Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing

2023 | Information Systems Research | Citations: 0

Authors: Bauer, Kevin; von Zahn, Moritz; Hinz, Oliver

Abstract: Because of a growing number of initiatives and regulations, predictions of moder ... Expand

Abstract: Because of a growing number of initiatives and regulations, predictions of modern artificial intelligence (AI) systems increasingly come with explanations about why they behave the way they do. In this paper, we explore the impact of feature-based explanations on users’ information processing. We designed two complementary empirical studies where participants either made incentivized decisions on their own, with the aid of opaque predictions, or with explained predictions. In Study 1, laypeople engaged in the deliberately abstract investment game task. In Study 2, experts from the real estate industry estimated listing prices for real German apartments. Our results indicate that the provision of feature-based explanations paves the way for AI systems to reshape users’ sense making of information and understanding of the world around them. Specifically, explanations change users’ situational weighting of available information and evoke mental model adjustments. Crucially, mental model adjustments are subject to the confirmation bias so that misconceptions can persist and even accumulate, possibly leading to suboptimal or biased decisions. Additionally, mental model adjustments create spillover effects that alter user behavior in related yet disparate domains. Overall, this paper provides important insights into potential downstream consequences of the broad employment of modern explainable AI methods. In particular, side effects of mental model adjustments present a potential risk of manipulating user behavior, promoting discriminatory inclinations, and increasing noise in decision making. Our findings may inform the refinement of current efforts of companies building AI systems and regulators that aim to mitigate problems associated with the black-box nature of many modern AI systems. History: Alessandro Acquisti, senior editor; Jason Chan, associate editor. Funding: This work was supported by the Deutsche Forschungsgemeinschaft (DFG) (Projek 449023539), Volkswagen Foundation (ML2MT), and LeibnizInstitute for Financial Research SAFE. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1199. Collapse

Topics: explainable artificial intelligence artificial intelligence shapley additive explanation local interpretable model agnostic explanation decision making

Methods: F-test regression analysis method experiment field study robustness check

Decision support systems in crowdfunding: A fuzzy cognitive maps (FCM) approach

2023 | Decision Support Systems | Citations: 0

Authors: Borrero-Domínguez, Cinta; Escobar-Rodríguez, Tomás

Abstract: Crowdfunding as an alternative source of finance to banking is the subject of nu ... Expand

Abstract: Crowdfunding as an alternative source of finance to banking is the subject of numerous research works. This research proposes the guidelines of a Decision Support System (DSS) in the area of crowdfunding based on a Fuzzy Cognitive Maps (FCM) approach. A framework consisting of three phases (ten steps) has been proposed to design the guidelines of a DSS in crowdfunding. The first phase focusses in the construction of the FCM. FCMbased scenarios have been built in the second phase. Finally, in the third phase the guidelines of the DSS in crowdfunding have been defined. Results show that DSS using FCM-based scenarios could be useful tools to promoters, funders and managers of crowdfunding projects. Collapse

Topics: crowdsourcing crowdfunding decision support system decision making innovation management

Methods: simulation

A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction

2023 | Information Systems Research | Citations: 0

Authors: Chen, Gang; Xiao, Shuaiyong; Zhang, Chenghong; Zhao, Huimin

Abstract: As artificial intelligence and digitalization technologies are flourishing real- ... Expand

Abstract: As artificial intelligence and digitalization technologies are flourishing real-time, online interaction–based commercial modes, exploiting customers’ purchase intention implied in online interaction processes may foster huge business opportunities. In this study, we target the task of voice chat–based customer response prediction in an emerging online interaction–based commercial mode, the invite-online-and-experience-in-store mode. Prior research shows that satisfaction, which can be revealed by the discrepancy between prior expectation and actual experience, is a key factor to disentangle customers’ purchase intention, whereas black-box deep learning methods empirically promise us advantageous capabilities in dealing with complex voice data, for example, text and audio information incorporated in voice chat. To this end, we propose a theory-driven deep learning method that enables us to (1) learn customers’ personalized product preferences and dynamic satisfaction in the absence of their profile information, (2) model customers’ actual experiences based on multiview voice chat information in an interlaced way, and (3) enhance the customer response prediction performance of a black-box deep learning model with theory-driven dynamic satisfaction. Empirical evaluation results demonstrate the advantageous prediction performance of our proposed method over state-of-the-art deep learning alternatives. Investigation of cumulative satisfaction reveals the collaborative predictive roles of theory-driven dynamic satisfaction and deep representation features for customer response prediction. Explanatory analysis further renders insights into customers’ personalized preferences and dynamic satisfaction for key product attributes. History: Yong Tan, Senior Editor; Jingjing Zhang, Associate Editor. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71971067 and 72271059] and the China Postdoctoral Science Foundation [Grant 2022M722394]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1196. Collapse

