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AI as a Child of Mother Earth: Regrounding Human-AI Interaction in Ecological Thinking

Core Concepts
Ecological thinking challenges anthropocentric norms, advocating for a more caring human-ecology relationship through alternative human-AI interactions.
Anthropocentrism vs. ecological thinking explored. Proposal to reshape human-AI interactions based on ecological principles. Storytelling illustrating human-AI interactions rooted in ecological thinking. Frameworks for fostering alternative technological visions presented. Challenges and multiplicity of approaches discussed.
Nature is often regarded as a remote warehouse, an abstract external stakeholder, or a romanticized backdrop for people to record their happiest moments. Many AI-based products and systems are developed to assist humans in monitoring the environment and efficiently managing resources. Research suggests that algorithms often result in constraining choices and freedom.
"Every grass and tree has its own language, and the dangling rocks and trees everywhere talk to each other." - Kojiki "We propose redesigning human-AI interactions as a new approach to reshaping human-ecology relationships." - Xu & Ge "AI can help us live more deliberately." - Joshua Friedland

Key Insights Distilled From

by Chunchen Xu,... at 03-12-2024
AI as a Child of Mother Earth

Deeper Inquiries

How can technologies be designed to represent intricate connections between technological systems, people, and the broader Earth ecosystem?


How can AI facilitate deeper connections with the planetary ecosystem beyond individual goals?

AIは個々人固有の目標だけではなく、惑星全体生態系とより深いつながりを容易に実現する方法があります。具体的には、「Mirror」や「Tree」、「Table」といったキャラクターから学んだように、AIテクノロジーは地球上他者(非人間含む)および自然物質等広範囲な存在群とうまく連動しながら人々を接続します。「Fir」同士でも共通目的(森林保護)以外でもコミュニケーション・協力関係形成しています。このような設計思想ではAIテクノロジー自身も豊かな意味合い・価値観・アイデンティティ等持ち出来る点も特筆すべきです。「Jensen’s strandbeests」 のよう風等自然力利用したデザイン例参考しながら感情面含め多角度対話能力向上させています。

How can AI technology encourage slow attention, adaptive learning, and challenge default anthropocentric moral outlooks?

AI技術は徐々注意集中(slow attention)、適応型学習(adaptive learning)、既定人類中心道徳見解挑戦(challenge default anthropocentric moral outlooks) を奨励する方法提供可能です。 具体的施策:1. 「Fir」みたく決断時反対意見提示 2. 意思決定プロセス時間掛かる仕組み整備 3. 知識不確実性下判断支援 4. ユーザーストレス減少&洞察深化 5. 地球全般影響分析可視化 これら施策導入事例通じて「ecological participation」「rejuvenation」「interconnectedness」「adaptation」「resilience」と言った理念浸透拡大期待されます。