本の詳細
形式
キンドル
ページ数
289
言語
英語
公開されました
Jul 7, 2020
出版社
Springer
説明
In the realm of artificial intelligence and multi-agent systems, the complexities surrounding explainability and transparency take center stage in the documented proceedings of a significant workshop held in Auckland. Scholars and practitioners gathered to delve into the pressing challenges and innovative solutions that surround autonomous agents. The discussions ranged widely, exploring ways to enhance trust and understanding in automated systems, essential for fostering user acceptance and effective collaboration.
The contributions from a diverse array of experts shed light on the intricate interplay between algorithmic functionality and human oversight. The focus on explainability reveals a commitment to making sophisticated technologies accessible and comprehensible to users, addressing concerns that often accompany modern automation. This exploration is not just theoretical; it strives to offer practical frameworks and insights that can be applied across various domains.
Through a combination of rigorous analysis and forward-thinking proposals, the workshop proceedings present a rich tapestry of knowledge. Readers will find a valuable resource that encapsulates the state of the art in creating more transparent and accountable autonomous systems, driving the conversation forward in both academia and industry.
The contributions from a diverse array of experts shed light on the intricate interplay between algorithmic functionality and human oversight. The focus on explainability reveals a commitment to making sophisticated technologies accessible and comprehensible to users, addressing concerns that often accompany modern automation. This exploration is not just theoretical; it strives to offer practical frameworks and insights that can be applied across various domains.
Through a combination of rigorous analysis and forward-thinking proposals, the workshop proceedings present a rich tapestry of knowledge. Readers will find a valuable resource that encapsulates the state of the art in creating more transparent and accountable autonomous systems, driving the conversation forward in both academia and industry.