Uncovering Hidden Profiles; Managerial Interventions for Discovering Superior Decision Alternatives

A common reason for the use of teams in organizations is the idea that each individual can bring a unique perspective to the decision task; however, research shows that teams often fail to surface and use unique information to evaluate decision alternatives. Under a condition known as the hidden profile, each member uniquely possesses a critical clue needed to uncover the superior solution. Failure to share and adequately evaluate this information will result in poor decision quality. In order to mitigate this team decision-making bias, the present study utilizes experimental research to examine the impact of the devil’s advocacy technique on the decision quality of hidden profile teams. Results show that advocacy groups had higher decision qualities than groups under free discussion; however, advocacy teams also had higher levels of anger and lower levels of individual support for their group’s decision. As a result, while these teams selected the best solution, the presence of a devil’s advocate introduces conditions that may hinder the solution’s implementation. Furthermore, similar experiments with advocacy techniques suggest that the positive effect on decision quality found here is reduced in the presence of stronger hidden profiles

Artificial Intelligence in Medicine: A New Way to Diagnose and Treat Disease

Artificial intelligence (AI) has immense potential to transform medicine by improving diagnostic accuracy and enabling personalized treatments. This paper explores how AI systems analyze medical images, lab tests, genetic data, and patient histories to detect disease earlier and guide therapy selection. Though still an emerging field, impressive results demonstrate AI can surpass human clinicians on diagnostic tasks. For example, an AI system detected breast cancer from mammograms more accurately than expert radiologists. In ophthalmology, AI outperformed ophthalmologists in diagnosing diabetic retinopathy. By finding subtle patterns in complex datasets, AI promises to catch diseases like cancer in early, more treatable stages. Beyond diagnosis, AI can identify optimal treatments for individual patients based on their genetic makeup and lifestyle factors. Researchers are also using AI to design new medications. While AI offers many benefits, challenges remain regarding clinician displacement, legal liability, data privacy, and the "black box" nature of AI reasoning. More research is needed, but it is clear that AI will fundamentally alter medical practice. AI empowers clinicians to provide earlier, more precise diagnoses and tailored therapies for patients. Though it will not replace doctors, by automating routine tasks and uncovering hidden insights, AI can free physicians to focus on holistic care. The future of medicine lies in humans and smart machines working together

Cognitive biases, heuristics and decision-making in design for behaviour change

Copyright @ 2012 Social Science Electronic PublishingMuch human behaviour can be seen as decision-making, and so understanding and influencing those decision-making processes could be an important component in design for behaviour change. This paper examines the 'heuristics and biases' approach to modelling decision-making, and attempts to extract insights which are relevant to designers working to influence user behaviour for social or environmental benefit -- either by exploiting biases, or helping to counter those which lead to undesirable behaviour. Areas covered include a number of specific cognitive biases in detail, and the alternative perspective of Gigerenzer and others, who contend (following Herbert Simon) that many heuristics potentially leading to biases are actually ecologically rational, and part of humans' adaptive responses to situations. The design relevance of this is briefly considered, and implications for designers are summarised

Information is not a Virus, and Other Consequences of Human Cognitive Limits

The many decisions people make about what to pay attention to online shape the spread of information in online social networks. Due to the constraints of available time and cognitive resources, the ease of discovery strongly impacts how people allocate their attention to social media content. As a consequence, the position of information in an individual's social feed, as well as explicit social signals about its popularity, determine whether it will be seen, and the likelihood that it will be shared with followers. Accounting for these cognitive limits simplifies mechanics of information diffusion in online social networks and explains puzzling empirical observations: (i) information generally fails to spread in social media and (ii) highly connected people are less likely to re-share information. Studies of information diffusion on different social media platforms reviewed here suggest that the interplay between human cognitive limits and network structure differentiates the spread of information from other social contagions, such as the spread of a virus through a population.Comment: accepted for publication in Future Interne

