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Top Risks for Businesses in 2024

Just weeks into 2024, it is already clear that uncertainty will be the watchword. Will the economic soft landing of 2023 persist into 2024? Will labor unrest, strong in 2023, settle down as inflation cools? Will inflation remain tamed? Will the U.S. elections bring continuity or a new administration with very different views on the role of the U.S. in the world and in regulating business?

Uncertainty is also fueling a complex risk environment that will require monitoring global developments more so than in the past. As outlined below, geopolitical risks are present, multiple, interconnected and high impact. International relations have traditionally fallen outside the mandate of most C-Suites, but how the U.S. government responds to geopolitical challenges will impact business operations. Beyond additional disruptions to global trade, businesses in 2024 will face risks associated with expanding protectionist economic policies, climate change impacts, and AI-driven disruptors.

Geopolitical Tensions Disrupting Global Trade

The guardrails are coming off the international system that enshrines the ideals of preserving peace and security through diplomatic engagement, respecting international borders (not changing them through military might) and ensuring the free flow of global trade. In 2022, the world was shocked by Russia’s invasion of Ukraine, but it has taken time for the full impact to reverberate through the international system. While political analysts write on a “spillover of conflict,” the more insidious impact is that more leaders of countries and non-state groups are acting outside the guardrails because they are no longer deterred from using military force to achieve political goals, making 2024 ripe for new military conflicts disrupting global trade beyond the ongoing war in Europe.

In October 2023, Hamas launched a war from Gaza against Israel. Thus far, fighting has spread to the West Bank, between Israel and Lebanese Hezbollah in the north, and to the Red Sea, with Iranian-backed Houthis attacking shipping through the strategic Bab al Mandab strait. Container ships and oil tankers, to avoid the risks, are re-routing to the Cape of Good Hope, adding two weeks of extra sailing time, with the associated costs. Insurance premiums for cargo ships sailing in the eastern Mediterranean have skyrocketed, with some no longer servicing Israeli ports. Companies and retailers with tight delivery schedules are switching to airfreight, which is expected to drive up airfreight rates.

Iran, emboldened by its blossoming relationship with Russia as one of Moscow’s new arms suppliers, is activating its proxy armies in Yemen, Iraq, Syria and Lebanon to attack Western targets. In a two-day period in January 2024, the Iran Revolutionary Guards directly launched strikes in Syria, Iraq and Pakistan. Nuclear-armed Pakistan retaliated with a cross border strike in Iran. While there are many nuances to these incidents, it is evident that deterrence against cross-border military conflict is eroding in a region with deep, festering grievances among neighbors. Iran is in an escalatory mode and could resume harassing shipping in the Persian Gulf and the strategic Strait of Hormuz, where about a fifth of the volume of the world’s total oil consumption passes through on a daily basis.

In East Asia, North Korea is also emboldened by the changing geopolitical environment. Pyongyang, too, has become a major supplier of weaponry to Moscow for use in Ukraine. While Russia (and China) in the past have constructively contained North Korean predilection for aggression against its neighbors, Supreme Leader Kim Jong Un may believe the time is ripe to change the status quo. Ominously, in a Jan. 15 speech before the Supreme People’s Assembly (North Korea’s parliament), Kim rejected the policy of reunification with South Korea and proposed incorporating the country into North Korea “in the event of war.” While North Korean leaders frequently revert to brinksmanship and aggressive language, Kim’s speech reflects confidence of a nuclear power, aligned with Russia against a shared adversary – South Korea, which is firmly aligned with the G7 consensus on Russia. A war in the Korean peninsula would be felt around the world because East Asia is central to global shipping and manufacturing, disrupting supply chains, as well as the regional economy.

China is also waiting for the right moment to “unite” Taiwan with the mainland. Beijing has seen the impact of Western sanctions on Russia over Ukraine and has been deterred from aiding the Russian war effort. In many ways, China has benefited from these sanctions and the reorientation of global trade. Also, Russia, with its far weaker economy, has proven surprisingly resilient to sanctions, another lesson for China. Meanwhile, the Taiwanese people voted in January and returned for a third time the ruling party that strongly rejects Chinese territorial claims. Tensions are high, with the Chinese military once again harassing Taiwanese defenses. For Beijing, the “right moment” could fall this year should conflict break out on the Korean peninsula, which would tie the U.S. down because of the Mutual Defense Treaty.

The uncertainty here is not that there are global tensions, but how the U.S. will respond as they develop and how U.S. businesses can navigate external shocks. Will the U.S. be drawn into a new war in the Middle East? Can the U.S. manage multiple conflicts, already deeply involved in supporting Ukraine? Is the U.S. economy resilient enough to withstand trade disruptions? How can businesses strengthen their own resiliency?

Economic Protectionism Increasing Costs and Risks

Geopolitical tensions, the global pandemic and the unequal benefits of globalization are impacting economic policies of the U.S. and the political discourse around the merits of unrestrained free trade. Protectionist economic policies are creeping in, under the nomenclature of “secure supply chains,” “friend-shoring” and “home-shoring.” The U.S. has imposed tariffs on countries (even allies) accused of unfair trade practices and has foreclosed access to certain technologies by unfriendly countries, namely China.

While the response to some of these trade restrictions are new trade agreements with “friends” to regulate access under preferred terms, in essence creating multiple “friends” trade blocs for specific sectors, other responses are retaliatory, including counter tariffs and export restrictions or outright bans. In 2024, the U.S. economy will see the impact of these trade fragmentation policies in acute ways, with upside risks of new business opportunities and downside risks of supply chain disruptions, critical resource competition, increased input costs, compliance risks and increased reputational risks.

Trade with China, which remains significant and important to the stability of the U.S. economy, will pose new risks in 2024. While Washington and Beijing have agreed to some political and security guardrails to manage the relationship, economic competition is unrestrained and stability in the bilateral relations is not guaranteed. The December 2023 bipartisan report by the House Select Committee on the Strategic Competition Between the United States and the Chinese Communist Party, with its 150 recommendations on fundamentally resetting economic and technological competition with China, if even partially adopted, risks reigniting the trade war.

2024 is a presidential election year for the U.S. A change of control of the executive branch could result in many economic and regulatory policy reversals. The definition of “friend” could shift or narrow. Restrictions on trade with China could accelerate.

Impacts of Climate Change and Sustainability Policies

2023 was the hottest year on record, and El Niño conditions are expected to further boost the warming trend. Many regions experienced record-breaking wildfire activity in 2023, including Canada where 18 million hectares of land burned. Extreme storms caused life-threatening flooding in Europe, Asia and the Americas. 2024 is expected to bring even more climate hazards. The impacts will be physical and financial, including growing insurance losses and adverse impacts on operations and value chain. Analysts expect that in 2024, the economic and financial costs of adverse health impacts from climate change will increase, with risks related to the spread of infectious disease, insufficient access to clean water, and physical harm to the elderly and vulnerable. The direct economic effect will be on health systems, but also loss of productivity due to extreme weather incidents and effects of epidemics.

Energy transition to low-carbon emissions is underway in the U.S., but it is uneven and still uncertain. The financial market is investing in an impressive number of startups and large-scale projects revolving around cleantech. Still, there is hesitancy on the opportunity and risks of sustainability. Thus far, progress towards sustainability goals has been private sector-led and government-enabled. There is a risk that government incentive programs encouraging the transition to low-carbon energy could be reversed or curtailed under a new administration.

In 2024, some companies will face more climate disclosure compliance requirements. The Securities and Exchange Commission (SEC) is expected to release its final rule on climate change disclosures. The final action has been delayed several times because of pushback by public companies on some of the requirements, including Scope 3 greenhouse gas emission disclosures (those linked to supply chains and end users). California has not waited for the SEC’s final rule: In October 2023, Gov. Gavin Newsom signed into law legislation that will require large companies to disclose greenhouse gas emissions. The California climate laws go into effect in 2026, but companies will need to start much earlier to build the capabilities to plan, track and report their carbon footprint. For U.S. companies doing business in the European Union, they will need to comply with the EU Corporate Sustainability Reporting Directive, with the rules coming into force mid-2024.

Disruptive Technology

In 2023, generative AI was the talk of the town; in 2024, it will be the walk. Companies are popping up with new tools for every imaginable sector, to increase efficiency, task automation, customization, personalization and cost reduction. Business leaders are scrambling to integrate AI to gain a competitive edge, while navigating the everyday risks related to privacy, liability and security. While there are concerns that AI will displace humans, there is a growing consensus that while some jobs will disappear, people will focus on higher value work. That said, new rounds of labor disruptions linked to workforce transition are likely in 2024.

2024 will also bring AI-generated misinformation and disinformation. Bad actors will spread “synthetic” content, such as sophisticated voice cloning, doctored images and counterfeit websites, seeking to manipulate people, damage companies and economies, and foment dissent.

In 2024, around 2 billion people in more than 50 countries will vote in elections at risk of manipulation by misinformation and disinformation, which could destabilize the real and perceived legitimacy of newly elected governments, risking political unrest, violence, terrorism and erosion of democratic processes. Large democracies will hold elections in 2024, including the U.S., the EU, Mexico, South Korea, India, Pakistan, Indonesia and South Africa. Synthetic content can be very difficult to detect, while easy to produce with AI tools.

This is not a theoretical threat; synthetic content is already being disseminated in the U.S., targeting New Hampshire voters with robocalls that share fake recorded messages from President Biden encouraging people not to vote in the primary election. The U.S. is already polarized with citizens distrustful of the government and media, a ready vulnerability. Businesses are not immune. Notably, CEOs have stood apart, with higher ratings for trustworthiness and risk being called upon to vouch for “truth” (and becoming collateral damage in the fray).

AI-powered malware will make 2023 cyber risks look like child’s play. Attackers can use AI algorithms to find and exploit software vulnerabilities, making attacks precise and effective. AI can help hackers quickly identify security measures and evade them. AI-created phishing attacks will be more sophisticated and difficult to detect because the algorithms can assess larger amounts of piecemeal information and craft messages that mimic communication styles.

The role of states backing cyber armies to spread disinformation or steal information is growing and is part and parcel of the erosion of the existing international order. States face little deterrence from digital cross-border attacks because there are yet to be established mechanisms to impose real costs.

Can Artificial Intelligence Assist with Cybersecurity Management?

AI has great capability to both harm and to protect in a cybersecurity context. As with the development of any new technology, the benefits provided through correct and successful use of AI are inevitably coupled with the need to safeguard information and to prevent misuse.

Using AI for good – key themes from the European Union Agency for Cybersecurity (ENISA) guidance

ENISA published a set of reports earlier last year focused on AI and the mitigation of cybersecurity risks. Here we consider the main themes raised and provide our thoughts on how AI can be used advantageously*.

Using AI to bolster cybersecurity

In Womble Bond Dickinson’s 2023 global data privacy law survey, half of respondents told us they were already using AI for everyday business activities ranging from data analytics to customer service assistance and product recommendations and more. However, alongside day-to-day tasks, AI’s ‘ability to detect and respond to cyber threats and the need to secure AI-based application’ makes it a powerful tool to defend against cyber-attacks when utilized correctly. In one report, ENISA recommended a multi-layered framework which guides readers on the operational processes to be followed by coupling existing knowledge with best practices to identify missing elements. The step-by-step approach for good practice looks to ensure the trustworthiness of cybersecurity systems.

Utilizing machine-learning algorithms, AI is able to detect both known and unknown threats in real time, continuously learning and scanning for potential threats. Cybersecurity software which does not utilize AI can only detect known malicious codes, making it insufficient against more sophisticated threats. By analyzing the behavior of malware, AI can pin-point specific anomalies that standard cybersecurity programs may overlook. Deep-learning based program NeuFuzz is considered a highly favorable platform for vulnerability searches in comparison to standard machine learning AI, demonstrating the rapidly evolving nature of AI itself and the products offered.

A key recommendation is that AI systems should be used as an additional element to existing ICT, security systems and practices. Businesses must be aware of the continuous responsibility to have effective risk management in place with AI assisting alongside for further mitigation. The reports do not set new standards or legislative perimeters but instead emphasize the need for targeted guidelines, best practices and foundations which help cybersecurity and in turn, the trustworthiness of AI as a tool.

Amongst other factors, cybersecurity management should consider accountability, accuracy, privacy, resiliency, safety and transparency. It is not enough to rely on traditional cybersecurity software especially where AI can be readily implemented for prevention, detection and mitigation of threats such as spam, intrusion and malware detection. Traditional models do exist, but as ENISA highlights they are usually designed to target or’address specific types of attack’ which, ‘makes it increasingly difficult for users to determine which are most appropriate for them to adopt/implement.’ The report highlights that businesses need to have a pre-existing foundation of cybersecurity processes which AI can work alongside to reveal additional vulnerabilities. A collaborative network of traditional methods and new AI based recommendations allow businesses to be best prepared against the ever-developing nature of malware and technology based threats.

In the US in October 2023, the Biden administration issued an executive order with significant data security implications. Amongst other things, the executive order requires that developers of the most powerful AI systems share safety test results with the US government, that the government will prepare guidance for content authentication and watermarking to clearly label AI-generated content and that the administration will establish an advanced cybersecurity program to develop AI tools and fix vulnerabilities in critical AI models. This order is the latest in a series of AI regulations designed to make models developed in the US more trustworthy and secure.

Implementing security by design

A security by design approach centers efforts around security protocols from the basic building blocks of IT infrastructure. Privacy-enhancing technologies, including AI, assist security by design structures and effectively allow businesses to integrate necessary safeguards for the protection of data and processing activity, but should not be considered as a ‘silver bullet’ to meet all requirements under data protection compliance.

This will be most effective for start-ups and businesses in the initial stages of developing or implementing their cybersecurity procedures, as conceiving a project built around security by design will take less effort than adding security to an existing one. However, we are seeing rapid growth in the number of businesses using AI. More than one in five of our survey respondents (22%), for instance, started to use AI in the past year alone.

However, existing structures should not be overlooked and the addition of AI into current cybersecurity system should improve functionality, processing and performance. This is evidenced by AI’s capability to analyze huge amounts of data at speed to provide a clear, granular assessment of key performance metrics. This high-level, high-speed analysis allows businesses to offer tailored products and improved accessibility, resulting in a smoother retail experience for consumers.

Risks

Despite the benefits, AI is by no-means a perfect solution. Machine-learning AI will act on what it has been told under its programming, leaving the potential for its results to reflect an unconscious bias in its interpretation of data. It is also important that businesses comply with regulations (where applicable) such as the EU GDPR, Data Protection Act 2018, the anticipated Artificial Intelligence Act and general consumer duty principles.

Cost benefits

Alongside reducing the cost of reputational damage from cybersecurity incidents, it is estimated that UK businesses who use some form of AI in their cybersecurity management reduced costs related to data breaches by £1.6m on average. Using AI or automated responses within cybersecurity systems was also found to have shortened the average ‘breach lifecycle’ by 108 days, saving time, cost and significant business resource. Further development of penetration testing tools which specifically focus on AI is required to explore vulnerabilities and assess behaviors, which is particularly important where personal data is involved as a company’s integrity and confidentiality is at risk.

Moving forward

AI can be used to our advantage but it should not been seen to entirely replace existing or traditional models to manage cybersecurity. While AI is an excellent long-term assistant to save users time and money, it cannot be relied upon alone to make decisions directly. In this transitional period from more traditional systems, it is important to have a secure IT foundation. As WBD suggests in our 2023 report, having established governance frameworks and controls for the use of AI tools is critical for data protection compliance and an effective cybersecurity framework.

Despite suggestions that AI’s reputation is degrading, it is a powerful and evolving tool which could not only improve your business’ approach to cybersecurity and privacy but with an analysis of data, could help to consider behaviors and predict trends. The use of AI should be exercised with caution, but if done correctly could have immeasurable benefits.

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* While a portion of ENISA’s commentary is focused around the medical and energy sectors, the principles are relevant to all sectors.

Exploring the Future of Information Governance: Key Predictions for 2024

Information governance has evolved rapidly, with technology driving the pace of change. Looking ahead to 2024, we anticipate technology playing an even larger role in data management and protection. In this blog post, we’ll delve into the key predictions for information governance in 2024 and how they’ll impact businesses of all sizes.

Embracing AI and Automation: Artificial intelligence and automation are revolutionizing industries, bringing about significant changes in information governance practices. Over the next few years, it is anticipated that an increasing number of companies will harness the power of AI and automation to drive efficient data analysis, classification, and management. This transformative approach will not only enhance risk identification and compliance but also streamline workflows and alleviate administrative burdens, leading to improved overall operational efficiency and effectiveness. As organizations adapt and embrace these technological advancements, they will be better equipped to navigate the evolving landscape of data governance and stay ahead in an increasingly competitive business environment.
Prioritizing Data Privacy and Security: In recent years, data breaches and cyber-attacks have significantly increased concerns regarding the usage and protection of personal data. As we look ahead to 2024, the importance of data privacy and security will be paramount. This heightened emphasis is driven by regulatory measures such as the California Consumer Privacy Act (CCPA) and the European Union’s General Data Protection Regulation (GDPR). These regulations necessitate that businesses take proactive measures to protect sensitive data and provide transparency in their data practices. By doing so, businesses can instill trust in their customers and ensure the responsible handling of personal information.
Fostering Collaboration Across Departments: In today’s rapidly evolving digital landscape, information governance has become a collective responsibility. Looking ahead to 2024, we can anticipate a significant shift towards closer collaboration between the legal, compliance, risk management, and IT departments. This collaborative effort aims to ensure comprehensive data management and robust protection practices across the entire organization. By adopting a holistic approach and providing cross-functional training, companies can empower their workforce to navigate the complexities of information governance with confidence, enabling them to make informed decisions and mitigate potential risks effectively. Embracing this collaborative mindset will be crucial for organizations to adapt and thrive in an increasingly data-driven world.
Exploring Blockchain Technology: Blockchain technology, with its decentralized and immutable nature, has the tremendous potential to revolutionize information governance across industries. By 2024, as businesses continue to recognize the benefits, we can expect a significant increase in the adoption of blockchain for secure and transparent transaction ledgers. This transformative technology not only enhances data integrity but also mitigates the risks of tampering, ensuring trust and accountability in the digital age. With its ability to provide a robust and reliable framework for data management, blockchain is poised to reshape the way we handle and secure information, paving the way for a more efficient and trustworthy future.
Prioritizing Data Ethics: As data-driven decision-making becomes increasingly crucial in the business landscape, the importance of ethical data usage cannot be overstated. In the year 2024, businesses will place even greater emphasis on data ethics, recognizing the need to establish clear guidelines and protocols to navigate potential ethical dilemmas that may arise. To ensure responsible and ethical data practices, organizations will invest in enhancing data literacy among their workforce, prioritizing education and training initiatives. Additionally, there will be a growing focus on transparency in data collection and usage, with businesses striving to build trust and maintain the privacy of individuals while harnessing the power of data for informed decision-making.

The future of information governance will be shaped by technology, regulations, and ethical considerations. Businesses that adapt to these changes will thrive in a data-driven world. By investing in AI and automation, prioritizing data privacy and security, fostering collaboration, exploring blockchain technology, and upholding data ethics, companies can prepare for the challenges and opportunities of 2024 and beyond.

Jim Merrifield, Robinson+Cole’s Director of Information Governance & Business Intake, contributed to this report.

5 Trends to Watch: 2024 Emerging Technology

Increased Adoption of Generative AI and Push to Minimize Algorithmic Biases – Generative AI took center stage in 2023 and popularity of this technology will continue to grow. The importance behind the art of crafting nuanced and effective prompts will heighten, and there will be greater adoption across a wider variety of industries. There should be advancements in algorithms, increasing accessibility through more user-friendly platforms. These can lead to increased focus on minimizing algorithmic biases and the establishment of guardrails governing AI policies. Of course, a keen awareness of the ethical considerations and policy frameworks will help guide generative AI’s responsible use.
Convergence of AR/VR and AI May Result in “AR/VR on steroids” The fusion of Augmented Reality (AR) and Virtual Reality (VR) technologies with AI unlocks a new era of customization and promises enhanced immersive experiences, blurring the lines between the digital and physical worlds. We expect to see further refining and personalizing of AR/VR to redefine gaming, education, and healthcare, along with various industrial applications.
EV/Battery Companies Charge into Greener Future. With new technologies and chemistries, advancements in battery efficiency, energy density, and sustainability can move the adoption of electric vehicles (EVs) to new heights. Decreasing prices for battery metals canbatter help make EVs more competitive with traditional vehicles. AI may providenew opportunities in optimizing EV performance and help solve challenges in battery development, reliability, and safety.
“Rosie the Robot” is Closer than You Think. With advancements in machine learning algorithms, sensor technologies, and integration of AI, the intelligence and adaptability of robotics should continue to grow. Large language models (LLMs) will likely encourage effective human-robot collaboration, and even non-technical users will find it easy to employ robotics to accomplish a task. Robotics is developing into a field where machines can learn, make decisions, and work in unison with people. It is no longer limited to monotonous activities and repetitive tasks.
Unified Defense in Battle Against Cyber-Attacks. Digital threats are expected to only increase in 2024, including more sophisticated AI-powered attacks. As the international battle against hackers wages on, threat detection, response, and mitigation will play a crucial role in staying ahead of rapidly evolving cyber-attacks. As risks to national security and economic growth, there should be increased collaboration between industries and governments to establish standardized cybersecurity frameworks to protect data and privacy.

Algorithmic Pricing Agents and Price-Fixing Facilitators: Antitrust Law’s Latest Conundrum

Are machines doing the collaborating that competitors may not?

It is an application of artificial intelligence (“AI”) that many businesses, agencies, legislators, lawyers, and antitrust law enforcers around the world are only beginning to confront. It is also among the top concerns of in-house counsel across industries. Competitors are increasingly setting prices through the use of communal, AI-enhanced algorithms that analyze data that are private, public, or a mix of both.

Allegations in private and public litigation describe “algorithmic price fixing” in which the antitrust violation occurs when competitors feed and access the same database platform and use the same analytical tools. Then, as some allege, the violations continue when competitors agree to the prices produced by the algorithms. Right now, renters and prosecutors are teeing off on the poster child for algorithmic pricing, RealPage Inc., and the many landlords and property managers who use it.

PRIVATE AND PUBLIC LITIGATION

A Nov. 1, 2023 complaint filed by the Washington, DC, Attorney General’s office described RealPage’s offerings this way: “[A] variety of technology-based services to real estate owners and property managers including revenue management products that employ statistical models that use data—including non-public, competitively sensitive data—to estimate supply and demand for multifamily housing that is specific to particular geographic areas and unit types, and then generate a ‘price’ to charge for renting those units that maximizes the landlord’s revenue.”

The complaint alleges that more than 30% of apartments in multifamily buildings and 60% of units in large multifamily buildings nationwide are priced using the RealPage software. In the Washington-Arlington-Alexandria Metropolitan Area that number leaps to more than 90% of units in large buildings. The complaint alleges that landlords have agreed to set their rates using RealPage.

Private actions against RealPage have also been filed in federal courts across the country and have been centralized in multi-district litigation in the Middle District of Tennessee (In re: RealPage, Inc., Rental Software Antitrust Litigation [NO. II], Case No. 3:23-md-3071, MDL No. 3071). The Antitrust Division of the Department of Justice filed a Statement of Interest and a Memorandum in Support in the case urging the court to deny the defendants’ motion to dismiss.

Even before the MDL, RealPage had attracted the Antitrust Division’s attention when the company acquired its largest competitor, Lease Rent Options for $300 million, Axiometrics for $75 million, and On-Site Manager, Inc. for $250 million.

The Antitrust Division has been pursuing the use of algorithms in other industries, including airlines and online retailers. The DOJ and FTC are both studying the issue and reaching out to experts to learn more.

JOURNALISTS AND SENATORS

Additionally, three senators urged DOJ to investigate RealPage after reporters at ProPublica wrote an investigative report in October 2022. The journalists claim that RealPage’s price-setting software “uses nearby competitors’ nonpublic rent data to feed an algorithm that suggests what landlords should charge for available apartments each day.” ProPublica speculated that the algorithm is enabling landlords to coordinate prices and in the process push rents above competitive levels in violation of the antitrust laws.

Senators Amy Klobuchar (D-MN), Dick Durban (D-IL) and Cory Booker (D-NJ) wrote to the DOJ concerned that the RealPage enables “a cartel to artificially inflate rental rates in multifamily residential buildings.”

Sen. Sherrod Brown (D-OH) also wrote to the Federal Trade Commission with concerns “about collusion in the rental market,” urging the FTC to “review whether rent setting algorithms that analyze rent prices through the use of competitors’ private data … violate antitrust laws.” The Ohio senator specifically mentioned RealPage’s YieldStar and AI Revenue Management programs.

THE EUROPEANS

The European Commission has enacted the Artificial Intelligence Act, which includes provisions on algorithmic pricing, requiring algorithmic pricing systems be transparent, explainable, and non-discriminatory with regard to consumers. Companies that use algorithmic pricing systems will be required to implement compliance procedures, including audits, data governance, and human oversight.

THE LEGAL CONUNDRUM

An essential element of any claimed case of price-fixing under the U.S. antitrust laws is the element of agreement: a plaintiff alleging price-fixing must prove the existence of an agreement between two or more competitors who should be setting their prices independently but aren’t. Consumer harm from collusion occurs when competitors set prices to achieve their maximum joint profit instead of setting prices to maximize individual profits. To condemn algorithmic pricing as collusion, therefore, requires proof of agreement.

It may be difficult for the RealPage plaintiffs to prove that the RealPage’s users agreed among themselves to adhere to any particular price or pricing formula, but not impossible. End users are likely to argue that RealPage’s pricing recommendations are merely aggregate market signals that RealPage is collecting and disseminating. The use of the same information service, their argument will go, does not prove the existence of an agreement for purposes of Section 1 of the Sherman Act.

The parties and courts embroiled in the RealPage litigation are constrained to live under the law as it presently exists, so the solution proposed by Michal Gal, Professor and Director of the Forum on Law and Markets at the University of Haifa, is out of reach. In her 2018 paper, “Algorithms as Illegal Agreements,” Professor Gal confronts the agreement problem when algorithms set prices and concludes that it is time to “rethink our laws and focus on reducing harms to social welfare rather than on what constitutes an agreement.” Academics have been critical of the agreement element of Section 1 for years, but it is unlikely to change anytime soon, even with the added inconvenience it poses where competitors rely on a common vendor of machine-generated pricing recommendations.

Nonetheless, there is some evidence that autonomous machines, just like humans, can learn that collusion allows sellers to charge monopoly prices. In their December 2019 paper, “Artificial Intelligence, Algorithmic Pricing and Collusion,” Emilio Calvano, Giacomo Calzolari, Vincenzo Denicolo, and Sergio Pastorello at the Department of Economics at the University of Bologna showed with computer simulations that machines autonomously analyzing prices can develop collusive strategies “from scratch, engaging in active experimentation and adapting to changing environments.” The authors say indications from their models “suggest that algorithmic collusion is more than a remote theoretical possibility.” They find that “relatively simple [machine learning] pricing algorithms systematically learn to play collusive strategies.” The authors claim to be the first to “clearly document the emergence of collusive strategies among autonomous pricing agents.”

THE AGREEMENT ELEMENT IN THE MACHINE PRICING CASE

For three main reasons, the element of agreement need not be an obstacle to successfully prosecuting a price-fixing claim against competitors that use a common or similar vendor of algorithmic pricing data and software.

First, there is significant precedent for inferring the existence of an agreement among parties that knowingly participate in a collusive arrangement even if they do not directly interact, sometimes imprecisely referred to as a “rimless wheel hub-and-spoke” conspiracy. For example, in Toys “R” Us, Inc. v. F.T.C., 221 F.3d 928 (9^th Cir. 2000), the court inferred the necessary concerted action from a series of individual agreements between toy manufacturers and Toys “R” Us in which the manufacturers promised not to sell the toys sold to Toys “R” Us and other toy stores to big box stores in the same packaging. The FTC found that each of the manufacturers entered into the restraint on the condition that the others also did so. The court found that Toys “R” Us had engineered a horizontal boycott against a competitor in violation of Section 1, despite the absence of evidence of any “privity” between the boycotting manufacturers.

The Toys “R” Us case relied on the Supreme Court’s decision in Interstate Circuit v. United States, 306 U.S. 208 (1939), in which movie theater chains sent an identical letter to eight movie studios asking them to restrict secondary runs of certain films. The letter disclosed that each of the eight were receiving the same letter. The Court held that a direct agreement was not a prerequisite for an unlawful conspiracy. “It was enough that, knowing that concerted action was contemplated and invited, the distributors gave their adherence to the scheme and participated in it.”

