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This paper examines a commonly used measure of persuasion whose precise interpretation has been obscure in the literature. By using the potential outcome framework, we define the causal persuasion rate by a proper conditional probability of…

Econometrics · Economics 2022-12-02 Sung Jae Jun , Sokbae Lee

Background and Context: Few instruments exist to measure students' CS engagement and learning especially in areas where coding happens with creative, project-based learning and in regard to students' self-beliefs about computing. Objective:…

Computers and Society · Computer Science 2023-07-20 Luis Morales-Navarro , Michael T. Giang , Deborah A. Fields , Yasmin B. Kafai

A typical setup in many machine learning scenarios involves a server that holds a model and a user that possesses data, and the challenge is to perform inference while safeguarding the privacy of both parties. Private Inference has been…

Information Theory · Computer Science 2023-11-27 Zirui Deng , Vinayak Ramkumar , Rawad Bitar , Netanel Raviv

Customer Satisfaction is the most important factors in the industry irrespective of domain. Key Driver Analysis is a common practice in data science to help the business to evaluate the same. Understanding key features, which influence the…

Machine Learning · Statistics 2018-05-29 Kumarjit Pathak , Jitin Kapila , Aasheesh Barvey

Evaluating the efficiency of human-AI interactions is challenging, including subjective and objective quality aspects. With the focus on the human experience of the explanations, evaluations of explanation methods have become mostly…

Artificial Intelligence · Computer Science 2024-05-10 Helena Löfström

Prior beliefs are central to Bayesian accounts of cognition, but many of these accounts do not directly measure priors. More specifically, initial states of belief heavily influence how new information is assumed to be utilized when…

Neurons and Cognition · Quantitative Biology 2022-01-11 Peter A. V. DiBerardino , Alexandre L. S. Filipowicz , James Danckert , Britt Anderson

Personalization despite being an effective solution to the problem information overload remains tricky on account of multiple dimensions to consider. Furthermore, the challenge of avoiding overdoing personalization involves estimation of a…

Information Retrieval · Computer Science 2017-11-09 Arjumand Younus , Muhammad Atif Qureshi

The problem is in the estimation of the fraction of population with a sensitive characteristic. We consider the Item Count Technique an indirect method of questioning designed to protect respondents' privacy. The exact confidence interval…

Methodology · Statistics 2024-10-21 Stanislaw Jaworski , Wojciech Zielinski

Theoretically as well as experimentally it is investigated how people represent their knowledge in order to make decisions or to share their knowledge with others. Experiment 1 probes into the ways how people 6ather information about the…

Artificial Intelligence · Computer Science 2013-04-15 Alf C. Zimmer

Interactive Information Retrieval refers to the branch of Information Retrieval that considers the retrieval process with respect to a wide range of contexts, which may affect the user's information seeking experience. The identification…

Information Retrieval · Computer Science 2017-04-07 Christina Lioma , Birger Larsen , Hinrich Schütze , Peter Ingwersen

Research on human-AI collaboration often prioritizes objective performance. However, understanding human subjective preferences is essential to improving human-AI complementarity and human experiences. We investigate human preferences for…

Human-Computer Interaction · Computer Science 2025-03-10 Chase McDonald , Cleotilde Gonzalez

This paper studies the item-to-item recommendation problem in recommender systems from a new perspective of metric learning via implicit feedback. We develop and investigate a personalizable deep metric model that captures both the internal…

Information Retrieval · Computer Science 2022-03-24 Trong Nghia Hoang , Anoop Deoras , Tong Zhao , Jin Li , George Karypis

Decisions by Machine Learning (ML) models have become ubiquitous. Trusting these decisions requires understanding how algorithms take them. Hence interpretability methods for ML are an active focus of research. A central problem in this…

Machine Learning · Computer Science 2019-01-25 Philipp Schmidt , Felix Biessmann

Unbiased learning to rank (ULTR), which aims to learn unbiased ranking models from biased user behavior logs, plays an important role in Web search. Previous research on ULTR has studied a variety of biases in users' clicks, such as…

Information Retrieval · Computer Science 2025-08-29 Zechun Niu , Lang Mei , Liu Yang , Ziyuan Zhao , Qiang Yan , Jiaxin Mao , Ji-Rong Wen

Objectives: The aim of this paper is to contrast the retrospective and prospective use of experts beliefs in choosing between survival models in economic evaluations. Methods: The use of experts retrospective (posterior) beliefs is…

Methodology · Statistics 2021-09-15 J. W. Stevens , M. Orr

Personality quiz is a powerful tool that enables costumer segmentation by actively asking them questions, and marketers are using it as an effective method of generating leads and increasing e-commerce sales. In this paper, we study the…

Machine Learning · Computer Science 2020-12-29 Shaojie Tang , Jing Yuan

Trust is essential for our interactions with others but also with artificial intelligence (AI) based systems. To understand whether a user trusts an AI, researchers need reliable measurement tools. However, currently discussed markers…

Human-Computer Interaction · Computer Science 2023-10-26 Martin Dechant , Susanne Poeller , Benedikt Hosp , Olga Lukashova-Sanz , Alexandra Sipatchin , Siegfried Wahl

Collective intelligence is believed to underly the remarkable success of human society. The formation of accurate shared beliefs is one of the key components of human collective intelligence. How are accurate shared beliefs formed in groups…

Computers and Society · Computer Science 2016-08-08 Peter M. Krafft , Julia Zheng , Wei Pan , Nicolás Della Penna , Yaniv Altshuler , Erez Shmueli , Joshua B. Tenenbaum , Alex Pentland

Conflicts of interest often arise between data sources and their users regarding how the users' information needs should be interpreted by the data source. For example, an online product search might be biased towards presenting certain…

Databases · Computer Science 2026-03-09 Nischal Aryal , Arash Termehchy , Marianne Winslett

We show that it is possible to understand and identify a decision maker's subjective causal judgements by observing her preferences over interventions. Following Pearl [2000], we represent causality using causal models (also called…

Theoretical Economics · Economics 2024-01-23 Joseph Y. Halpern , Evan Piermont
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