The Opportunity in Difficulty: A Dynamic Privacy Budget Allocation Mechanism for Privacy-Preserving Multi-dimensional Data Collection

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

Authors: Chen, Xue; Wang, Cheng; Yang, Qing; Hu, Teng; Jiang, Changjun

Abstract: Data collection under local differential privacy (LDP) has been gradually on the ... Expand

Abstract: Data collection under local differential privacy (LDP) has been gradually on the stage. Compared with the implementation of LDP on the single attribute data collection, that on multi-dimensional data faces great challenges as follows: (1) Communication cost. Multivariate data collection needs to retain the correlations between attributes, which means that more complex privatization mechanisms will result in more communication costs. (2) Noise scale. More attributes have to share the privacy budget limited by data utility and privacy-preserving level, which means that less privacy budget can be allocated to each of them, resulting in more noise added to the data. In this work, we innovatively reverse the complex multi-dimensional attributes, i.e., the major negative factor that leads to the above difficulties, to act as a beneficial factor to improve the efficiency of privacy budget allocation, so as to realize a multi-dimensional data collection under LDP with high comprehensive performance. Specifically, we first present a Multivariate k-ary Randomized Response (kRR) mechanism, called Multi-kRR. It applies the RR directly to each attribute to reduce the communication cost. To deal with the impact of a large amount of noise, we propose a Markov-based dynamic privacy budget allocation mechanism Markov-kRR, which determines the present privacy budget (flipping probability) of an attribute related to the state of the previous attributes. Then, we fix the threshold of flipping times in Markov-kRR and propose an improved mechanism called MarkFixed-kRR, which can obtain more optimized utility by choosing the suitable threshold. Finally, extensive experiments demonstrate the efficiency and effectiveness of our proposed methods. Collapse

Topics: privacy marketing management differential privacy data quality local differential privacy

Methods: computational algorithm experiment lasso regression archival research survey

Prediction of hotel booking cancellations: Integration of machine learning and probability model based on interpretable feature interaction

2023 | Decision Support Systems | Citations: 0

Authors: Chen, Shuixia; Ngai, Eric W.T.; Ku, Yaoyao; Xu, Zeshui; Gou, Xunjie; Zhang, Chenxi

Abstract: Reliable hotel cancellation prediction can help establish appropriate operationa ... Expand

Abstract: Reliable hotel cancellation prediction can help establish appropriate operational strategies for hotel manage­ ment. In this sector, personal name records (PNR) data may be the most representative information source for prediction tasks. Despite the popularity of PNR, its inherent lack of availability has been commonly disregarded in the literature. Existing studies have directly input PNR into high-dimensional machine learning (ML) models to achieve cancellation predictions. Another type of model generates cancellation prediction based on the proba­ bility modeling of samples. In this study, we propose an interpretable feature interaction method to enrich the existing PNR information. Thereafter, we empirically assess the prediction performance of the two model classes. This study specifically determines whether or not the two methods can cross-fertilize each other to improve cancellation prediction. To do so, we propose a model integrating Bayesian networks (BNs) and Lasso regression for this prediction task. This study utilizes BNs for the probability model consistent with our correlated variables and dichotomous prediction setting. Moreover, we use a linear ML model (i.e., Lasso regression), given its ad­ vantages in reducing ineffective predictors and transparency for ranking feature importance. Empirical results show that the proposed integration model has better prediction performance, and the obtained BN estimators and interactive features are the most important predictors. This study contributes to the booking cancellation liter­ ature by proposing an interpretable feature interaction and a prediction method integrating two types of effective models. The obtained accurate and interpretable cancellation prediction further contributes to offering practical implications to hoteliers in managerial decision-making. Collapse

Topics: missing data decision making competitive advantage price management marketing management

Methods: lasso regression experiment computational algorithm robustness check parametric test

SOLVING THE DATA-DRIVEN NEWSVENDOR WITH ATTENTION TO TIME

2023 | European Conference On Information Systems | Citations: 0

Authors: Feddersen, Leif; Cleophas, Catherine

Abstract: Inventory management systems support firms in planning for an uncertain future ... Expand

Abstract: Inventory management systems support firms in planning for an uncertain future by using demand forecasts and optimization models to make restocking decisions. Recent work on the "data-driven" newsvendor found that incorporating machine learning (ML) can improve the success of inventory management by accounting for demand-driving information. However, ML methods are infamously hard to interpret, which may hinder their acceptance. To ameliorate this, we show how to apply an interpretable attention-based architecture, the Temporal Fusion Transformer (TFT), to the data-driven newsvendor problem. Our approach replicates and extends the original TFT time series forecasting method to the inventory management domain. We evaluate our method on two real-world retail datasets, each covering 260 perishable food items, and provide domain-specific benchmarks. The computational study illustrates TFT's interpretable predictions and their comparatively high accuracy. Our work aims to lay the groundwork for further design science research on transparency in human-AI collaboration in this domain. Collapse

Topics: human AI collaboration human computer interaction Python decision making accounting

Methods: time series analysis longitudinal research artificial neural network feature engineering machine learning

Theories: learning theory

IoT and Wearable Devices-Enhanced Information Provision of AR Glasses: A Multi-Modal Analysis in Aviation Industry

2023 | International Conference on Information Systems | Citations: 0

Authors: Han, Xuewen; Li, Ting; Xu, Sean Xin; Yang, Zherui; Yin, Zhitao; Zhang, Kunpeng

Abstract: While Augmented Reality (AR) glasses are now instrumental in industries for del ... Expand

Abstract: While Augmented Reality (AR) glasses are now instrumental in industries for delivering work-related information, the current one-size-fits-all information provision of AR glasses fails to cater to diverse workers' needs and environmental conditions. We propose a framework for harnessing Internet of thing (IoT) and wearable technology to improve the adaptability and customization of information provision by AR. As a preliminary exploration, this short paper develops a multi-modal data processing system for work performance classification in the aviation industry. Using machine learning algorithms for multi-modal feature extraction and classifier construction, this framework provides a more objective and consistent evaluation of work performance compared to singlemodal approaches. The proposed analytics architecture can provide valuable insights for other industries struggling to implement IoT and mixed reality. Collapse

Topics: smart wearable device mobile system internet of things individual characteristic ergonomics

Methods: oculometry machine learning support vector machine lasso regression robustness check

Can Conversations on Reddit Forecast Future Economic Uncertainty? An Interpretable Machine Learning Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: Jiang, Jinhang; Feng, Mei; Srinivasan, Karthik

Abstract: In recent years, social media has become an indispensable source of information ... Expand

Abstract: In recent years, social media has become an indispensable source of information through which public attitudes, opinions, and concerns can be studied and quantified. This paper proposes an interpretable machine learning framework for predicting the Equity Market-related Economic Uncertainty Index using features generated from a popular discussion forum on Reddit. Our framework consists of a series of custom preprocessing and analytics methods, including BERTopic for latent topic identification and regularized linear models. Using our framework, we evaluate explanatory models with different configurations over a large corpus of Reddit posts belonging to the personal finance category. Our analysis generates valuable insights about discussion topics on Reddit and their efficacy in accurately predicting future economic uncertainty. The study demonstrates the potential of using social media data and interpretable machine learning to inform economic forecasting research. Collapse

Topics: Reddit social media explainable artificial intelligence Python permutation feature importance

Methods: descriptive statistic time series analysis longitudinal research topic model experiment

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

Academic Entrepreneurship: Entrepreneurial Advisors and Their Advisees’ Outcomes

2023 | Organization Science | Citations: 0

Authors: Roche, Maria P.

Abstract: The transfer of complex knowledge and skills is difficult, often requiring inten ... Expand

Abstract: The transfer of complex knowledge and skills is difficult, often requiring intensive interaction and extensive periods of coworking between a mentor and mentee, which is particularly true in apprenticeship-like settings and on-the-job training. This paper studies a context that quintessentially describes this type of learning: the academic laboratory. I focus on ways a change in the attention of a principal investigator, moving to entrepreneurship, may influence knowledge transmission and skill development by examining the relationship of this change with their PhD students’ scientific productivity and careers. To do so, I rely on novel restricted-access data encompassing faculty and PhD students in computer sciences, engineering, and the life sciences who were active at an elite U.S. research university from 2001 to 2017. The results suggest a substantial negative association between a professor’s entrepreneurial activity and the short- and long-run publication output of the PhD students they train. Furthermore, I detect a decrease in students’ likelihood of becoming professors themselves but an increase in their likelihood of working for consulting firms on graduation. Finally, I provide evidence suggesting that changes in trainee development are the most feasible drivers of the results rather than changes in trainee research orientation, selection, or life cycle effects. Funding: This work was supported by the National Science Foundation [NSF SciSIP Dissertation Improvement Grant 1933387]. The author thanks the Harvard Business School Division of Research and Faculty Development for further financial support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1633. Collapse

