581 research outputs found

    Disentangling and Operationalizing AI Fairness at LinkedIn

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    Operationalizing AI fairness at LinkedIn's scale is challenging not only because there are multiple mutually incompatible definitions of fairness but also because determining what is fair depends on the specifics and context of the product where AI is deployed. Moreover, AI practitioners need clarity on what fairness expectations need to be addressed at the AI level. In this paper, we present the evolving AI fairness framework used at LinkedIn to address these three challenges. The framework disentangles AI fairness by separating out equal treatment and equitable product expectations. Rather than imposing a trade-off between these two commonly opposing interpretations of fairness, the framework provides clear guidelines for operationalizing equal AI treatment complemented with a product equity strategy. This paper focuses on the equal AI treatment component of LinkedIn's AI fairness framework, shares the principles that support it, and illustrates their application through a case study. We hope this paper will encourage other big tech companies to join us in sharing their approach to operationalizing AI fairness at scale, so that together we can keep advancing this constantly evolving field

    Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability

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    Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far. In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs. We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes. We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research

    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    Finetuning Analytics Information Systems for a Better Understanding of Users : Evidence of Personification Bias on Multiple Digital Channels

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    Although the effect of hyperparameters on algorithmic outputs is well known in machine learning, the effects of hyperparameters on information systems that produce user or customer segments are relatively unexplored. This research investigates the effect of varying the number of user segments on the personification of user engagement data in a real analytics information system, employing the concept of persona. We increment the number of personas from 5 to 15 for a total of 330 personas and 33 persona generations. We then examine the effect of changing the hyperparameter on the gender, age, nationality, and combined gender-age-nationality representation of the user population. The results show that despite using the same data and algorithm, varying the number of personas strongly biases the information system’s personification of the user population. The hyperparameter selection for the 990 total personas results in an average deviation of 54.5% for gender, 42.9% for age, 28.9% for nationality, and 40.5% for gender-age-nationality. A repeated analysis of two other organizations shows similar results for all attributes. The deviation occurred for all organizations on all platforms for all attributes, as high as 90.9% in some cases. The results imply that decision makers using analytics information systems should be aware of the effect of hyperparameters on the set of user or customer segments they are exposed to. Organizations looking to effectively use persona analytics systems must be wary that altering the number of personas could substantially change the results, leading to drastically different interpretations about the actual user base.© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

    The Impact of Artificial Intelligence on Strategic and Operational Decision Making

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    openEffective decision making lies at the core of organizational success. In the era of digital transformation, businesses are increasingly adopting data-driven approaches to gain a competitive advantage. According to existing literature, Artificial Intelligence (AI) represents a significant advancement in this area, with the ability to analyze large volumes of data, identify patterns, make accurate predictions, and provide decision support to organizations. This study aims to explore the impact of AI technologies on different levels of organizational decision making. By separating these decisions into strategic and operational according to their properties, the study provides a more comprehensive understanding of the feasibility, current adoption rates, and barriers hindering AI implementation in organizational decision making

    The Capability of E-reviews in Online Shopping. Integration of the PLS- SEM and ANN Method

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    Purpose: The aim of this study is to investigate the impact of e-review on iGen's propensity to purchase online. Especially, it can be better understood by dissecting the relationship among 3 variables of e-review (review valence, quantity of e-review and quality of e-review), e-satisfaction, and intention to buy.   Theoretical framework: This study classifies e-reviews according to their valence, quantity, and quality based on the study of Khammash (2008).   Design/methodology/approach: The PLS-SEM method was used to analyze data collected from online surveys administered to a sample of 222 iGen in Ho Chi Minh City to assess the hypotheses behind the study. Additionally, the Artificial Neural Network technique was used to separate SEM predictors that were relatively important.   Findings:  There are three results from the investigation: It has been found that (1) e-satisfaction is positively affected by valence, (2) e-satisfaction is generally increased with the high quality of e-review, but the quantity of e-review does not necessarily affect customers' e-satisfaction, and (3) e-satisfaction given in the context of an e-commerce platform has a strong effect on customers' online shopping intention. This study sheds new light on iGen's online buying habits and offers valuable management implications for iGen, online merchants, and e-commerce sites.   Research, Practical & Social implications:  E-reviews have become a significant factor in determining consumers' online purchase decisions. They also assist iGen in understanding how a qualified e-review—one that is clear, understandable, helpful, and has enough justification to support the opinions—will be advantageous for other consumers who wish to shop online.   Originality/value:  Provides the theory of e-review and its role in the online business environment. In addition, understand more about the behavior of igen, an age with a huge amount of spending on an online shopping platform

