Enabling Seamless Human-AI Interaction and Effective Teaming

Human-Centric AI (HCAI) designs or develops artificial intelligence (AI) systems that incorporate human needs, values, and collaborative relationships with AI. HCAI differs from traditional AI, which works somewhat autonomously or with minimum human involvement, with the human component as its defining feature and the basis for adding interpretability. The presented paradigm is then applied to various areas, including healthcare, finance, autonomous systems, and cybersecurity, because it helps enable collaborative work between humans and AI to optimize efficiency, safety, and trust. To successfully collaborate with humans, such systems must operate in complex issues and follow established guidelines, ethical considerations, and regulatory frameworks. Using scholarly and industrial sources to understand the field, this paper covers key aspects of HAI, including the best practices for interaction of the human-AI, factors for trust building, regulatory compliance, business scalability, and emerging challenges.

Authors

Institutions

Publication Details

Journal
International Journal of Intelligence Science
Published
2025-01-01
DOI
https://doi.org/10.4236/ijis.2025.153005
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enabling Seamless Human-AI Interaction and Effective Teaming

Vivek Singh
International Journal of Intelligence Science
Ethics and Social Impacts of AI
article

Enabling Seamless Human-AI Interaction and Effective Teaming

Vivek Singh
article en

Abstract

Human-Centric AI (HCAI) designs or develops artificial intelligence (AI) systems that incorporate human needs, values, and collaborative relationships with AI. HCAI differs from traditional AI, which works somewhat autonomously or with minimum human involvement, with the human component as its defining feature and the basis for adding interpretability. The presented paradigm is then applied to various areas, including healthcare, finance, autonomous systems, and cybersecurity, because it helps enable collaborative work between humans and AI to optimize efficiency, safety, and trust. To successfully collaborate with humans, such systems must operate in complex issues and follow established guidelines, ethical considerations, and regulatory frameworks. Using scholarly and industrial sources to understand the field, this paper covers key aspects of HAI, including the best practices for interaction of the human-AI, factors for trust building, regulatory compliance, business scalability, and emerging challenges.

International Journal of Intelligence ScienceVol. 15(03)
Palatin Technologies (United States) (US)
Openalex Percentile: Top 89%
Ethics and Social Impacts of AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.