Beauty’s in the AI of the Beholder: How AI Anchors Subjective and Objective Predictions

Researchers increasingly acknowledge that algorithms can exhibit bias, but artificial intelligence (AI) is increasingly integrated into the organizational decision-making process. How does biased AI shape human choices? We consider a sequential AI-human decision that mirrors organizational decisions; an automated system provides a score and then a human decides a score using their discretion. We conduct an AMT survey and ask participants to assign one of two types of scores: a subjective, context-dependent measure (Beauty) and objective, observer-independent measure (Age). Participants are either shown the AI score, shown the AI score and its error, or not shown the AI score. We find that participants without knowledge of the AI score do not exhibit bias; however, knowing the AI scores for the subjective measure induces bias in the participants’ scores due to the anchoring effect. Although participants’ scores do not display bias, participants who receive information about the AI error rates devalue the AI score and reduce their error. This study makes several contributions to the information systems literature. First, this paper provides a novel way to discuss artificial intelligence bias by distinguishing between subjective and objective measures. Second, this paper highlights the potential spillover effects from algorithmic bias into human decisions. If biased artificial intelligence anchors human decisions, then it can induce bias into previously unbiased scores. Third, we examine a method to encourage participants to reduce their reliance on the artificial intelligence, reporting the error rate, and find evidence that it is effective for the objective measure

When Do Likes Create Bias?

The rise of online communities has ushered in a new era of content sharing with platforms that serve many functions and overcome the geographic and synchronous limitations of traditional word-of-mouth communications. Community-based question answering sites (CQA) have emerged as convenient platforms for users to exchange knowledge and opinions with others. Research on CQA has primarily focused on engaging members to voluntarily contribute to these communities. Helpfulness ratings and “likes” are one mechanism platforms can use to engage members, but these subjective evaluations can also create bias. In this ERF paper, the elaboration likelihood model is applied to better understand when bias can occur with these platforms. An experimental design and a planned data collection are reported

The Tangled Web: Studying Online Fake News

Fake news has become a ubiquitous and extremely worrying phenomenon, capturing the attention of academics, governments, businesses, media, and the general public. Despite this notoriety, many questions remain to be answered about the generation, diffusion, consumption, and impacts of fake news that are spread through social media and online communities. A nascent body of IS research is emerging that addresses some of these questions. In this panel, we aim to motivate further IS research and produce an agenda by highlighting some of the important issues that need to be discussed with regard to fake news. We examine how IS scholarship can address these issues by drawing on its existing body of knowledge as well as considering less-studied but potentially fruitful areas of research

When Popularity Meets Position

Popularity information is usually thought to have a great impact on individual’s decision making and choice. However, most of the websites are displaying products by its popularity. This could potentially result in the popularity effect being overestimated because popularity is confounded with position when they are sorted in the descending order. In this paper, we try to fill this gap in the literature by bridging together popularity effect and position effect to understand whether popularity effect overcomes position effect. By conducting a series of lab experiments, our results suggest that popularity effect is overestimated in prior studies, and its effect becomes less salient when we consider the position effect. Our results have both theoretical implication and practical implication for the website designer

Loot Box Purchase Decisions in Digital Business Models: The Role of Certainty and Loss Experience

Game providers are increasingly employing and selling loot boxes, which can be considered virtual goods that consist of further virtual goods on a randomized basis. As such, game providers can foster profitability without impeding user experience. Drawing on prospect theory, we investigate ideas for the design of loot box menus to optimize revenue generation and user well-being. By conducting a contest-based online experiment with 159 participants, our analyses reveal that including certain (vs. uncertain) content in loot boxes can influence users’ purchase behaviors and thus increase revenues. Moreover, this effect increases when participants previously experienced a loss. Thus, our findings demonstrate that game providers can profit from offering certain content in loot boxes

The Effect of Recommender Systems on Users’ Situation Awareness and Actions

Many organizations are implementing recommender systems with the expectation to influence users’ actions. However, research has shown that poorly designed recommender systems may be counterproductive. For instance, if a recommender system provides too many recommendations, users cannot focus on relevant recommendations anymore. To tackle this challenge, recommender systems need to be balanced and adjusted to the processes in which they shall support users. Only designed correctly, recommender systems may influence users’ situation awareness and, ultimately, enable them to perform informed actions. Research has shown that users’ situation awareness depends on users’ elaboration. Therefore, we draw on the Elaboration Likelihood Model to conceptualize recommendation velocity and recommendation faithfulness as two variables that influence users’ situation awareness. Furthermore, since research identified process automation as a major antecedent of situation awareness, we conceptualize process automation as a third influencing variable. Finally, we develop a conceptual research model and outline our next steps

