disclosure moderate the relationship between sentiment toward hedonic and utilitarian attributes and customer satisfac­ tion. This study enriches theoretical research on customer satisfaction, offers insights into consumer behavioral research in subscription retailing, and provides implications for managers to attract more subscribers to the curated service. Collapse

Topics: customer satisfaction data breach Python online review marketing management

Methods: sentiment analysis natural language processing support vector machine computational algorithm machine learning

Theories: expectation disconfirmation theory

Time Series Prediction Using Deep Learning Methods in Healthcare

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

Authors: Morid, Mohammad Amin; Sheng, Olivia R. Liu; Dunbar, Joseph

Abstract: Traditional machine learning methods face unique challenges when applied to heal ... Expand

Abstract: Traditional machine learning methods face unique challenges when applied to healthcare predictive analytics. The high-dimensional nature of healthcare data necessitates labor-intensive and time-consuming processes when selecting an appropriate set of features for each new task. Furthermore, machine learning methods depend heavily on feature engineering to capture the sequential nature of patient data, oftentimes failing to adequately leverage the temporal patterns of medical events and their dependencies. In contrast, recent deep learning (DL) methods have shown promising performance for various healthcare prediction tasks by specifically addressing the high-dimensional and temporal challenges of medical data. DL techniques excel at learning useful representations of medical concepts and patient clinical data as well as their nonlinear interactions from high-dimensional raw or minimally processed healthcare data. In this article, we systematically reviewed research works that focused on advancing deep neural networks to leverage patient structured time series data for healthcare prediction tasks. To identify relevant studies, we searched MEDLINE, IEEE, Scopus, and ACM Digital Library for relevant publications through November 4, 2021. Overall, we found that researchers have contributed to deep time series prediction literature in 10 identifiable research streams: DL models, missing value handling, addressing temporal irregularity, patient representation, static data inclusion, attention mechanisms, interpretation, incorporation of medical ontologies, learning strategies, and scalability. This study summarizes research insights from these literature streams, identifies several critical research gaps, and suggests future research opportunities for DL applications using patient time series data. Collapse

Topics: missing data health information system database system healthcare data

Methods: longitudinal research time series analysis structured literature research ontological modelling literature study

Champions for Social Good: How Can We Discover Social Sentiment and Attitude-Driven Patterns in Prosocial Communication?

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Mukkamala, Raghava; Kauffman, Robert; Henriksen, Helle

Abstract: The UN High Commissioner on Refugees (UNHCR) is pursuing a social media strateg ... Expand

Abstract: The UN High Commissioner on Refugees (UNHCR) is pursuing a social media strategy to inform people about displaced populations and refugee emergencies. It is actively engaging public figures to increase awareness through its prosocial communications and improve social informedness and support for policy changes in its services. We studied the Twitter communications of UNHCR social media champions and investigated their role as high-profile influencers. In this study, we offer a design science research and data analytics framework and propositions based on the social informedness theory we propose in this paper to assess communication about UNHCR's mission. Two variables-refugee-emergency and champion type-relate to the informedness of UNHCR champions' followers. Based on a Twitter sentiment and attitude corpus, we applied a five-step design science analytics framework involving machine learning and natural language processing to test how the emergency type and champion type impact social communication patterns. Positive and neutral sentiment dominated the tweets of the champions and their followers for most refugee-emergency types. High participation-intensity champions emphasized high-intensity emergencies with tweet patterns reflecting dominant positive or neutral sentiment and sharing/liking attitudes. However, we found that sports figures effects were limited in spreading UNHCR's message, despite their millions of followers. We demonstrate the power of data science for prosocial policy based on refugee crisis awareness and instantiate our methods and knowledge contributions in a research framework that derives knowledge, decisions, and actions from behavioral, design, and economics of information systems perspectives. Collapse

Topics: Twitter social media analytical information system big data application programming interface

Methods: design science BERT natural language processing unsupervised learning artificial neural network

Theories: Lewin's field theory