Risk and the future of AI: Algorithmic bias, data colonialism, and marginalization

2023 | Information and Organization | Citations: 0

Authors: Arora, A.; Barrett, M.; Lee, E.; Oborn, E.; Prince, K.

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Topics: artificial intelligence algorithmic bias responsible artificial intelligence legal context outsourcing

Methods: computational algorithm design methodology generative adversarial network

Exploring Digital Social Norms Nudges in E-Grocery: Typical Consumer Testimonials with a Warm Glow

2023 | International Conference on Business Informatics | Citations: 0

Authors: Gottschewski-Meyer, Phillip Oliver; Lüddemann, Nicole; Landwehr, Marvin Auf der; Viebahn, Christoph von

Abstract: Digitization offers several possibilities to alter consumer decisions to suppor ... Expand

Abstract: Digitization offers several possibilities to alter consumer decisions to support social concerns. The objective of this study is to examine the impact of personalized digital social norm nudges on consumer decisions enriched with the theory of warm glow on e-grocery buying decisions with the aim of supporting social projects. Specific pro-social behaviors targeted were supporting fair payment of the producers of grocery goods, social inclusion projects and initiatives against poverty by deciding for a specific choice option. A between-subjects experiment was performed with the help of a questionnaire using a mock-up mobile grocery store to measure product choices. Results showed that claims supporting pro-social initiatives have a significant impact on buying decisions. Perceived product recommendation influenced our model positively, while we had a negative price impact. The study suggests that warm glow theory and enriched social norm nudges are effective tools for behavior change towards social initiatives. Collapse

Topics: social norm crowdsourcing web page social presence artificial intelligence

Methods: experiment survey experimental group statistical hypothesis test Levene's test

Are Deep Learning-Generated Social Media Profiles Indistinguishable from Real Profiles?

2023 | HICSS | Citations: 0

Authors: Rossi, S.; Kwon, Y.; Auglend, O.; Mukkamala, R.; Rossi, M.; Thatcher, J.

Abstract: In recent years, deep learning methods have become increasingly capable of gene ... Expand

Abstract: In recent years, deep learning methods have become increasingly capable of generating near photorealistic pictures and humanlike text up to the point that humans can no longer recognize what is realand what is AI-generated. Concerningly, there is evidence that some of these methods have already been adopted to produce fake social media profiles and content. We hypothesize that these advances have made detecting generated fake social media content in the feed extremely difficult, if not impossible, for the average user of social media. This paper presents the results of an experiment where 375 participants attempted to label real and generated profiles and posts in a simulated social media feed. The results support our hypothesis and suggest that even fully-generated fake profiles with posts written by an advanced text generator are difficult for humans to identify. Collapse

Topics: social media Twitter social network armed conflict website

Methods: experiment deep learning survey descriptive statistic stochastic model

Will they take this offer? A machine learning price elasticity model for predicting upselling acceptance of premium airline seating

2023 | Information & Management | Citations: 0

Authors: Thirumuruganathan, Saravanan; Emadi, Noora Al; Jung, Soon-gyo; Salminen, Joni; Robillos, Dianne Ramirez; Jansen, Bernard J.

Abstract: Employing customer information from one of the world’s largest airline companies ... Expand

Abstract: Employing customer information from one of the world’s largest airline companies, we develop a price elasticity model (PREM) using machine learning to identify customers likely to purchase an upgrade offer from economy to premium class and predict a customer’s acceptable price range. A simulation of 64.3 million flight bookings and 14.1 million email offers over three years mirroring actual data indicates that PREM implementation results in approximately 1.12 million (7.94%) fewer non-relevant customer email messages, a predicted increase of 72,200 (37.2%) offers accepted, and an estimated $72.2 million (37.2%) of increased revenue. Our results illustrate the potential of automated pricing information and targeting marketing messages for upselling acceptance. We also identified three customer segments: (1) Never Upgrades are those who never take the upgrade offer, (2) Upgrade Lovers are those who generally upgrade, and (3) Upgrade Lover Lookalikes have no historical record but fit the profile of those that tend to upgrade. We discuss the implications for airline companies and related travel and tourism industries. Collapse