When Can AI Reduce Individuals’ Anchoring Bias and Enhance Decision Accuracy? Evidence from Multiple Longitudinal Experiments

This study aimed to identify and explain the mechanism underlying decision-making behaviors adaptive to AI advice. We develop a new theoretical framework by drawing on the anchoring effect and the literature on experiential learning. We focus on two factors: (1) the difference between individuals’ initial estimates and AI advice and (2) the existence of a second anchor (i.e., previous-year credit scores). We conducted two longitudinal experiments in the corporate credit rating context, where correct answers exist stochastically. We found that individuals exhibit some paradoxical behaviors. With greater differences and no second anchor, individuals are more likely to make adjustment efforts, but their initial estimates remain strong anchors. Yet, in multiple-anchor contexts individuals tend to diminish dependence on their initial estimates. We also found that the accuracy of individuals was dependent on their debiasing efforts

Effect of timing and source of online product recommendations: An eye-tracking study

Online retail business has become an emerging market for almost all business owners. Online recommender systems provide better services to the consumers as well as assist consumers with their decision making processes. In this study, a controlled lab experiment was conducted to assess the effect of recommendation timing (early, mid, and late) and recommendation source (expert reviews vs. consumer reviews) on e-commerce users\u27 interest and attention. Eye-tracking data was extracted from the experiment and analyzed. The results suggest that users show more interest in recommendation based on consumer reviews than recommendation based on expert reviews. Earlier recommendations do not receive greater user attention than later recommendations --Abstract, page iii

O lado comportamental dos agentes de recomendação : uma revisão bibliométrica

Recommendation agents have been used to assist consumers in online purchase for almost 20 years. Their use has been studied in academic research with two different approaches. The first one addresses computational problems related to generating accurate recommendations. The other seeks to understand how user interaction with recommendation agents can alter behaviors in online shopping. Through bibliometric and scientometric methods, this study looked for the most influential papers, authors and journals in the field of behavioral recommendation research. In the present work, only articles investigating behavioral aspects of recommendation usage were considered. The identified articles were analyzed in terms of their methodology, variables and repercussion. At the end, a total of 175 articles published in journals from many different fields of academic research were found, attesting the multidisciplinary nature of this topic. Most of the studies were empirical investigations using experimental methodology, however theoretical papers showed to be more influential. It was possible to identify 29 different dependent variables used to measure the effects of recommendations in online assisted purchase. The 19 independent variables used in these studies were related to characteristics of the recommendation agent, user characteristics or vendor characteristics. Results also showed that the field still lacks confirmatory studies capable of creating a greater assurance for the knowledge already developed in the field.um período de cerca de 20 anos. Sua utilização atualmente é estudada na pesquisa acadêmica a partir de duas diferentes abordagens. A primeira se destina à resolução de problemas computacionais relacionados à geração de recomendações acuradas. A segunda tem como intuito entender como a interação do usuário com agentes de recomendação pode alterar seu comportamento de compra online. Usando um método bibliométrico e cientométrico, este estudo buscou os artigos, autores e publicações mais influentes no campo de pesquisa comportamental. Isto significa que apenas artigos que investigaram aspectos comportamentais do uso de recomendações foram considerados. Os artigos identificados foram também analisados em termos de sua metodologia, variáveis e repercussão. A maioria dos estudos se tratavam de investigações empíricas usando metodologia experimental, entretanto os artigos teóricos se demonstraram mais influentes. Também foi possível identificar 29 variáveis dependentes usadas para medir os efeitos das recomendações em compras online assistidas. As 19 variáveis independentes usadas nesses estudos estavam relacionadas com características do agente de recomendação, características do usuário ou características do vendedor. Os resultados também demonstraram que o campo ainda carece de estudos confirmatórios capazes de criar mais certeza para o conhecimento já desenvolvido na área