Topics: entrepreneurship IT career productivity personality knowledge sharing

Methods: survey lasso regression longitudinal research difference in differences computational algorithm

#MyIBDHistory on Twitter: Identifying Disease Characteristics Using Personal Tweets

2023 | HICSS | Citations: 0

Authors: Stemmer, Maya; Ravid, Gilad; Parmet, Yisrael

Abstract: Inflammatory bowel disease (IBD) is usually classified into Crohn's disease (CD) ... Expand

Abstract: Inflammatory bowel disease (IBD) is usually classified into Crohn's disease (CD) or ulcerative colitis (UC). Inconclusive cases are diagnosed with IBD unclassified (IBD-U). In 2018, IBD patients shared their disease history on Twitter and signed their tweets with #MyIBDHistory. In this research, we analyzed those tweets and built a logistic regression classifier that predicts patients' IBD type. We constructed tabular classification features and assessed their importance using the regression coefficients and association rules. We identified key features that distinguished CD from UC and used the classifier to predict the disease type of IBD-U patients. Our results correlated with IBD-related research. The two most prominent features that tilted the classification towards CD were suffering from fistulas or nutrient deficiencies. We identified gender differences in disease perspective prior to diagnosis. The research shows that the personal information shared by patients on Twitter can enhance existing medical knowledge regarding their disease. Collapse

Topics: Twitter database system logistics management privacy application programming interface

Methods: logistic regression lasso regression association rule learning regression analysis method descriptive statistic

An AI-based Decision Support System for Predicting Mental Health Disorders

2023 | Information Systems Frontiers | Citations: 0

Authors: Tutun, Salih; Johnson, Marina E.; Ahmed, Abdulaziz; Albizri, Abdullah; Irgil, Sedat; Yesilkaya, Ilker; Ucar, Esma Nur; Sengun, Tanalp; Harfouche, Antoine

Abstract: Approximately one billion individuals suffer from mental health disorders, such ... Expand

Abstract: Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants’ answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy. Collapse

Topics: artificial intelligence decision support system explainable artificial intelligence information portal decision making

Methods: computational algorithm support vector machine design process lasso regression ridge regression

Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality

2023 | Information Systems Research | Citations: 0

Authors: Yang, Kai; Lau, Raymond Y. K.; Abbasi, Ahmed

Abstract: Analysts, managers, and policymakers are interested in predictive analytics capa ... Expand

Abstract: Analysts, managers, and policymakers are interested in predictive analytics capable of offering better foresight. It is generally accepted that in forecasting scenarios involving organizational policies or consumer decision making, personal characteristics, including personality, may be an important predictor of downstream outcomes. The inclusion of personality features in forecasting models has been hindered by the fact that traditional measurement mechanisms are often infeasible. Text-based personality detection has garnered attention because of the public availability of digital textual traces. However, the text machine learning space has bifurcated into two branches: feature-based methods relying on manually crafted human intuition, or deep learning language models that leverage big data and compute, the main commonality being that neither branch generates accurate personality assessments, thereby making personality measures infeasible for downstream forecasting applications. In this study, we propose DeepPerson, a design artifact for text-based personality detection that bridges these two branches by leveraging concepts from relevant psycholinguistic theories in conjunction with advanced deep learning strategies. DeepPerson incorporates novel transfer learning and hierarchical attention network methods that use psychological concepts and data augmentation in conjunction with person-level linguistic information. We evaluate the utility of the proposed artifact using an extensive design evaluation on three personality data sets in comparison with state-of-the-art methods proposed in academia and industry. DeepPerson can improve detection of personality dimensions by 10–20 percentage points relative to the best comparison methods. Using case studies in the finance and health domains, we show that more accurate text-based personality detection can translate into significant improvements in downstream applications such as forecasting future firm performance or predicting pandemic infection rates. Our findings have important implications for research at the intersection of design and data science, and practical implications for managers focused on enabling, producing, or consuming predictive analytics.History: Olivia Sheng, Senior Editor; Huimin Zhao, Associate Editor.Funding: This work was supported by Oracle for Research (NLP for the Greater Good) and the U.S. National Science Foundation’s Division of Information and Intelligent Systems [Grants BDS-1636933 and IIS-1816504]. The work was also partly supported by the Research Grants Council of the Hong Kong Special Administrative Region [Project: CityU 11507219] and the City University of Hong Kong SRG [Project: 7005780].Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1111. Collapse

Topics: personality Twitter social media pandemic Google+

Methods: machine learning BERT design science deep learning computational algorithm

Theories: big five model