Topics: customer satisfaction willingness to pay decision making marketing information system customer service

Methods: deep learning experiment machine learning BERT theory development

Blockchain-based Governance, Risk Management, and Compliance for Fractional Ownership: Design and Implementation of A Decentralized Autonomous Agent System

2023 | HICSS | Citations: 0

Authors: Cu, Mina; Peko, Gabrielle; Chan, Johnny; Sundaram, David

Abstract: Fractional ownership makes homeownership more affordable. But there are challeng ... Expand

Abstract: Fractional ownership makes homeownership more affordable. But there are challenges in a fractional ownership real estate transaction (FORET) regarding governance, risk management and compliance (GRC) processes. Centralized GRC solutions are less effective in managing the tiered structure of communications in a FORET, which can lead to principal-agent problems such as information asymmetry, risk aversion, and moral hazard. In this research we investigate how these principal-agent problems in FORET could be mitigated. Using an agency theory perspective, we adopt a design science multimethodological research approach. We propose conceptual and system artefacts to support the design and implementation of a decentralized autonomous agent system. These artefacts deliver a formal problem representation structure related to centralized GRC in fractional ownership. We illustrate our solution with a system prototype and implementation. We evaluate the research outputs and compare them with existing GRC systems. This paper contributes to the understanding of GRC in supporting fractional ownership decision making. Collapse

Topics: blockchain decision making cloud computing smart contract moral hazard

Methods: design artifact design artifact testing design science field study theory development

Theories: agency theory lemon market theory

The Sustainable Stewardship Management Model for Green IS

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Ekman, Peter; Rondell, Jimmie; Erixon, Cecilia; Anastasiadou, Elena; Berglind, Magnus; Dahlin, Peter; Holmstedt, Matthias; Thompson, Steve

Abstract: Triple bottom-line challenges are increasingly related to the long-term surviva ... Expand

Abstract: Triple bottom-line challenges are increasingly related to the long-term survival of companies. Both researchers and businesses understand that the transformation towards a sustainable future is dependent on green IS initiatives. However, companies base their decisions on business cases when investing in IS. Many of the required changes that green IS can offer-especially when it comes to new systems that require new business models-are not captured by business cases. This paper presents a model that widens the perspective on value creation. The presented Sustainable Stewardship Management Model (SSMM) allows companies that strive for a stewardship role to evaluate both existing and new forms of green IS by combining casual and effectual decision-making. The model is a result of a collaboration with several large Swedish commercial real estate companies, their tenants, and a non-governmental organization (NGO). The model will be tested in a follow-up project. Collapse

Topics: green IS value creation IT investment smart city decision making

The Unfolding of Digital Transformation in Pre-Digital Companies: A Meta-Case Analysis

2023 | International Conference on Information Systems | Citations: 0

Authors: Haskamp, Thomas; Hund, Axel; Raabe, Jun-Patrick; Uebernickel, Falk

Abstract: ... Expand

Abstract: Collapse

Topics: digital transformation innovation management digital innovation resistance to change organizational change

Methods: case study qualitative coding literature sample synthesis meta analysis

Beyond Binary Boundaries – Exploring the Potential of Categorical Variables in Counterfactual Explanations

2023 | International Conference on Business Informatics | Citations: 0

Authors: Hühn, Philipp; Hofer, Simon

Abstract: The rapid development of Artificial Intelligence (AI) has brought about signifi ... Expand

Abstract: The rapid development of Artificial Intelligence (AI) has brought about significant changes in various industries. However, the opaqueness of AI systems leads to blind trust, distrust, or even avoidance of the systems altogether. Against this background, counterfactual explanation methods generate humanunderstandable explanations of how an AI system arrives at a given output. However, the thorough integration of categorical variables in the generation of counterfactual explanations remains a challenging research problem. In this paper, we investigate by means of a user study how a human-like handling of categorical variables affects users' perception of explanations for a state-of-theart counterfactual explanation approach. The results show that using a humanlike handling of categorical variables leads to the generation of explanations preferred by users. Moreover, we find that the handling of categorical variables affects users' perceptions of the concreteness, coherence, and relevance of the generated counterfactual explanations. Collapse

Topics: explainable artificial intelligence artificial intelligence electronic market

Methods: gradient boosting computational algorithm Student's t-test survey design artifact

Just Diverse Among Themselves: How Does Negative Performance Feedback Affect Boards’ Expertise vs. Ascriptive Diversity?