Uncovering Bias in Personal Informatics

Personal informatics (PI) systems, powered by smartphones and wearables, enable people to lead healthier lifestyles by providing meaningful and actionable insights that break down barriers between users and their health information. Today, such systems are used by billions of users for monitoring not only physical activity and sleep but also vital signs and women's and heart health, among others. %Despite their widespread usage, the processing of particularly sensitive personal data, and their proximity to domains known to be susceptible to bias, such as healthcare, bias in PI has not been investigated systematically. Despite their widespread usage, the processing of sensitive PI data may suffer from biases, which may entail practical and ethical implications. In this work, we present the first comprehensive empirical and analytical study of bias in PI systems, including biases in raw data and in the entire machine learning life cycle. We use the most detailed framework to date for exploring the different sources of bias and find that biases exist both in the data generation and the model learning and implementation streams. According to our results, the most affected minority groups are users with health issues, such as diabetes, joint issues, and hypertension, and female users, whose data biases are propagated or even amplified by learning models, while intersectional biases can also be observed

Tidying Up the Conversational Recommender Systems' Biases

The growing popularity of language models has sparked interest in conversational recommender systems (CRS) within both industry and research circles. However, concerns regarding biases in these systems have emerged. While individual components of CRS have been subject to bias studies, a literature gap remains in understanding specific biases unique to CRS and how these biases may be amplified or reduced when integrated into complex CRS models. In this paper, we provide a concise review of biases in CRS by surveying recent literature. We examine the presence of biases throughout the system's pipeline and consider the challenges that arise from combining multiple models. Our study investigates biases in classic recommender systems and their relevance to CRS. Moreover, we address specific biases in CRS, considering variations with and without natural language understanding capabilities, along with biases related to dialogue systems and language models. Through our findings, we highlight the necessity of adopting a holistic perspective when dealing with biases in complex CRS models

Social and interpersonal approaches to design for behaviour change

Copyright @ 2012 Social Science Electronic PublishingThis paper reviews a diverse set of social and interpersonal infl uence approaches and techniques which could be relevant to designers seeking to infl uence behavior change for social and environmental bene fit. These include work on social proof (which already has some practical applications in household energy use reduction studies) and dramaturgical and contextual approaches to modelling interaction. Perspectives on interpersonal infl uence are also covered, such as techniques extracted from Dale Carnegie's 'How to Win Friends and In fluence People', and a brief dive into the world of neuro-linguistic programming. In each case, implications for designers are highlighted and summarized at the end of the paper

Two Essays on the Recommendation Behavior of Multi-line Salespeople

This dissertation consists of two essays in which we examine the recommendation behavior of multi-line salespeople. Multi-line salespeople are those who are able to choose among overlapping, competing manufacturers’ products to make a recommendation to their customers. In this dissertation, we seek to explain why and how multi-line salespeople may recommend particular products to their customers. In the first essay, we examine why salespeople may recommend a particular product. Manufacturers frequently face the challenge of motivating distributor salespeople to focus efforts on their products rather than on their competitors’. Thus, manufacturers often rely on outcome (e.g., rewards) and behavior (e.g., training) controls. We refer to these as external controls because they reflect mechanisms by which one firm directs another firm’s employees. External controls tend to raise concerns among salespeople about the appropriateness of being influenced by an outside firm, which can be alleviated by seeking cues about their managers’ external controls. The results of a three-source, multilevel study suggests that manufacturers can enhance the ability of salesperson external controls to drive focused effort (i.e., recommendations) by increasing similar sales manager external controls; however, increasing dissimilar controls may reduce the positive impact of salesperson external controls on their focused effort. In the second essay, we examine how salespeople may recommend a particular product. The process of how purchase decisions are made by customers is well-known in the literature (i.e., self decision-making); however, to date, there has not been a complementary understanding of how purchase decisions are made for customers (i.e., self-other decision-making). The results from a qualitative study involving 71 covert participant observation encounters with salespeople across 71 store locations of 3 retailers indicate a three-step recommendation process: goals, strategies, and recommendations. Drawing upon field observations and the decision-making literature, we show that salespeople emphasize different goals when recommending products than customers making decisions for themselves. We also complement prior research by expanding the scope of known decision-making strategies (self and self-other lexicographic) and surfacing a new decision-making strategy (product homogenization). Finally, we identify three recommendation types, and link the steps in the process model via a set of integrating propositions