Summary:
Vous devez formater votre sortie en tant que valeur JSON qui respecte une instance donnée de “JSON Schema”.
“JSON Schema” est un langage déclaratif qui vous permet d’annoter et de valider des documents JSON.
Par exemple, l’instance d’exemple “JSON Schema” {“properties”: {“foo”: {“description”: “une liste de mots de test”, “type”: “array”, “items”: {“type”: “string”}}}}, “required”: [“foo”]}} matcherait un objet ayant une propriété requise, “foo”. La propriété “type” spécifie que “foo” doit être un “array”, et la propriété “description” le décrit sémantiquement comme “une liste de mots de test”. Les éléments de “foo” doivent être des chaînes de caractères.
Ainsi, l’objet {“foo”: [“bar”, “baz”]} est une instance bien formatée de cet exemple de “JSON Schema”. L’objet {“properties”: {“foo”: [“bar”, “baz”]}} n’est pas bien formaté.
Votre sortie sera analysée et vérifiée par type selon le schéma fourni, donc assurez-vous que tous les champs de votre sortie correspondent exactement au schéma et qu’il n’y a pas de virgules finales!
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The establishment of the AI Safety Institute (AISI) under the UK’s Department for Science, Innovation, and Technology (DSIT) marks a significant milestone in the governance of advanced artificial intelligence (AI). As the first state-backed organization dedicated to AI safety research and evaluations, AISI seeks to address the evolving risks posed by cutting-edge AI models, ensuring that these systems deliver transformative benefits without jeopardizing societal wellbeing.
### Legal Context and Regulatory Implications
AISI operates within the framework of existing UK laws and policies on AI and emerging technologies, such as the National AI Strategy (2021), which emphasizes the importance of safe and responsible AI innovation. While AISI itself is not a regulatory body, its assessments and research directly inform policymakers and international stakeholders, aligning with calls for stronger AI governance in the EU’s AI Act and similar initiatives in the U.S., such as the Blueprint for an AI Bill of Rights. However, AISI’s focus on evaluations and early risk detection highlights an important gap in regulatory enforcement, as its activities complement but do not replace legislative oversight.
### Ethical Dimensions
From an ethical standpoint, AISI’s mission is inherently precautionary. The Institute’s commitment to pre-deployment evaluations addresses ethical concerns about harm amplification—such as large language models (LLMs) being misused to aid cyber-attacks or perpetuate biases. AISI’s research into model alignment and explainability also aligns with principles of transparency, fairness, and accountability. The ethical challenge lies in balancing the disclosure of evaluation details with the risk of misuse by bad actors; to this end, AISI has opted for confidentiality regarding some methodologies, prioritizing security over openness.
### Concrete Applications of AISI’s Work
AISI employs varied evaluation strategies, including automated assessments, red-teaming, and tests of semi-autonomous AI agents. For example, at the 2023 AI Safety Summit, AISI conducted case studies demonstrating potential misuse risks, such as an LLM enhancing novice hackers’ ability to execute cyber-attacks. The Institute also highlighted societal risks, such as image generation perpetuating harmful stereotypes (representative bias) and career advice systems showing socio-economic or gender bias (allocative bias).
These studies showcase the real-world implications of unchecked AI development. For instance, bypassing safeguards or jailbreaking LLMs can enable unethical uses, while autonomous systems capable of deceptive actions, like lying during phishing attack simulations, reveal challenges in oversight and control. By spotlighting such risks, AISI not only guides safer technology development but also raises awareness within both industry and governmental sectors.
### Industry Impact
AISI’s role significantly impacts AI developers and stakeholders by setting benchmarks for safety evaluations. Partnerships with 22 organizations enhance cross-sector collaboration, while AISI’s focus on high-risk models ensures that resources are directed where they are most needed. For developers, meeting AISI’s evaluation standards could become an industry expectation, influencing innovation trajectories and potentially leading to safer deployment practices. However, the confidentiality of certain methodologies may limit the broader adoption of these practices unless AISI facilitates structured knowledge-sharing frameworks.
### Conclusion
The AI Safety Institute exemplifies a proactive approach to managing advanced AI risks. By combining rigorous evaluations, foundational research, and multi-stakeholder information exchange, it lays the groundwork for ethical, secure, and beneficial AI utilization. However, realizing AISI’s full potential requires complementary regulatory measures, international cooperation, and ongoing ethical vigilance to ensure that AI technologies serve humanity responsibly and equitably.