As AI continues its shift from centralized clouds to decentralized and locally hosted systems, a new wave of ethical questions is surfacing ones that existing laws, policies, and even philosophies are struggling to answer.
Unlike traditional cloud AI, where accountability often lies with large corporations or cloud providers, local and decentralized AI flips the script. Now, individuals, businesses, and autonomous devices themselves play a much bigger role in training, hosting, and deploying AI.
Bias and Hallucinations at the Edge
Bias in AI isn’t new but detecting and correcting it becomes harder when models are fragmented across thousands of devices. In centralized systems, biases can be flagged, retrained, or mitigated at scale. In decentralized models, each instance might evolve differently, based on the data it interacts with.
Example: A decentralized healthcare chatbot running on local devices might offer different advice in different regions simply because the localized training data is biased or incomplete.
Edge LLMs are also more likely to hallucinate (generate false or misleading outputs) when their datasets are too narrow or too personalized—a risk that multiplies without centralized oversight.
A 2024 MIT Ethics Lab study found that edge-deployed AI systems are 40% more likely to retain user-induced biases over time compared to their centrally monitored counterparts.
