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메릴랜드 법무장관, 즉각적인 연방 AI 규제 촉구 초당파 연합 합류

메릴랜드주 법무장관 앤서니 G. 브라운(Anthony G. Brown)은 의회에 인공 지능에 대한 연방정부의 신속한 감독을 제정할 것을 촉구하는 26개 주 연합에 주정부가 참여한다고 발표했습니다.

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Source-provided image accompanying Maryland attorney general joins bipartisan coalition urging immediate federal AI regulation
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mocoshow.com
소스 링크
mocoshow.comhttps://mocoshow.com/2026/09/27/maryland-joins-bipartisan-coalition-seeking-immediate-federal-ai-regulation/
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주요 용어

인공지능(AI)
패턴 인식, 추론, 언어 또는 의사 결정이 필요한 작업을 수행하는 시스템 구축의 광범위한 분야입니다.
AI 안전
AI 시스템의 유해한 행동, 실패, 오용 위험을 줄이는 데 중점을 둔 분야입니다.
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무슨 일이 일어났나요?

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.

소스 세부정보: mocoshow.com ↗

왜 중요한가요?

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

대화형 메커니즘: 실제로 작동하는 방식

이 개발의 이면에 있는 기본 기술을 대화식으로 살펴보세요.

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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다음에 무엇을 볼 것인가

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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