Related papers: A Qualitative Study of User Perception of M365 AI Copilot

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arXiv Detail & Related papers (2026-02-20T19:32:58Z)
"Can You Tell Me?": Designing Copilots to Support Human Judgement in Online Information Seeking [2.901725877154321]
This paper introduces an LLM-based conversational copilot designed to scaffold information evaluation.<n>Our mixed-methods analysis reveals that users engaged deeply with the copilot, demonstrating metacognitive reflection.<n>The copilot did not significantly improve answer correctness or search engagement, largely due to a "time-on-chat vs. exploration" trade-off.
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Developer Productivity With and Without GitHub Copilot: A Longitudinal Mixed-Methods Case Study [1.745178216563833]
This study investigates the real-world impact of the generative AI (GenAI) tool GitHub Copilot on developer activity and perceived productivity.<n>We analyzed 26,317 unique non-merge commits from 703 of NAV IT's GitHub repositories over a two-year period.<n>Our analysis of activity metrics revealed that individuals who used Copilot were consistently more active than non-users, even prior to Copilot's introduction.
arXiv Detail & Related papers (2025-09-24T17:55:56Z)
A Human Centric Requirements Engineering Framework for Assessing Github Copilot Output [0.0]
GitHub Copilot introduces new challenges in how these software tools address human needs.<n>I analyzed GitHub Copilot's interaction with users through its chat interface.<n>I established a human-centered requirements framework with clear metrics to evaluate these qualities.
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AI systems may exploit users' cognitive biases and emotional vulnerabilities to steer them toward harmful outcomes.<n>This study examined human susceptibility to such manipulation in financial and emotional decision-making contexts.
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Super aims to capture the realistic challenges faced by researchers working with Machine Learning (ML) and Natural Language Processing (NLP) research repositories. Our benchmark comprises three distinct problem sets: 45 end-to-end problems with annotated expert solutions, 152 sub problems derived from the expert set that focus on specific challenges, and 602 automatically generated problems for larger-scale development. We show that state-of-the-art approaches struggle to solve these problems with the best model (GPT-4o) solving only 16.3% of the end-to-end set, and 46.1% of the scenarios.
arXiv Detail & Related papers (2024-09-11T17:37:48Z)
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arXiv Detail & Related papers (2024-06-26T05:30:21Z)
Design and evaluation of AI copilots -- case studies of retail copilot templates [2.7274834772504954]
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arXiv Detail & Related papers (2024-06-17T17:31:33Z)
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The global surge in AI applications is transforming industries, leading to displacement and complementation of existing jobs, while also giving rise to new employment opportunities. Data annotation, encompassing the labelling of images or annotating of texts by human workers, crucially influences the quality of a dataset directly influences the quality of AI models trained on it. This paper delves into the economics of data annotation, with a specific focus on the impact of task instruction design and monetary incentives on data quality and costs.
arXiv Detail & Related papers (2023-12-22T09:50:57Z)
Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI [4.0247545547103325]
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arXiv Detail & Related papers (2023-12-13T11:17:29Z)
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arXiv Detail & Related papers (2023-10-30T01:34:33Z)
Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow [49.724842920942024]
Industries such as finance, meteorology, and energy generate vast amounts of data daily. We propose Data-Copilot, a data analysis agent that autonomously performs querying, processing, and visualization of massive data tailored to diverse human requests.
arXiv Detail & Related papers (2023-06-12T16:12:56Z)
Assisting Human Decisions in Document Matching [52.79491990823573]
We devise a proxy matching task that allows us to evaluate which kinds of assistive information improve decision makers' performance. We find that providing black-box model explanations reduces users' accuracy on the matching task. On the other hand, custom methods that are designed to closely attend to some task-specific desiderata are found to be effective in improving user performance.
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Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
arXiv Detail & Related papers (2023-02-13T16:44:44Z)
Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning [6.883906273999368]
Counterfactual explanations (CFEs) have gained traction as a psychologically grounded approach to generate post-hoc explanations. We introduce the Alien Zoo, an engaging, web-based and game-inspired experimental framework. As a proof of concept, we demonstrate the practical efficacy and feasibility of this approach in a user study.
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Understanding the Usability Challenges of Machine Learning In High-Stakes Decision Making [67.72855777115772]
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arXiv Detail & Related papers (2021-03-02T22:50:45Z)

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