从我们的错误中学习？

Traffic Technology International Pub Date : 2024-01-01 DOI:10.12968/s1356-9252(24)40033-6

Lauren Dyson

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引用次数: 0

摘要

基于人工智能的决策最终可能会因为人工智能使用有偏见的数据进行教学而产生缺陷。虽然机器学习算法有可能带来更高的安全性、更好的流动性和更有效的交通管理，但数据偏差可能导致负面后果，如歧视、不公平和数据不可靠。我们向专家请教如何在智能数据管理中避免意想不到的后果

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Learning from Our Mistakes?

Decisions based on artificial intelligence could end up being flawed because AI is being taught using biased data. While machine learning algorithms have the potential to enable greater safety, better access to mobility and more effective traffic management, data bias can lead to negative consequences, such as discrimination, unfairness and unreliability of data. We ask the experts how we can avoid unintended consequences in smart data management

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Traffic Technology International

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