Topics: electronic mail marketing management price management customer satisfaction application programming interface

Methods: autoencoder data transformation machine learning experiment principal component analysis

Theories: optimization theory decision theory

Unbox the Black-Box: Predict and Interpret YouTube Viewership Using Deep Learning

2023 | Journal of Management Information Systems | Citations: 0

Authors: Xie, Jiaheng; Chai, Yidong; Liu, Xiao

Abstract: As video-sharing sites emerge as a critical part of the social media landscape, ... Expand

Abstract: As video-sharing sites emerge as a critical part of the social media landscape, video viewership prediction becomes essential for content creators and businesses to optimize influence and marketing outreach with minimum budgets. Although deep learning champions viewership prediction, it lacks interpretability, which is required by regulators and is fundamental to the prioritization of the video production process and promoting trust in algorithms. Existing interpretable predictive models face the challenges of imprecise interpretation and negligence of unstructured data. Following the design-science paradigm, we propose a novel Precise Wide-and-Deep Learning (PrecWD) to accurately predict viewership with unstructured video data and well-established features while precisely interpreting feature effects. PrecWD's prediction outperforms benchmarks in two case studies and achieves superior interpretability in two user studies. We contribute to IS knowledge base by enabling precise interpretability in video-based predictive analytics and contribute nascent design theory with generalizable model design principles. Our system is deployable to improve video-based social media presence. Collapse

Topics: YouTube web page explainable artificial intelligence shapley additive explanation analytical information system

Methods: deep learning case study machine learning design principle computational algorithm

ProcessGAN: Supporting the creation of business process improvement ideas through generative machine learning

2023 | Decision Support Systems | Citations: 0

Authors: van Dun, Christopher; Moder, Linda; Kratsch, Wolfgang; Röglinger, Maximilian

Abstract: Business processes are a key driver of organizational success, which is why busi ... Expand

Abstract: Business processes are a key driver of organizational success, which is why business process improvement (BPI) is a central activity of business process management. Despite an abundance of approaches, BPI as a creative task is time-consuming and labour-intensive. Most importantly, its level of computational support is low. The few computational BPI approaches hardly leverage the opportunities brought about by computational creativity, neglect process data, and rely on rather rigid improvement patterns. Given the increasing amount of process data in the form of event logs and the uptake of generative machine learning for automating creative tasks in various domains, there is huge potential for BPI. Hence, following the design science research paradigm, we specified, implemented, and evaluated ProcessGAN, a novel computational BPI approach based on generative adversarial networks that supports the creation of BPI ideas. Our evaluation shows that ProcessGAN improves the creativity of process designers, particularly the originality of BPI ideas, and shapes up useful in real-world settings. Moreover, ProcessGAN is the first approach to combine BPI and computational creativity. Collapse

Topics: process mining business process improvement open source artificial intelligence source code

Methods: design artifact event log computational algorithm business process modeling design science

Cross-Lingual Cybersecurity Analytics in the International Dark Web with Adversarial Deep Representation Learning

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Ebrahimi, Mohammadreza; Chai, Yidong; Samtani, Sagar; Chen, Hsinchun

Abstract: International dark web platforms operating within multiple geopolitical regions ... Expand

Abstract: International dark web platforms operating within multiple geopolitical regions and languages host a myriad of hacker assets such as malware, hacking tools, hacking tutorials, and malicious source code. Cybersecurity analytics organizations employ machine learning models trained on human-labeled data to automatically detect these assets and bolster their situational awareness. However, the lack of human-labeled training data is prohibitive when analyzing foreign-language dark web content. In this research note, we adopt the computational design science paradigm to develop a novel IT artifact for cross-lingual hacker asset detection (CLHAD). CLHAD automatically leverages the knowledge learned from English content to detect hacker assets in non-English dark web platforms. CLHAD encompasses a novel Adversarial deep representation learning (ADREL) method, which generates multilingual text representations using generative adversarial networks (GANs). Drawing upon the state of the art in cross-lingual knowledge transfer, ADREL is a novel approach to automatically extract transferable text representations and facilitate the analysis of multilingual content. We evaluate CLHAD on Russian, French, and Italian dark web platforms and demonstrate its practical utility in hacker asset profiling, and conduct a proof-of-concept case study. Our analysis suggests that cybersecurity managers may benefit more from focusing on Russian to identify sophisticated hacking assets. In contrast, financial hacker assets are scattered among several dominant dark web languages. Managerial insights for security managers are discussed at operational and strategic levels. Collapse

