Explainable Artificial Intelligence: Frameworks for Ensuring the Trustworthiness

Title

Explainable Artificial Intelligence: Frameworks for Ensuring the Trustworthiness

Creator

Shama U.; Sharma V.; Gupta V.

Description

The growing computer power and ubiquity of big data are allowing Artificial Intelligence (AI) to gain widespread adoption and applicability in a wide range of sectors. The absence of an explanation for the conclusions made by today's AI algorithms is a significant disadvantage in crucial decision-making systems. For example, existing black-box AI systems are vulnerable to bias and adversarial assaults, which can taint the learning and inference processes. Explainable AI (XAI) is a recent trend in AI algorithms that gives explanations for their AI conclusions. Many contemporary AI systems have been shown to be vulnerable to undetectable assaults, biased against underrepresented groups, and deficient in user privacy protection. These flaws damage the user experience and undermine people's faith in all AI systems. This study proposes a systematic way to tie the social science notions of trust to the technology employed in AI-based services and products. 2024 IEEE.

Source

TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

and Transparency; Autonomy; Control; Data Privacy; Fairness; Reliability; Safety and Security

Coverage

Shama U., Christ University, Banglore, India; Sharma V., Christ University, Banglore, India; Gupta V., Christ University, Banglore, India

Rights

Restricted Access

Relation

ISBN: 979-835038427-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/TQCEBT59414.2024.10545063

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204449341&doi=10.1109%2fTQCEBT59414.2024.10545063&partnerID=40&md5=7514240432d4d683ddf10df45d0ca3f3