Paxton is an innovative legal technology firm transforming the legal landscape. Our vision is to equip legal professionals with an AI assistant that supercharges efficiency, enhances quality, and enables extraordinary results.
Developer of an document review platform designed to help law firms automate the reviewing process and find relevant evidence. The company's platform uses artificial intelligence to find evidence to support clients' cases, instantly view events timelines, autogenerate tags, and auto-categorize documents, helping lawyers to unearth critical evidence, and auto-generate comprehensive timelines.
DocLens.ai is a Software as a Service (SaaS) platform that leverages artificial intelligence (AI) and machine learning (ML) to assist insurance professionals in managing legal risks associated with liability claims and complex document reviews. The platform is designed to process both structured and unstructured data, including various types of documents, to extract critical information and provide actionable insights.
Wexler establishes the facts in any contentious matter, from an internal investigation, to international litigation to an employee grievance. Disputes of any kind rely on a deep understanding of the facts. With Wexler, legal, HR, compliance , forensic accounting and tax teams can quickly understand the facts in any matter, reducing doubt, saving critical time and increasing ROI, through more successful outcomes and fewer written off costs.
DeepJudge is the core AI platform for legal professionals. Powered by world-class enterprise search that serves up immediate access to all of the institutional knowledge in your firm, DeepJudge enables you to build entire AI applications, encapsulate multi-step workflows, and implement LLM agents.
Alexi is the premier AI-powered litigation platform, providing legal teams with high-quality research memos, pinpointing crucial legal issues and arguments, and automating routine litigation tasks.
RAND's analysis examines whether AI-generated works merit copyright protection and if training AI models on copyrighted content violates U.S. and international law. The research reveals emerging global divergence, with Beijing courts recognizing copyright for AI-generated images demonstrating human intellectual effort while U.S. approaches remain uncertain pending landmark litigation like NYT v. OpenAI. This comprehensive legal assessment provides critical insights for content creators and AI developers navigating the unresolved fair use questions that will determine billions in potential liability and the future of generative AI training practices.
RAND's policy analysis examines how AI algorithms' opacity creates fundamental privacy challenges around data collection, use, and decision-making, particularly in light of the EU AI Act's transparency requirements. The research explores regulatory approaches to address AI's lack of explainability while examining the tension between innovation and privacy protection under frameworks like GDPR and emerging U.S. state privacy laws. This authoritative government-sponsored research provides crucial insights for policymakers grappling with AI's transformative impact on privacy rights and the effectiveness of existing privacy legal frameworks.
FinTech Weekly's analysis contrasts global AI regulatory approaches, from the EU AI Act's comprehensive framework with €35 million penalties to China's strict government control versus Japan's flexible industry self-regulation. The piece examines Trump's pro-AI executive orders reversing Biden's 'safe and secure' AI policies while highlighting how financial services firms increasingly look to EU standards as 'best practice' despite compliance costs. This industry perspective illuminates the political volatility around AI regulation and the challenges facing financial services firms navigating divergent international regulatory philosophies.
Banking industry analysis reveals that while the EU AI Act establishes the world's first AI regulation in 2024, U.S. financial institutions face a 'fast-moving target' for compliance as regulatory frameworks remain unsettled. The assessment highlights SEC efforts to address AI conflicts of interest for investment advisors while emphasizing that loose regulatory frameworks create significant risks if AI isn't implemented diligently. This practitioner-focused piece underscores how compliance officers must monitor evolving AI requirements to protect data safety and security amid shifting national and global regulatory concerns.
Skadden's comprehensive analysis examines how the EU AI Act will govern financial services from 2024 while U.S. regulators rely on existing frameworks and guidance rather than new AI-specific legislation. The report details critical regulatory concerns including data quality, model risk, governance challenges, and consumer protection as 79% of UK financial firms have deployed machine learning applications beyond pilot phases. This authoritative assessment demonstrates the divergent regulatory approaches across jurisdictions and highlights the industry demand for harmonized international standards as financial institutions navigate complex compliance requirements.
Cooley's analysis reveals how Trump's reversal of Biden's AI policies eliminated federal guidance on workplace wearables and algorithmic bias, yet underlying anti-discrimination laws like Title VII and ADA remain fully applicable to AI systems. The piece emphasizes that while agencies removed AI-specific guidance documents, employers still face liability for discriminatory AI outcomes, particularly as state laws like Colorado's SB 24-205 impose additional AI compliance requirements. This practical legal assessment helps employers navigate the regulatory gap between federal policy shifts and persistent legal obligations in AI deployment.