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馬裡蘭州總檢察長加入兩黨聯盟,敦促立即實施聯邦人工智慧監管

馬裡蘭州總檢察長安東尼·G·布朗 (Anthony G. Brown) 宣布該州加入 26 個州聯盟,呼籲國會對人工智慧迅速實施聯邦監管。

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Source-provided image accompanying Maryland attorney general joins bipartisan coalition urging immediate federal AI regulation
來源參考來源記錄
出版商
mocoshow.com
來源連結
mocoshow.comhttps://mocoshow.com/2026/09/27/maryland-joins-bipartisan-coalition-seeking-immediate-federal-ai-regulation/
來源類型
連結來源-主要來源狀態尚未確定。
背景60 秒內了解這一點

從這裡開始

關鍵術語

人工智慧(AI)
建構執行需要模式識別、推理、語言或決策的任務的系統的廣泛領域。
人工智慧安全
該領域專注於減少人工智慧系統中的有害行為、故障和誤用風險。
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發生了什麼事

Maryland Attorney General Anthony G. Brown signed on to a bipartisan coalition of 25 other state attorneys general that is urging Congress to pass immediate federal regulation of the artificial‑intelligence industry.

On September 27, 2026, Maryland Attorney General Anthony G. Brown announced that the state had joined a bipartisan coalition of 25 other state attorneys general. The coalition, formed earlier this year, is urging Congress to enact immediate federal regulation of the artificial‑intelligence (AI) industry.

In its public statement, the coalition cited recent incidents involving AI agents—such as autonomous decision‑making failures and unverified outputs—as evidence of systemic risk. The attorneys general are calling for three core federal actions: (1) mandatory ‑testing and standards, (2) transparent, government‑led incident response processes with publicly released findings, and (3) an independent safety leadership body insulated from commercial profit pressures.

The coalition also emphasizes the need for international cooperation to manage the pace of AI development and to prevent the emergence of harmful superintelligence. While seeking federal oversight, the group wants Congress to preserve existing state AI statutes and to allow state officials to enforce the new federal protections.

來源詳情: mocoshow.com ↗

為什麼這很重要

The coalition’s appeal reflects growing concern among state officials that existing AI oversight is fragmented and insufficient to address emerging risks such as unsafe AI agents, opaque incident response, and profit‑driven safety compromises. By pushing for federal safety‑testing standards, transparent government‑led incident reporting, and an insulated safety leadership, the group seeks to create a uniform baseline that can be enforced nationwide while preserving state‑level enforcement powers. The call for international cooperation underscores fears that unchecked AI advancement could lead to competitive races and the emergence of harmful superintelligence, issues that state‑level actions alone cannot mitigate.

The coalition’s demand marks a significant escalation in state‑level pressure on the federal government to act, moving the conversation from advisory reports to a coordinated political push. Uniform federal standards could reduce regulatory arbitrage, where companies exploit gaps between state laws, and could provide clearer compliance pathways for AI developers.

By insisting on an independent safety leadership insulated from profit motives, the coalition aims to address concerns that industry self‑regulation may prioritize market growth over public safety. This mirrors broader debates about the appropriate balance between innovation and risk mitigation in rapidly evolving AI technologies.

International cooperation, as highlighted by the coalition, is crucial because AI development is a global enterprise. Without coordinated standards, a race to the bottom could emerge, with jurisdictions competing on lax oversight, potentially accelerating the deployment of unsafe systems.

Interactive Mechanism

互動機制:它實際上是如何運作的

以互動方式探索這項發展背後的基礎技術。

Agent Lifecycle Stage:
1
User Intent & Planning: "Audit customer refund request #4092 and settle payment."
2
Tool Calling: Emits structured JSON call crm_get_transaction(id='4092').
3
Guardrail & Verification:🛡️ Paused: High-value action requires human operator sign-off.
4
Final Settlement: Refund recorded, email receipt dispatched, and audit log stored.
Core takeaway: An AI agent is not just a language model—it is a closed loop of planning, tool invocation, and environment feedback. Production systems require self-healing retries and strict human approval guardrails.
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接下來看什麼

Watch for congressional response to the coalition’s letter, including any hearings, draft legislation, or bipartisan bills that may emerge. Monitor whether other states join the coalition, and track any executive‑branch actions that could pre‑empt or complement the proposed federal framework. The coalition’s push for preserving state AI laws may also generate legal challenges or negotiations over jurisdictional authority.

Congressional hearings or bills that reference the coalition’s letter, especially any bipartisan proposals for testing and incident‑response frameworks.

Potential expansion of the coalition as additional states may join, increasing political leverage and possibly prompting a coordinated state‑federal enforcement model.

Executive‑branch initiatives, such as directives from the White House Office of Science and Technology Policy, that could align with or diverge from the coalition’s recommendations.

Legal challenges concerning the interplay between federal AI regulations and existing state statutes, which could shape the ultimate scope of enforcement authority.

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