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AI Litigation Surge Anticipated
PLUS: EP Senior Policy Adviser Leaks EU AI Act Revisions
Veronica Lopez
April 18, 2024
Hold onto your seats as we navigate through the impending surge in AI-related litigation, spotlighted by top legal minds at a University of California, Berkeley symposium. From the halls of the U.S. and European patent offices grappling with AI inventions, to the American Bar Association's insights on using AI to sharpen merger analyses, we’re here to guide you through the complexities of AI's legal landscape. And with IBM dominating 2023’s AI patent race, the stakes are higher than ever. Are you ready for the deep dive into this legal whirlwind? Let’s get started! 🚀👩⚖️
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In a recent symposium at the University of California, Berkeley, legal experts warned of an impending wave of litigation related to AI, stemming from its expanding use and the ongoing tightening of regulations. These developments are likely to challenge courts to examine the opaque inner workings of corporate AI algorithms to assign liability.
Key takeaways include:
Nicole A. Ozer of the ACLU highlighted that with over 30 AI-related bills under review in California, significant regulatory changes are expected by September, indicating a shift towards more civil litigation involving AI algorithms.
A significant joint letter from multiple U.S. regulatory agencies reinforces that AI technologies are not exempt from existing laws, signaling to companies the importance of ensuring their AI systems comply with national standards to avoid potential legal consequences.
The European Union has recently enacted the Artificial Intelligence Act, setting up the world's first regulatory framework specifically for AI algorithms, with expectations to influence global AI practices.
Experts anticipate a rise in AI-related litigation across various domains, including intellectual property, privacy, employment decisions, and consumer disputes.
There is a call for more precise and accurate legal language concerning AI technology, as rapid advancements in generative AI are likely to reshape legal disputes in the near future.
Michele E. Gilman noted a lack of transparency in the use of AI by companies, leading to discriminatory decisions affecting employment, housing, and healthcare, which are often contested in small claims courts where judges may lack the expertise to adjudicate AI-related issues.
The courts are debated on whether they should delve into the workings of AI algorithms during litigation, with opinions divided on whether understanding the technology is necessary for resolving disputes.
Concerns are raised about the potential misuse of AI in legal settings, such as allegations of evidence being deepfakes, which could complicate litigation and drive up costs.
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In 2023, IBM secured the top position for obtaining the most U.S. artificial intelligence patents, outpacing its nearest competitors by a substantial margin. According to a report by Harrity Patent Analytics, IBM leads with a significant lead in both AI utility patents and total AI patents held. This dominance in patent acquisition underscores IBM's robust position in the AI technological landscape and highlights the competitive efforts by other leading tech companies.
Key takeaways include:
IBM's Leadership in AI Patents: IBM obtained 1,211 AI utility patents in 2023, making it the leader among tech companies. This is over 300 patents ahead of its closest competitor, Google, which secured 870 patents.
Top Competitors: Following IBM and Google, Samsung, Amazon, and Microsoft also featured prominently in the list of top AI patent holders, demonstrating the breadth of involvement in AI development across different sectors of the technology industry.
Comprehensive AI Patent Holdings: When considering the total number of AI patents held, IBM still leads with nearly 7,000 patents. Google, Microsoft, Samsung, and Amazon follow, with Intel also appearing prominently in the rankings.
Patent Application Landscape: Samsung, IBM, and Google each have around 2,000 pending AI patent applications, with Samsung slightly leading. This indicates a strong future positioning for these companies in the AI patent space.
Blocking Patents: Google and Microsoft are noted for holding the most patents that could block other companies' applications due to covering essential technologies or methods, with 1,145 and 1,068 blocking patents respectively.
Impact of Blocking Patents: The companies with significant numbers of blocking patents, such as IBM, Samsung, and Microsoft, are also notably affected by others' blocking patents, which could complicate future developments and patent filings in the AI sector.
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Applicants seeking patent protection for AI inventions at the U.S. Patent and Trademark Office (USPTO) and the European Patent Office (EPO) face differing requirements and scrutiny. As AI technologies evolve, the focus on disclosure standards in both jurisdictions has intensified, reflecting the challenges in defining what constitutes sufficient information to secure a patent.
Key takeaways include:
The USPTO and EPO have different criteria for the sufficiency of disclosure required for AI-related patents, influenced by their respective legal frameworks.
In the U.S., detailed implementation specifics, such as the types of training data and the AI algorithms used, must be disclosed to demonstrate possession of the invention.
European standards are stricter, often necessitating a detailed description of AI architecture and the technical problem solved by the invention.
Decisions by the Patent Trial and Appeal Board (PTAB) illustrate the necessity for precise, technical disclosure to support AI patent claims, emphasizing specific algorithmic details linked to the desired outcomes.
Several PTAB decisions have denied patent claims due to insufficient written descriptions, showing the challenges in meeting these stringent requirements.
The EPO’s Technical Boards of Appeal (TBA) apply a more rigorous standard, often refusing patents that the USPTO might approve, due to insufficient explanations of how AI processes achieve their claimed technical effects.
The TBA decisions underscore the importance of characterizing training data and providing clear descriptions of the AI's functional details to facilitate reproducibility by a skilled artisan.
Both the USPTO and EPO emphasize the necessity for patent applications to explicitly detail how AI inventions achieve specific results, reflecting a broader trend towards greater specificity in patent documentation for emerging technologies.
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At the recent American Bar Association's antitrust spring meeting in Washington, D.C., discussions focused on the integration of AI and machine learning tools in traditional merger analyses. The enhanced capabilities and methodologies that these tools provide are helping to bridge the gap between the advanced techniques used by businesses and those employed by regulatory agencies. This development marks a significant evolution in the analytical processes used in competition law enforcement across both the United States and Europe.
Key takeaways include:
Enhanced Precision and Localization: AI and machine learning enable more granular and localized analysis in merger cases, utilizing large amounts of data from various sources such as SKU-level sales or consumer behavior tracking. This allows for more precise and geographically specific insights into the impacts of mergers.
Improved Entry Analysis: Traditional entry analysis can be significantly enhanced using big data, offering deeper insights into how new competitors affect market dynamics and consumer behavior, which helps in understanding the competitive pressures in various local markets.
Advanced Demand Estimation: AI tools allow the use of complex datasets to control for numerous variables, reducing the risk of omitted variable bias and allowing for a more accurate estimation of price elasticity and demand shifts due to price changes.
Sophisticated Merger Simulation: AI can improve the realism of merger simulations by integrating dynamic and complex models of consumer response and competitive interaction, moving beyond simplistic assumptions used in traditional models.
Detailed Analysis of Merger Efficiencies: AI techniques facilitate a deeper understanding of how mergers can optimize supply chain efficiencies and cost structures, potentially leading to direct consumer benefits such as reduced prices or improved service delivery.
Utilization of Big Tech Techniques: The use of advanced algorithms that process vast amounts of data in real time provides a clearer picture of market dynamics and consumer preferences, which can be critical in assessing the substitutability and competition levels in various market segments.
Need for Enhanced Data Capabilities: To effectively deploy these advanced analytical tools, agencies and their economic advisers must develop substantial capabilities in data engineering, econometrics, and understanding of both proprietary and third-party data sources.
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