Artificial intelligence

Artificial intelligence (AI) has become an integral part of proactive corporate compliance programs, used in tandem with internal resources to detect and prevent fraud, potential violations of anticorruption or money laundering laws, and other legal corporate compliance risks.

We have been working with our clients in the implementation of AI tools to monitor internal communications, while protecting employee privacy and confidentiality, to assess external transactions and third-party interactions for suspicious patterns and trends, and to conduct due diligence and risk assessments on business partners in high-risk jurisdictions.

AI is also an integral part of our corporate investigation protocol, providing an efficient and cost-effective way to review and analyze large amounts of data, minimizing the impact on internal operations, while allowing a company to respond accurately and timely to a government inquiry or the needs of an internal investigation.

In addition, we embrace the use of AI to monitor, track and review data and to use AI as a strategic tool to help our clients manage the increasingly complex legal compliance requirements across the world.

Artificial intelligence (AI) is a “disruptor” – it is driving the evolution, and even the redundancy, of many existing practices and methods of transacting business across the spectrum of industry sectors. AI’s potential to create change brings with it significant risks and rewards.

Here, we describe the relevance of AI in the context of our Corporate Practice, for both us and our clients.

AI Companies

We apply knowledge and experience drawn from across the range of different financing sources to assist AI companies, often young businesses experiencing rapid growth, to identify and understand the most appropriate sources of capital for growth, not just from traditional routes – such as private equity or venture capital, global equity and capital markets or debt and related financial instruments – but also from emerging sources, such as crowdfunding and Initial Coin Offerings. In implementing investment transactions for our clients, our corporate finance know-how, combined with our understanding of the challenges and needs of AI businesses, helps us understand and prioritize what is important and ensure our clients take investment on the most appropriate terms and with the right protections in place.

Investors and Lenders

For private equity and venture capital investors, as well as for lenders, our familiarity with emerging AI technologies helps us not only to understand business within the sector, as well as sector terminology, but also to appreciate what factors underpin the value of a particular technology. With that knowledge, we can focus due diligence and ensure that finance is provided on the most relevant terms.

Corporate Department Resources

As AI drives evolution in deal execution and risk assessment in the corporate environment, we are committed to maintaining a comprehensive knowledge of the capabilities (and limitations) of the evolving spectrum of available AI resources, so that we know which AI products can most appropriately be deployed to meet the individual needs of our clients, and can advise our clients accordingly. In performing our services for clients, we can use AI to help them obtain competitive advantage and success through:

• Improving risk assessment and valuation

• Maximizing efficiency and value from legal spend

• Minimizing deal execution timescales

• Preserving and enhancing deal value

We can help buyers and investors benefit from the deployment of data analysis software to enhance approaches to due diligence and risk assessment, improving speed, coverage and value.

We can also help sellers to improve sale preparations and preserve value through the sale process, though the use of AI tools to analyse and sort unstructured contract data into relevant categories, and in analyzing data room content ahead of marketing to prospective buyers, so as to facilitate:

• Rapid and early identification of issues

• Assessment of the impact of identified issues on value or timing

• Formulation of strategies to address identified issues

• Maintenance of control by avoiding unwelcome surprises emerging from buyer due diligence

Artificial intelligence (AI) and the communications sector are intimately intertwined, as virtually all aspects of everyday life may be connected to the Internet of Things.

At its core, AI (particularly cloud-based) relies on fast, reliable and secure connectivity to handle large amounts of data. In turn, AI can help reduce the cost and improve the quality and speed of data transmission and connectivity by enabling automatic optimal networks utilization and radio spectrum sharing.

Existing communications regulations and policies generally pre-date the development of robust AI capabilities. They often rely on obsolete concepts based on the Public Telephone Switch Network and, sometimes, may have the inadvertent effect of prohibiting new AI-embedded services and applications. It is no surprise, therefore, that several government agencies in charge of communications regulation and policy around the world are currently considering the role of AI in spectrum sharing, smart network management and in the communications sector in general to ensure that communications regulation and policy do not deter investment in and deployment of AI. Earlier this year, for example, the EU’s Radio Spectrum Policy Group (RSPG) – an advisory group that assists the EU Commission in the development of radio spectrum policy – announced that it plans to discuss the role of AI in spectrum sharing and in the communications field in general over the coming months. It is also likely that as the use of cloud-based AI increases, issues that have long been taken as dead in communications policy, such as open-air vs. in-building coverage, or geographic vs. population coverage as the basis for network licensing, will have to be re-visited, with potentially profound effects on the economics of the communications industry.

Our Communications team is deeply familiar with the potential regulatory and policy issues arising from the use of AI. We can offer creative, business-oriented solutions to mitigate regulatory risks and to shape the policy debate to enable the full potential of AI embedded applications (e.g., autonomous vehicles, drones, smart grid, small cells, etc.). In particular, our experience includes:

• Global experience in spectrum policymaking, allocation and assignment

• Experience in coordinating a seamless global advocacy strategy before the competent regulatory and policy bodies

• Devising global regulatory compliance programs for AI-embedded applications

• Assisting in obtaining regulatory permits or waivers, where needed

• Resolving regulatory disputes and government investigations

• Advising on the regulatory implications of the use of AI for smart network management and spectrum sharing

• World-leading expertise in the development and deployment of intelligent transportation systems and technologies, including autonomous vehicles

Devising and coordinating a seamless global advocacy strategy before the competent regulatory and policy bodies

A revolution of innovation ushered in by artificial intelligence (AI) has swept over the insurance industry in recent years, reshaping how the industry thinks about risk. As just one component of InsureTech (i.e., insurance technology), AI is rapidly changing the way the insurance industry identifies and selects risks, develops products, predicts losses and transacts business.

Examples include web and app-based companies that use AI to guide users through the application process, analytical approaches to emerging risks using “Big Data” and machine learning to identify and predict risks, AI to identify and prevent compliance issues with regulatory and sanctions regimes, and the use of machine learning and AI to make claims determinations. Yet, for as much as AI is helping insurers to place business more efficiently, AI is simultaneously upending traditional liability concepts, forcing the insurance industry to consider events precipitated not by direct human intervention but by machines.

AI also has transformed litigation, something insurance companies manage daily. AI brings the ability to search large data sets and identify relationships accurately and quickly. We have litigators and litigation support specialists who are well versed in predictive coding and AI applications in large litigation and government investigation document productions.

Our interdisciplinary team of insurance, regulatory, litigation, corporate, technology, data privacy, cybersecurity, tort and public policy experts work hand-in-hand to provide insurance and reinsurance industry clients, including industry service providers, with the experience and know-how necessary to bring AI and other InsureTech applications to life. We track legislative and regulatory perspectives and changes as AI is applied to insurance industry processes. Our team includes the former Director of the Ohio Insurance Department, the former CIO of the US, and the former Chief Legal and Governance Officer of Nationwide Mutual Insurance Company.

Our experience includes:

• Advising a major insurance carrier on fluidless underwriting issues.

• Helping major and startup insurance companies on development and regulatory product approvals for innovative homeowners and automotive products.

• Counseling multiline insurance companies on compliance issues associated with electronic and app-based sales and distribution.

• Advising major insurance carriers and a trade association on regulatory issues associated with use of “Big Data” in underwriting and predictive modeling.

Our team brings a value-added proposition to insurance and reinsurance companies and industry service providers seeking to address legal issues surrounding the use of Al specifically, and InsureTech in general.

Competition enforcement agencies around the world are studying the potential impact of artificial intelligence (AI) on market competition. As these enforcement agencies dig deeper and study AI-related activities in the market, questions inevitably will arise and competition uncertainty will likely increase. Questions about whether it is possible for companies to use AI as a means of colluding, with limited or no human involvement, are likely to proliferate. As questions multiply, regulatory scrutiny and competition-related risk are also likely to increase.

What creates the risk?

In an earlier era, antitrust authorities would have looked for evidence produced in a smoked-filled room of competitors. Today, AI could facilitate pricing collusion through price monitoring and matching algorithmic software. While companies may intelligently adapt their prices to those of their competitors, they cannot exchange information on future pricing intentions either directly or indirectly (e.g., through price signaling). This poses a new compliance challenge for companies using price matching and monitoring algorithms or implementing blockchains to implement smart contracts, particularly companies in markets with only a few large competitors. In other examples, AI could facilitate exploitation of market power (through discrimination and bias) or foreclosure of competitors. This can happen through a merger or an exclusive cooperation agreement resulting in the combination of a large and unique set of “Big Data”; or it can happen where a dominant company holding such large and unique set of “Big Data” leverages it to discriminate against its competitors or customers.

At the same time, enforcement agencies recognize that AI also can enhance competition by facilitating targeted marketing and rapid competitive responses to price changes, which may ultimately provide more competition, lower prices and better services for customers.

Where to draw the line?

Defining a benchmark for illegality requires assessing whether any illegal action was anticipated or planned (e.g., through AI programming instructions) or whether a particular outcome could have reasonably been foreseen, even when there has been no human agreement. Some enforcement agencies (notably, the EU Commission) have stated that AI remains under a firm’s direction and control and, therefore, the firm is liable for the actions taken by the algorithm – even if not fully understood by the individuals who developed or used it. Moreover, antitrust agencies have prosecuted and sanctioned mere facilitators of illegal conduct (such as business-to-business service platforms) as if they took part in the illegal conduct itself. Answering the question of who is liable for the decisions and actions of AI is, therefore, far from straightforward; it will depend on the factual context of each case and may vary in different jurisdictions applying different legal tests (which are a separate concept from contractual liability).

Our Antitrust & Competition team is tech perceptive and deeply familiar with the potential issues arising from the use of AI. We can offer creative, business-oriented solutions to mitigate antitrust risks and to disentangle the procompetitive effects of AI from its anticompetitive effects. In particular, our experience includes:

• Defending and bringing complaints against companies in connection with global competition law investigations, including the successful closure of an investigation by the EU Commission and the UK Competition and Markets Authority into an alleged price fixing practice by way of agreed commitments (Global Competition Review Award: Behavioral Matter of the Year – Europe).

• Advising various technology companies in successfully devising global antitrust compliance strategies, including conducting antitrust compliance audit programs and offering remedial reprogramming options for non-compliant algorithms.

• Coordinating global merger control filing strategies in many high-profile transactions, including successfully negotiating a settlement in return for merger control clearance of a multibillion-dollar acquisition involving two global home appliances and Internet of Things manufacturers.

• Advising various private and government clients on regulatory and policy reforms related to the application of antitrust law to the use of artificial intelligence, including speaking on “Artificial Intelligence and Competition Issues” at the International Institute of Communications Telecommunications and Media Forum in Brussels in April 2018.

As in the insurance industry, artificial intelligence (AI) technology is being rapidly adopted in the financial services industry (Fintech) and other highly regulated and compliance-heavy industries (Regtech).

For example, AI has been used to make decisions on offering financial services to consumers and businesses, detecting fraud and automating “Know Your Customer” reviews, as well as anti-money laundering reviews. AI has also streamlined contract reviews for a heavily contracts congested industry, so that banks can better predict which terms have a higher risk of default or have a higher compliance red flag.

One significant area of Fintech that may benefit from AI in the coming years is blockchain and smart contracts. Currently, smart contracts are gaining prominence because of the emergence of blockchain technology and the popularity of cryptocurrency. In particular, there is more acceptance of the verification of transactions on a public or private blockchain. A blockchain is a sophisticated technology for a distributed ledger that, in theory, more securely records transactions. Smart contracts are programs that execute based on parameters agreed by two or more counterparties. Smart contracts may either be coded entirely in standalone software, coupled with a traditional agreement, or partially governed by rules agreed to by the parties. Smart contracts have been used or are proposed to be used in various industries, in particular, the banking industry.

AI technologies that can be applied to smart contracts range from rule-based systems such as expert systems designed to make decisions based on rules and input, to more adaptive systems, such as neural networks, knowledge graphs and logic. One area important for smart contract is natural language processing (NLP). AI and NLP could be used with smart contracts in at least two aspects: to negotiate and agree to terms on behalf of people and to generate the smart contracts. These contracts may be programmed to negotiate terms for price and quality of certain goods using well-known AI game-playing algorithms. Parameters can be established for certain gap filler terms, such as ranges of price and range of quality that can be adjusted dynamically, and fixed inputs by users for various types of goods. But they are no panacea. Care should be taken, for example, to record that an offer and acceptance to the terms of the agreement have been provided by the smart contract, and AI-powered negotiation acting as agents on behalf of real people, as offer and acceptance is one of the cornerstones of a binding contract.