Kartik Hosanagar

About

Kartik Hosanagar is a Faculty Co-Director of Wharton Human-AI Research at the Wharton School. His research focuses on exploring the design, impact, and governance of intelligent systems across organizations and society. He is involved in advancing human-centered AI for business innovation, examining how AI influences creativity, the future of work, and business solutions. Hosanagar contributes to the understanding of responsible AI implementation, emphasizing accountability, ethics, and trust in AI systems. He collaborates on industry reports and research initiatives that address the adoption of AI agents, skills transition in the economy, and enterprise AI deployment. Additionally, he leads discussions and webinars on the latest AI applications, their impact on industries, and strategies for effective AI oversight.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Information Retrieval
• Computer Security
• Knowledge management
• Political Science
• Mathematics
• Distributed computing
• Engineering

Selected publications

• How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?

  Management Science · 2020 · 112 citations

  Senior authorCorresponding

  We investigate the moderating effect of product attributes and review ratings on views, conversion|views (conversion conditional on views), and final conversion of a purchase-based collaborative filtering recommender system on an e-commerce site. We run a randomized field experiment on a top retailer with 184,375 users split into a recommender-treated group and a control group. We tag theory-driven attributes of 37,125 unique products via Amazon Mechanical Turk to augment the usual product data…

  PublisherDOI

• Human-AI Interaction in Human Resource Management: Understanding Why Employees Resist Algorithmic Evaluation at Workplaces and How to Mitigate Burdens

  2021 · 102 citations

  Recently, Artificial Intelligence (AI) has been used to enable efficient decision-making in managerial and organizational contexts, ranging from employment to dismissal. However, to avoid employees’ antipathy toward AI, it is important to understand what aspects of AI employees like and/or dislike. In this paper, we aim to identify how employees perceive current human resource (HR) teams and future algorithmic management. Specifically, we explored what factors negatively influence employees’ per…

  PublisherDOI

• Designing Fair AI in Human Resource Management: Understanding Tensions Surrounding Algorithmic Evaluation and Envisioning Stakeholder-Centered Solutions

  CHI Conference on Human Factors in Computing Systems · 2022 · 63 citations

  Enterprises have recently adopted AI to human resource management (HRM) to evaluate employees’ work performance evaluation. However, in such an HRM context where multiple stakeholders are complexly intertwined with different incentives, it is problematic to design AI reflecting one stakeholder group's needs (e.g., enterprises, HR managers). Our research aims to investigate what tensions surrounding AI in HRM exist among stakeholders and explore design solutions to balance the tensions. By conduc…

  PublisherDOI

• To Brush or Not to Brush: Product Rankings, Consumer Search, and Fake Orders

  Information Systems Research · 2022-05-20 · 35 citations

  articleSenior author

  Brushing—online merchants placing fake orders of their own products—has been a widespread phenomenon on major e-commerce platforms. One key reason why merchants brush is that it boosts their rankings in search results. Products with higher sales volume are more likely to rank higher. Additionally, rankings matter because consumers face search frictions and narrow their attention to only the few products that show up at the top. Thus, fake orders can affect consumer choice. In our paper, we find…

  PublisherDOI

• Impact of Model Interpretability and Outcome Feedback on Trust in AI

  2024-05-11 · 27 citations

  articleOpen accessSenior author

  This paper bridges the gap in Human-Computer Interaction (HCI) research by comparatively assessing the effects of interpretability and outcome feedback on user trust and collaborative performance with AI. Through novel pre-registered experiments (N=1,511 total participants) using an interactive prediction task, we analyzed how interpretability and outcome feedback influence users’ task performance and trust in AI. The results counter the widespread belief that interpretability drives trust, show…

  PublisherOA PDFDOI

Frequent coauthors

• Dokyun Lee

  24 shared

• Daniel Fleder

  11 shared

• Ramayya Krishnan

  11 shared

• R. Guérin

  10 shared

• Vibhanshu Abhishek

  University of California System

  9 shared

• Ashish Agarwal

  The University of Texas at Austin

  8 shared

• Yong Tan

  University of Washington

  8 shared

• John Chuang

  University of California, Berkeley

  7 shared

Labs

• Wharton Human-AI ResearchPI

Similar researchers at University of Pennsylvania

• Joseph E. Gyourkoh-72
• Itay Goldsteinh-65
• David Mustoh-31