What is human-centered about human-centered AI? A map of the research landscape

T Capel, M Brereton - Proceedings of the 2023 CHI conference on …, 2023 - dl.acm.org
The application of Artificial Intelligence (AI) across a wide range of domains comes with both
high expectations of its benefits and dire predictions of misuse. While AI systems have …

Public health calls for/with ai: An ethnographic perspective

A Ismail, D Thakkar, N Madhiwalla… - Proceedings of the ACM on …, 2023 - dl.acm.org
Artificial Intelligence (AI) based technologies are increasingly being integrated into public
sector programs to help with decision-support and effective distribution of constrained …

Everybody's got ML, tell me what else you have: Practitioners' perception of ML-based security tools and explanations

J Mink, H Benkraouda, L Yang, A Ciptadi… - … IEEE Symposium on …, 2023 - ieeexplore.ieee.org
Significant efforts have been investigated to develop machine learning (ML) based tools to
support security operations. However, they still face key challenges in practice. A generally …

Troi: Towards understanding users perspectives to mobile automatic emotion recognition system in their natural setting

V Dissanayake, V Tang, DS Elvitigala, E Wen… - Proceedings of the …, 2022 - dl.acm.org
Emotional Self-Awareness (ESA) plays a vital role in physical and mental well-being. Recent
advancements in artificial intelligence technologies have shown promising emotion …

Faulty or Ready? Handling Failures in Deep-Learning Computer Vision Models until Deployment: A Study of Practices, Challenges, and Needs

A Balayn, N Rikalo, J Yang, A Bozzon - … of the 2023 CHI Conference on …, 2023 - dl.acm.org
Handling failures in computer vision systems that rely on deep learning models remains a
challenge. While an increasing number of methods for bug identification and correction are …

One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generations

Y Lee, K Son, TS Kim, J Kim, JJY Chung… - The 2024 ACM …, 2024 - dl.acm.org
As Large Language Models (LLMs) are nondeterministic, the same input can generate
different outputs, some of which may be incorrect or hallucinated. If run again, the LLM may …

VIME: Visual Interactive Model Explorer for Identifying Capabilities and Limitations of Machine Learning Models for Sequential Decision-Making

A Das Antar, S Molaei, YY Chen, ML Lee… - Proceedings of the 37th …, 2024 - dl.acm.org
Ensuring that Machine Learning (ML) models make correct and meaningful inferences is
necessary for the broader adoption of such models into high-stakes decision-making …

Is the Same Performance Really the Same?: Understanding How Listeners Perceive ASR Results Differently According to the Speaker's Accent

S Kim, YS Park, D Ahn, JM Kwak, J Kim - Proceedings of the ACM on …, 2024 - dl.acm.org
Research suggests that automatic speech recognition (ASR) systems, which automatically
convert speech to text, show different performances according to various input classes (eg …

The Trust Recovery Journey. The Effect of Timing of Errors on the Willingness to Follow AI Advice.

PK Kahr, G Rooks, C Snijders… - Proceedings of the 29th …, 2024 - dl.acm.org
Complementing human decision-making with AI advice offers substantial advantages.
However, humans do not always trust AI advice appropriately and are overly sensitive to …

[PDF][PDF] How Do We Assess the Trustworthiness of AI? Introducing the Trustworthiness Assessment Model (TrAM)

N Schlicker, K Baum, A Uhde, S Sterz, MC Hirsch… - 2022 - osf.io
Designing trustworthy systems and enabling external parties to accurately assess the
trustworthiness of these systems are crucial objectives. Only if trustors assess system …