Enhancing transparency in AI-powered customer engagement
This paper addresses the critical challenge of building consumer trust in AI-powered customer engagement by emphasising the necessity for transparency and accountability. Despite the potential of AI to revolutionise business operations and enhance customer experiences, widespread concerns about misinformation and the opacity of AI decision-making processes hinder trust. Surveys highlight a significant lack of awareness among consumers regarding their interactions with AI, alongside apprehensions about bias and fairness in AI algorithms. The paper advocates for the development of explainable AI models that are transparent and understandable to both consumers and organisational leaders, thereby mitigating potential biases and ensuring ethical use. It underscores the importance of organisational commitment to transparency practices beyond mere regulatory compliance, including fostering a culture of accountability, prioritising clear data policies and maintaining active engagement with stakeholders. By adopting a holistic approach to transparency and explainability, businesses can cultivate trust in AI technologies, bridging the gap between technological innovation and consumer acceptance, and paving the way for more ethical and effective AI-powered customer engagements. KEYWORDS: artificial intelligence (AI), transparency
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingFairnessMisinformationSimilar Papers 제목 키워드 기반
Valuing an Engagement Surface using a Large Scale Dynamic Causal Model
With recent rapid growth in online shopping, AI-powered Engagement Surfaces (ES) have become ubiquitous across retail services. These engagement surfaces perform an increasing range of functions, including recommending n…
Decision MakingBridging Search and CRM: Productionizing AI Product Research Agents for Customer Re-Engagement
Modern e-commerce platforms often operate search, recommendation, personalization, and CRM systems independently, limiting opportunities for proactive customer re-engagement. This is particularly challenging for explorat…
CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation
The introduction of new features and services in the banking sector often overwhelms customers, creating an opportunity for banks to enhance user experience through financial chatbots powered by large language models (LL…
AI AgentLanguage ModelingLanguage ModellingLarge Language Model+4Digital assistant in a point of sales
This article investigates the deployment of a Voice User Interface (VUI)-powered digital assistant in a retail setting and assesses its impact on customer engagement and service efficiency. The study explores how digital…
Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs
Product designers often begin their design process with handcrafted personas. While personas are intended to ground design decisions in consumer preferences, they often fall short in practice by remaining abstract, expen…