A Conversation about Calibrating Trust and Complementarity in Human-AI Teams
カートのアイテムが多すぎます
カートに追加できませんでした。
ウィッシュリストに追加できませんでした。
ほしい物リストの削除に失敗しました。
ポッドキャストのフォローに失敗しました
ポッドキャストのフォロー解除に失敗しました
A Conversation about Calibrating Trust and Complementarity in Human-AI Teams
-
ナレーター：
-
著者：
概要
This research examines the Trust–Complementarity Model, a strategic framework designed to improve how human-AI teams collaborate on complex, knowledge-intensive tasks. The research argues that organizational success depends on calibrating trust so that humans neither blindly follow nor unfairly reject algorithmic suggestions. By assigning pattern recognition to machines and reserving ethical reasoning and contextual judgment for people, companies can achieve superior collective intelligence. The research highlights the importance of transparent communication, specialized training, and psychological safety to prevent skill atrophy and automation bias. Ultimately, the research promotes dynamic learning systems where both human expertise and AI accuracy evolve through continuous, structured feedback.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.