The Application of the Principles of Responsible AI on Social Media Marketing for Digital Health release_7z56ylzmy5auhfsqf65tq4y6mq

by Rui Liu, Suraksha Gupta, Parth Patel

Published in Information Systems Frontiers by Springer Science and Business Media LLC.

2021   p1-25

Abstract

<jats:title>Abstract</jats:title>Social media enables medical professionals and authorities to share, disseminate, monitor, and manage health-related information digitally through online communities such as Twitter and Facebook. Simultaneously, artificial intelligence (AI) powered social media offers digital capabilities for organizations to select, screen, detect and predict problems with possible solutions through digital health data. Both the patients and healthcare professionals have benefited from such improvements. However, arising ethical concerns related to the use of AI raised by stakeholders need scrutiny which could help organizations obtain trust, minimize privacy invasion, and eventually facilitate the responsible success of AI-enabled social media operations. This paper examines the impact of responsible AI on businesses using insights from analysis of 25 in-depth interviews of health care professionals. The exploratory analysis conducted revealed that abiding by the responsible AI principles can allow healthcare businesses to better take advantage of the improved effectiveness of their social media marketing initiatives with their users. The analysis is further used to offer research propositions and conclusions, and the contributions and limitations of the study have been discussed.
In application/xml+jats format

Archived Files and Locations

application/pdf  687.1 kB
file_rzulvktt4jgdbar2jchfjqe4jy
link.springer.com (publisher)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2021-09-13
Language   en ?
Container Metadata
Not in DOAJ
In Keepers Registry
ISSN-L:  1387-3326
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 2596cf9a-3c6c-40d6-a9a4-c93fb5aa6748
API URL: JSON