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Insight | Thought Leadership

TMT Looking Ahead 2022

This five-part series explores key themes, offers timely insights, and lays out recommendations for TMT companies looking to navigate the latest industry developments.
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Artificial intelligence and machine learning (AI/ML) technology is driving many important new business opportunities across several industry sectors and will be an even more essential technology in the future. Companies are using it to implement home automation, industrial applications, smart cities and more. Apart from being an increasingly crucial technology, artificial intelligence and machine learning are projected to experience unprecedented market growth in the next few years, making the legal and regulatory environment around them complex and dynamic. This growing trend toward regulation comes in the wake of recent controversies exposing the risks of AI, most recently algorithmic bias and the use of facial recognition technology.

Successful investment in AI/ML
Emerging new AI-related technology is helping to create innovative solutions, services and business models. It is important to focus on specific due diligence requirements for AI/ML software around AI M&A deals. At the same time, compliance with data protection laws is at the center of AI deals, particularly around the origin and handling of data. Key to successful deals are antitrust issues, especially around the assessment of the competitive nature of the transaction and the scope of foreign investment regimes (e.g., CIFUS), as well as national security concerns. A team comprised of cross-functional experts to perform due diligence, draft and negotiate acquisition agreements, transition services and provide IP data license agreements is essential for a successful AI-related deal.

Evolving privacy for AI/ML strategies
Technology has made it easier than ever to collect, use and transfer data across borders. Consider how smart factories and smart cities or autonomous vehicles collect and process huge volumes of data. This raises key privacy issues, including around how this data is collected and processed, where it is stored and the ability to transfer to, and process it in, other jurisdictions. AI/ML is fueled by personal data. In order to improve user experience, it collects a vast amount of data from consumers, which means privacy law requirements on collection, handling, and transfer of data applies to it. This reliance on data also leads to issues on bias, accountability, autonomy, and ethics, making transparency of automated ML decision-making a key focus for regulators, AI/ML developers and researchers, as well as the media.

Protecting your AI/ML assets
Patent litigation is increasing in an ever-evolving digital landscape. AI technology comprises multiple patented innovations essential to meet industry standards. Patent litigation strategies need to be global, proactive, agile and informed by local intelligence. From competition issues raised by technological standardization to the cross-over between IP and regulatory protection across sectors, it means that a broad range of legal and technical expertise is essential to develop an effective patent litigation strategy. In addition to establishing the ownership of AI inventions, it is important to consider the patentability of algorithms, focusing on trade secrets protection and its maintenance. Crucial to AI IP protection are open source software licence audits and the review of data ownership and licensing.

Seamless, comprehensive advice on your AI-related tax strategy
As companies digitally transform and innovate to simultaneously drive and respond to market demands around AI and other technologies, the definition of a "digital" company continues to evolve in tax law. Important questions arise around the use of AI, for example, is machine learning data taxed as an asset?