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Cited by 20Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AIHuman-Automation Interaction and Safety

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The Human Factor in Explainable AI Frameworks for User Trust and Cognitive Alignment

Open access
Praveen Kumar Myakala, Anil Kumar Jonnalagadda, Chiranjeevi Bura·2025·IARJSET
DOI 10.17148/iarjset.2025.12110
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Abstract

Artificial Intelligence (AI) is transforming decision-making in critical fields like healthcare, finance, and governance.However, its "black box" nature undermines trust and comprehension.Explainable AI (XAI) addresses this by enhancing transparency and interpretability, yet aligning explainability with human cognitive and emotional needs remains challenging.This paper explores principles and methodologies for designing human-centered XAI, emphasizing user profiling, dynamic explanations, and ethical considerations like fairness and accountability.Key contributions include adaptive explanations tailored to diverse user needs and strategies to mitigate biases, advancing AI systems that are transparent, accessible, and trustworthy.

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Research works printed but not linked: 33

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Related

Metadata

Authors
Myakala, P. K., Jonnalagadda, A. K., & Bura, C.
Affiliations
University of Colorado Boulder (US)
Corresponding author
Praveen Kumar Myakala
ORCID
Venue
IARJSET
Publisher
Tejass Publishers
Published
2025-01-16
Volume
12
Issue
01
ISSN
  • 2393-8021 (ISSN-L)
  • 2394-1588
DOI
10.17148/iarjset.2025.12110
OpenAlex
W4406577700
Cited by
18
References
0
Citations by year
2026: 1 · 2025: 17
FWCI
7.25
Type
journal article
Language
en
Open access
Locations
  • IARJSET (publishedVersion)
  • SSRN Electronic Journal (acceptedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
Keywords
explainable AI, human-centered AI, user trust, cognitive alignment, user profiling, adaptive explanations, algorithmic fairness, accountability, bias mitigation, transparency, interpretability, human-AI interaction

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