Topics: darknet IT security knowledge sharing shapley additive explanation spoofing

Methods: machine learning design science design artifact case study deep learning

A Machine Learning Model Marketplace to Overcome Low Price Elasticity of Sales Forecasting

2022 | International Conference on Business Informatics | Citations: 0

Authors: Hütsch, Marek

Abstract: Machine learning (ML) sales forecasting applications occupy a special position i ... Expand

Abstract: Machine learning (ML) sales forecasting applications occupy a special position in industry and retail, as used in numerous optimization activities, such as price optimization. While optimizing prices, sales forecasting tools require elasticity regarding the optimization objective. When ML methods are used for sales forecasting, there must be sufficient data to learn the proper elasticity requirements. To overcome the problem of low price elasticity of sales forecasts, when historical price-change data are scarce, we present a sales forecasting model marketplace in which trained ML models are shared across companies. Evaluations of a design science project with 43 retail stores show that a model marketplace with neural networks and dropout profiles achieves good forecasting accuracy and high price elasticity. With these results, companies can implement automated optimization algorithms while avoiding extant cumbersome stovepiped methods. Collapse

Topics: privacy Sustainable Development Goal Python

Methods: case study artificial neural network computational algorithm design science operations research

Learning Faces to Predict Matching Probability in an Online Matching Platform

2022 | International Conference on Information Systems | Citations: 0

Authors: Kwon, Soonjae; Park, Sunghyuk; Lee, Gene Moo; Lee, Dongwon

Abstract: With the increasing use of online matching platforms, predicting matching proba ... Expand

Abstract: With the increasing use of online matching platforms, predicting matching probability between users is crucial for efficient market design. Although previous studies have constructed various visual features to predict matching probability, facial features, which are important in online matching, have not been widely used. We find that deep learning-enabled facial features can significantly enhance the prediction accuracy of a user's partner preferences from the individual rating prediction analysis in an online dating market. We also build prediction models for each gender and use prior theories to explain different contributing factors of the models. Furthermore, we propose a novel method to visually interpret facial features using the generative adversarial network (GAN). Our work contributes the literature by providing a framework to develop and interpret facial features to investigate underlying mechanisms in online matching markets. Moreover, matching platforms can predict matching probability more accurately for better market design and recommender systems. Collapse

Topics: online dating shapley additive explanation online labor market Google+ user behavior

Methods: StyleGAN deep learning generative adversarial network grounded theory artificial neural network

Tune Down the Misinformation, Please: Generating Corrective Messages for COVID-19 Misinformation

2022 | HICSS | Citations: 0

Authors: Meyer, Dylan; Tao, Jie; Kris, Alison

Abstract: The ongoing COVID-19 pandemic drastically changed our lives in multiple aspects, ... Expand

Abstract: The ongoing COVID-19 pandemic drastically changed our lives in multiple aspects, one of which is the reliance on social media during quarantine, both for social interaction and information-seeking purposes. However, the wide dissemination of misinformation on social media has impacted public health negatively. Previous studies on COVID-19 misinformation mainly focused on exploration of impacts and explanation of motivations, with few exceptions. In this study, we propose an analytical pipeline that generates corrective messages toward COVID-19 misinformation in a semiautomatic fashion, and then evaluate it against a large amount of data. Both the automated and manual evaluation results suggest the efficiency of the proposed pipeline, which can be used in combination with human intelligence by individuals and public health organizations in fighting COVID-19 misinformation. Collapse

Topics: misinformation Twitter social media covid 19 fake news

Methods: machine learning BERT transformer model experiment term frequency–inverse document frequency