    2023- The Twenty-seventh Annual Symposium of Student Scholars

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    The full program book from the Twenty-seventh Annual Symposium of Student Scholars, held on April 18-21, 2023. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1027/thumbnail.jp

    Representation Learning Methods for Sequential Information in Marketing and Customer Level Transactions

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    The rapid growth of data generated by businesses has surpassed human capabilities to produce actionable insights. Modern marketing applications depend on vast amounts of customer labelled data and supervised machine learning algorithms to predict customer behaviour and their potential next actions. However, this process requires significant effort in data pre-processing and the involvement of domain experts, which can be costly and time-consuming. This work reviews representation learning techniques as an alternative approach to feature engineering, aiming to eliminate the need for hand-crafted features and accelerate the process of extracting insights from data. Techniques such as Bayesian neural networks, general embeddings, and encoding-decoding architectures are explored to compress information obtained directly from raw input data into a dense probabilistic space. This thesis introduces the necessary technical aspects of neural networks and representation learning, from traditional methods like principal component analysis (PCA) and embeddings, to latent variable and generative methods that use deep neural networks, such as variational auto-encoders and Bayesian neural networks. It also explores the theoretical background of survival analysis and recommender systems, which serve as the foundation for the applications presented in this work to predict when individuals are likely to stop their relationship with businesses in a non-contractual settings or which items individuals are the most likely to interact with in their next purchase. Experiments conducted on real-world retail and benchmark datasets demonstrate comparable results in terms of predictive performance and superior computational efficiency when compared to existing methods

    Evaluating Copyright Protection in the Data-Driven Era: Centering on Motion Picture\u27s Past and Future

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    Since the 1910s, Hollywood has measured audience preferences with rough industry-created methods. In the 1940s, scientific audience research led by George Gallup started to conduct film audience surveys with traditional statistical and psychological methods. However, the quantity, quality, and speed were limited. Things dramatically changed in the internet age. The prevalence of digital data increases the instantaneousness, convenience, width, and depth of collecting audience and content data. Advanced data and AI technologies have also allowed machines to provide filmmakers with ideas or even make human-like expressions. This brings new copyright challenges in the data-driven era. Massive amounts of text and data are the premise of text and data mining (TDM), as well as the admission ticket to access machine learning technologies. Given the high and uncertain copyright violation risks in the data-driven creation process, whoever controls the copyrighted film materials can monopolize the data and AI technologies to create motion pictures in the data-driven era. Considering that copyright shall not be the gatekeeper to new technological uses that do not impair the original uses of copyrighted works in the existing markets, this study proposes to create a TDM and model training limitations or exceptions to copyrights and recommends the Singapore legislative model. Motion pictures, as public entertainment media, have inherently limited creative choices. Identifying data-driven works’ human original expression components is also challenging. This study proposes establishing a voluntarily negotiated license institution backed up by a compulsory license to enable other filmmakers to reuse film materials in new motion pictures. The film material’s degree of human original authorship certified by film artists’ guilds shall be a crucial factor in deciding the compulsory license’s royalty rate and terms to encourage retaining human artists. This study argues that international and domestic policymakers should enjoy broad discretion to qualify data-driven work’s copyright protection because data-driven work is a new category of work. It would be too late to wait until ubiquitous data-driven works block human creative freedom and floods of data-driven work copyright litigations overwhelm the judicial systems

    Tourists’ Personal Development Through Participatory Consumer-Generated Content

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    The paper seeks to investigate key factors influencing the personal development of tourists. This study examines the relationship between participatory consumer-generated content and tourists’ capabilities, emotions, and skills, as well as the moderating effect of previous tourists’ experiences. To evaluate the research model, 301 valid responses were examined using the PLS-SEM technique. The empirical findings showed that participatory consumer-generated content positively relates to tourists’ capabilities, emotions, and skills. Moreover, previous tourists’ experiences moderate the relationships of participatory consumer-generated content with tourists’ capabilities and skills; however, previous tourists’ experiences have no moderation effect on tourists’ emotions. Thus, our paper\u27s findings offer valuable contributions to theory and practice. Practitioners and authorities should stimulate users to share their tourism experiences and take the initiative to share easily traceable and searchable data. Moreover, businesses should implement activities that encourage tourists to share their experiences as soon as possible and make travel and tourism websites and social media platforms readily available
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