Artificial Intelligence and your business: A guide for navigating the legal, policy, commercial, and strategic challenges ahead

Artificial Intelligence and your business: A guide for navigating the legal, policy, commercial, and strategic challenges ahead April 2018 Contributing source: Artificial Intelligence The Next Digital Frontier?, McKinsey Global Institute, 2017 2 Hogan Lovells Global hotspots Silicon Valley — Top global hub for startups with 12,000+ active startup businesses — Global leader for venture capital (VC) investment — Headquarters of many top high- tech companies Boston — Long history of cooperation between science and industry — World-class universities such as MIT developing advanced technologies and providing a talent pipeline New York — Leading hub for financial and media industries — Strong funding ecosystem, second in the world after Silicon Valley for absolute number of early stage investments London — Global finance center, supporting both investment and FinTech applications — European leader of VC startup investments China — Leading in volume of academic research output in AI coming from universities — AI identified as a strategically important technology by the Chinese government Washington, D.C. — Leading center for U.S. policy and regulation of Artificial Intelligence (AI), including health, automotive, space, drones, and education AI in industry Unmanned Aircraft Systems (Drones) 4 Smart Homes 6 Autonomous and Connected Vehicles 8 Space and Satellite 10 Life Sciences 12 EU Robotics 15 FinTech 16 Education 18 Ethics in AI 19 Areas to consider in AI development and contracts AI in the Asia Pacific 22 Drafting contracts with AI 24 Privacy and Cybersecurity 26 Product Liability 28 Intellectual Property 30 Telecommunications 32 Media Regulation 33 Antitrust 34 Export Controls 35 3Artificial Intelligence and your business: A guide for navigating the legal, policy, commercial, and strategic challenges ahead What is Artificial Intelligence? Virtually every industry is being reshaped with the use of Artificial Intelligence (AI) and advanced machine-learning, ranging from healthtech to self-driving vehicles, to education and smart homes, drones and space, social media, and everything in between and beyond. These new technologies present a variety of commercial opportunities and the potential to change our daily lives. At the same time, new AI innovations bring many legal, policy, commercial, and strategic challenges that need to be considered thoughtfully across jurisdictions. In some instances, existing frameworks can be applied or adapted. For others, new paradigms and robust safeguards may be needed. And as machine- learning technologies continue to evolve, organizations will need dynamic, sophisticated compliance approaches. In this guide, we highlight several of the key challenges and commercial opportunities for AI and advanced machine-learning. Randy Segal Partner, Northern Virginia, Silicon Valley, Washington, D.C. T +1 703 610 6237 randy.segal@hoganlovells.com Mark Brennan Partner, Washington, D.C. T +1 202 637 6409 mark.brennan@hoganlovells.com Richard Diffenthal Partner, London T +44 20 7296 5868 richard.diffenthal@hoganlovells.com Unmanned aircraft systems (UAS or drones) technology has moved forward rapidly in recent years, and what used to be considered toys are quickly becoming powerful commercial tools that can provide enormous benefits in terms of safety and efficiency. Consulting firms suggest that the estimated global market for commercial UAS technology applications currently stands at about US$2 billion, could increase to US$120 billion by 2020. Advances in AI and machine-learning technology are allowing UAS to see and act like human pilots, and to process huge amounts of data in real time. Whether UAS are performing search and rescue missions, allowing farmers to be more efficient and environmentally friendly, inspecting power lines and cell towers, surveying and mapping large swaths of land, or performing package deliveries, AI is allowing drones to become more automated, safer, and efficient. The applications for AI in the drone industry are limited only by our collective imagination. The use-cases range from data analytics for industrial infrastructure inspections to navigating warehouses efficiently and everything in between. Real-time data analytics AI is allowing drones to collect and process huge volumes of data in real time. Aerial imagery that used to take humans hours, days, or weeks to review and analyze is being streamlined and automated by AI that strategically determines what kind of data and images are important enough to collect, and can simulate a human looking at thousands or millions of images. For example, drones being used to perform railway inspections can use a variety of onboard sensors (cameras) to inspect track conditions and identify defects that are invisible to the naked eye. Once detected, AI software can be used to provide recommendations on what, if any, maintenance may be necessary. Sense-and-avoid A pilot manually flying a drone should be able to avoid obstacles like buildings or other aircraft; but what happens if a drone loses all connectivity? To fully enable many of tomorrow’s most promising use-cases, drones will need to be capable of flying autonomously without human intervention, and this will require drones to be able to sense obstacles and react in time to avoid a collision. Computer vision, plus machine learning, is helping drones navigate more effectively by allowing drones to see the world as humans do. AI software is enabling drones to fly autonomously, even in the dark, obstacle-filled environments or beyond the reaches of GPS or other methods of connectivity. Unmanned Aircraft Systems (Drones) Swarm technology AI technology is enabling swarms of tens or hundreds of drones to operate entirely autonomously. The swarm collaborates by staying in constant communication with itself and by changing its configuration to complete the mission if any one drone is lost. Situational awareness AI is enabling better situational awareness and changing the way drones are able to interact with objects in their environment. In the not-too-distant future, AI technology will enable fully autonomous drone operations. Civil airworthiness authorities around the world maintain air safety by placing ultimate responsibility for safe operation of aircraft on the entity operating the aircraft and on the human pilot. Since fully autonomous drones will not have a human pilot, countries around the world will need to put in place policies, laws, and regulations that fully address this profound change. Lack of human judgment Fully autonomous drones will raise important policy questions regarding the removal of human judgment from the equation. Human pilots make not only safety-related decisions, but in certain circumstances — especially emergencies — moral and ethical decisions, such as whether to crash land in a heavily populated area versus a less populated one. With the removal of the human pilot and human judgment, what level of AI will be needed for drones to learn from experience and use that learned knowledge to make appropriate moral and ethical judgments? Security Who will have the legal responsibility to maintain the security of a fully autonomous drone, and to ensure that its automation, navigation, and communications systems are not hacked into? Regulatory and civil liability What if something goes wrong with a fully autonomous drone? Who will be responsible from a regulatory compliance and civil liability perspective in the event of an incident involving personal injury or property damage, or a failure to comply with rules and regulations? 5Artificial Intelligence and your business: A guide for navigating the legal, policy, commercial, and strategic challenges ahead Matthew Clark Senior Associate, Washington, D.C. T +1 202 637 5430