Accountability: Determining who is responsible for the outcomes generated by generative AI—the users, the developers, or the AI itself—presents significant ethical challenges.
Bias and Fairness: Generative AI models have the potential to reinforce biases found in the training data. This may result in the creation of offensive or discriminatory content that disproportionately targets marginalized populations.
Labor Concerns: When their papers are used, authors are not contacted or paid. Citation counts may also be impacted by this.
Misinformation: Misinformation and deepfakes, which can be used to sway public opinion or harm reputations, are a problem when it comes to the ability to produce incredibly lifelike text, images, or videos.
Privacy & Data Security: These models frequently call for huge datasets, which may contain private data like an individual's IP address and usage patterns across the service. Data privacy and the moral application of such data are called into question by this.
Transparency: AI's inner workings are a mystery. Even the developers of the algorithms are baffled by them. This reduces people's confidence in AI agents and technology.
This is a Libguide.
Creative Commons Libguide, RESEARCH WITH ARTIFICIAL INTELLIGENCE (AI) FOR NURSING STUDENTS by Nandi Prince is licensed under CC BY-NC