2023 | Organization Science | Citations: 0

Authors: Jung, HeeJung; Lee, Yonghoon G.; Park, Sun Hyun

Abstract: We investigate how negative performance feedback affects board diversity, which ... Expand

Abstract: We investigate how negative performance feedback affects board diversity, which is instrumental in shaping a firm’s strategic change. When a firm underperforms compared with its aspiration, its board is motivated to promptly address the underperformance. The board needs to not only help search for strategic alternatives but also quickly build consensus around its strategic reorientation. These two motivations lead the board to value two dimensions of diversity among its members differently. On the one hand, to understand the problem of underperformance and find a solution, the board is motivated to seek new expertise, avoiding redundancy in the pool of expertise already represented in the boardroom. This results in a higher level of diversity in director expertise. On the other hand, the urgent need to build consensus prompts the board to value trust and solidarity and to avoid potential conflict among directors. Because people perceive others with similar ascriptive backgrounds as trustworthy, changes in the board of an underperforming firm are likely to yield a lower level of diversity in its members’ ascriptive backgrounds. These changes in board are affected by the committee chairs of the board whose power and influence are significant in the boardroom. Analyses of the boards of 733 U.S. listed manufacturing firms show that when a firm underperforms compared with its aspirations, it increases the board expertise diversity, but decreases the board ascriptive diversity. When chairs on the board are gender or racial minorities, the negative association between underperformance and the board ascriptive diversity is weakened. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2022.1595. Collapse

Topics: research and development organizational productivity accounting corporate governance database system

Methods: statistical hypothesis test theory development qualitative interview descriptive statistic ordinary least square

Tackling the Accuracy-Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal

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

Authors: Kucklick, Jan-Peter; Müller, Oliver

Abstract: Deep learning models fuel many modern decision support systems, because they typ ... Expand

Abstract: Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows extending the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this article, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning, which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density. Collapse

Topics: Bing explainable artificial intelligence shapley additive explanation video conference price management

Methods: principal component analysis data transformation experiment artificial neural network regression analysis method

Theories: game theory

Beyond the First Offer: Decoding Negotiation Openings and Their Impact on Economic and Subjective Outcomes

2023 | Group Decision and Negotiation | Citations: 0

Authors: Lipp, Wolfram E.; Smolinski, Remigiusz; Kesting, Peter

Abstract: First offers play a significant role in negotiations as they anchor negotiators’ ... Expand

Abstract: First offers play a significant role in negotiations as they anchor negotiators’ perceptions and influence negotiation outcomes in favor of the first-offer proposer. However, negotiation is a joint decision-making process in which a first offer is typically succeeded by a counteroffer. The impact of a counteroffer has not yet been systematically researched. We propose that a counteroffer influences negotiation outcomes like a first offer. In addition, we conceptualize the “anchor zone” as the distance between the first offer and the counteroffer. We theorize that the anchor zone influences negotiation outcomes because it captures additional information compared to a single offer. To test our hypotheses, we conducted two studies: Study 1 was a vignette study (n = 190) in which participants reacted to a counteroffer that they received based on their first offer as part of a simulated negotiation. Study 2 was an online experiment (n = 212) in which participants negotiated by exchanging offers with no further communication. Our analysis suggests that the counteroffer is a significant predictor of economic outcomes. Thus, it works like a first offer, but with a lower impact. In addition, the anchor zone predicted how far the final agreement was from the first offer. Furthermore, we found that the third offer, the average concessions, and the number of offers mediated the effects of the counteroffer and anchor zone on economic outcomes. Finally, we discovered that a more aggressive counteroffer reduced the subjective value of both negotiators. Collapse

Topics: Apple iPhone decision making data quality

Methods: experiment statistical hypothesis test simulation statistical power analysis regression analysis method

Theories: behavioral economics