Navigating Responsible AI: A Case Study with Recommender System

Artificial Intelligence (AI) has been suggested to provide numerous enhancements in both our professional and personal lives. It consists of an extensive range of technologies that enable people and institutions to combine, evaluate, and apply data to make better, more automated decisions. Concern over the unintended and detrimental effects of these technologies is growing, even though the majority of emphasis has focused on the benefits that businesses have realized from adopting and using AI. In order to comprehend the ethical responsibility in developing technology, a new field called” responsible artificial intelligence” examines the ethics of AI. Recommender systems are essential for offering users personalized services since they can forecast users’ current preferences for specific items based on their past behaviour. The evolution of recommender systems has naturally incorporated AI, encompassing machine learning and computational intelligence techniques, to improve prediction accuracy. This paper first systematically discusses the basic concepts of responsible AI and their application in various domains like healthcare, agriculture, education, social media, employment, democracy, and military systems. The paper has also discussed how a recommender system satisfies the properties of a responsible AI. The various benefits and challenges associated with a responsible AI are also discussed in this paper. The idea of an affectionate AI and the function of a recommender system as an affectionate AI has also been discussed in the paper

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-24
DOI
https://doi.org/10.5281/zenodo.13988087
Primary Topic
Biocrusts and Microbial Ecology
Type
article
Field-Weighted Citation Impact
0.00
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article

Navigating Responsible AI: A Case Study with Recommender System

Zenodo (CERN European Organization for Nuclear Research)
Biocrusts and Microbial Ecology
article

Navigating Responsible AI: A Case Study with Recommender System

article en

Abstract

Artificial Intelligence (AI) has been suggested to provide numerous enhancements in both our professional and personal lives. It consists of an extensive range of technologies that enable people and institutions to combine, evaluate, and apply data to make better, more automated decisions. Concern over the unintended and detrimental effects of these technologies is growing, even though the majority of emphasis has focused on the benefits that businesses have realized from adopting and using AI. In order to comprehend the ethical responsibility in developing technology, a new field called” responsible artificial intelligence” examines the ethics of AI. Recommender systems are essential for offering users personalized services since they can forecast users’ current preferences for specific items based on their past behaviour. The evolution of recommender systems has naturally incorporated AI, encompassing machine learning and computational intelligence techniques, to improve prediction accuracy. This paper first systematically discusses the basic concepts of responsible AI and their application in various domains like healthcare, agriculture, education, social media, employment, democracy, and military systems. The paper has also discussed how a recommender system satisfies the properties of a responsible AI. The various benefits and challenges associated with a responsible AI are also discussed in this paper. The idea of an affectionate AI and the function of a recommender system as an affectionate AI has also been discussed in the paper

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 57%
Biocrusts and Microbial Ecology
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Navigating Responsible AI: A Case Study with Recommender System · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS