Abstract
The proliferation of artificial intelligence (AI) in digital platforms has complicated the concept of truth in communication studies. The article presents the dichotomic framework of Front-end AI and Back-end AI to tackle the complexity of distinguishing truth. Front-end AI refers to AI technology used up-front, often as the face of a product or service, challenging the authenticity and truthfulness of content. In contrast, Back-end AI refers to AI technology used behind the scenes, which can generate misleading or biased content without disclosing its AI-generated nature. Addressing these challenges requires different approaches, such as verification and ethical guidelines for Front-end AI and algorithmic transparency, bias detection, and human oversight for Back-end AI.
Subject
Social Sciences (miscellaneous),Communication
Reference37 articles.
1. The ethics of facial recognition technologies, surveillance, and accountability in an age of artificial intelligence: a comparative analysis of US, EU, and UK regulatory frameworks;Almeida;AI and Ethics,2022
2. Big data's disparate impact;Barocas;Calif. Law Rev.,2016
3. AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias;Bellamy;IBM J. Res. Dev.,2019
4. Network Propaganda
5. Ethical assurance: a practical approach to the responsible design, development, and deployment of data-driven technologies;Burr;AI and Ethics,2022