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Integrating AI tools into legal practice without compromising the security of sensitive client information is a paramount concern. In this article, we'll examine how AI is revolutionizing certain aspects of legal work, while offering best practices for employing these technologies and providing guidance for legal professionals in selecting the right AI products and service providers.
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Because AIs are capable of causing harm but cannot be a legal entity, they are not held accountable by court action. Several current and future possibilities exist to resolve AI difficulties. Current options involve identifying indirect liability. Future options include but are not limited to changing the law to make an AI a legal person and/or changing the law to make AI programing an ultra-hazardous activity.
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AI is designed to accomplish goals specified by and receive directions from a human being. Thus, it has been suggested that either direct or vicarious liability may be applied to hold the human programmer who wrote the software algorithms liable for the damages caused by the AI agent.
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The concentration of dominant technology firms could harm U.S. national interests and global leadership, Federal Trade Commission Chair Lina Khan said in March at a Carnegie Endowment for International Peace event.
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Most of the legal industry has by now boarded the generative artificial intelligence train, filling up conference sessions dedicated to the topic, testing new legal technology solutions and exploring the emerging legal questions that the technology will pose. But most of their questions about generative AI are still unanswered.
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It's time to stop the hype, stop talking up AI as if it's the next best thing since sliced bread and prove that it's a useful tool and technology that can actually be used in the actual practice of law.
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The DOJ is likely to face many practical challenges and novel issues as it begins coding its own algorithm for AI-related enforcement. This article briefly examines three areas of AI-related enforcement where such practical challenges and novel issues may arise.
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The cybersecurity landscape is on the brink of a transformative shift, with predictive analytics and behavioral analysis leading the charge for more resilient and adaptive defenses.
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The possibilities for patenting innovative applications of multimodal models across industries are endless.
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Hackers are using AI to sift large digital data to identify more convincing approaches for their scams as well as weaknesses in weaknesses in software coding or network security.