The analogous issue in the algorithmic pricing scenario is whether the vendor’s end users that their competitors are also end users. If so, the inquiry can consider the agreement element satisfied if the algorithm does, in fact, jointly maximize the end users’ profits.

The second factor overcoming the agreement element is related to the first. Whether software that recommends prices has interacted with the prices set by competitors to achieve joint profit maximization—that is, whether the machines have learned to collude without human intervention—is an empirical question. The same techniques used to uncover machine-learned collusion by simulation can be used to determine the extent of interdependence in historical price setting. If statistical evidence of collusive pricing is available, it is enough that the end users knowingly accepted the offer to set its prices guided by the algorithm. The economics underlying the agreement element in the first place lies in prohibition of joint rather than individual profit maximization, so direct evidence that market participants are jointly profit maximizing should obviate the need for further evidence of agreement.

A third reason the agreement element need not stymie a Section 1 action against defendants engaged in algorithmic pricing is based on the Supreme Court’s decision in American Needle v. NFL, 560 U.S. 183 (2010). In that case the Court made clear that arrangements that remove independent centers of decision-making from the market run afoul of Section 1, if the net effect of the algorithm is to displace individual decision-making with decisions outsourced to a centralized pricing agent, the mechanism should be immaterial.

The rimless wheel of the so-called hub-and-spoke conspiracy is an inadequate analogy because the wheel in these cases does have a rim, i.e., a connection between the conspirators. In the scenarios above in which the courts have found Section 1 liability i) each of the participants knew that its rivals were also entering into the same or similar arrangements, ii) the participants devolved pricing authority away from themselves down to an algorithmic pricing agent, and iii) historical prices could be shown statistically to have exceeded the competitive level in a way consistent with collusive pricing. These elements connect the participants in the scheme, supplying the “rim” to the spokes of the wheel. If the plaintiffs in the RealPage litigation can establish these elements, they will have met their burden of establishing the requisite element of agreement in their Section 1 claim.

What Employers Need to Know about the White House’s Executive Order on AI

President Joe Biden recently issued an executive order devised to establish minimum risk practices for use of generative artificial intelligence (“AI”) with focus on rights and safety of people, with many consequences for employers. Businesses should be aware of these directives to agencies, especially as they may result in new regulations, agency guidance and enforcements that apply to their workers.

Executive Order Requirements Impacting Employers

Specifically, the executive order requires the Department of Justice and federal civil rights offices to coordinate on ‘best practices’ for investigating and prosecuting civil rights violations related to AI. The ‘best practices’ will address: job displacement; labor standards; workplace equity, health, and safety; and data collection. These principles and ‘best practices’ are focused on benefitting workers and “preventing employers from undercompensating workers, evaluating job applications unfairly, or impinging on workers’ ability to organize.”

The executive order also requested a report on AI’s potential labor-market impacts, and to study and identify options for strengthening federal support for workers facing labor disruptions, including from AI. Specifically, the president has directed the Chairman of the Council of Economic Advisers to “prepare and submit a report to the President on the labor-market effects of AI”. In addition, there is a requirement for the Secretary of Labor to submit “a report analyzing the abilities of agencies to support workers displaced by the adoption of AI and other technological advancements.” This report will include principles and best practices for employers that could be used to mitigate AI’s potential harms to employees’ well-being and maximize its potential benefits. Employers should expect more direction once this report is completed in April 2024.

Increasing International Employment?

Developing and using generative AI inherently requires skilled workers, which President Biden recognizes. One of the goals of his executive order is to “[u]se existing authorities to expand the ability of highly skilled immigrants and nonimmigrants with expertise in critical areas to study, stay, and work in the United States by modernizing and streamlining visa criteria, interviews, and reviews.” While work visas have been historically difficult for employers to navigate, this executive order may make it easier for US employers to access skilled workers from overseas.

Looking Ahead

In light of the focus of this executive order, employers using AI for recruiting or decisions about applicants (and even current employees) must be aware of the consequences of not putting a human check on the potential bias. Working closely with employment lawyers at Sheppard Mullin and having a multiple checks and balances on recruiting practices are essential when using generative AI.

While this executive order is quite limited in scope, it is only a first step. As these actions are implemented in the coming months, be sure to check back for updates.

The FCC Approves an NOI to Dive Deeper into AI and its Effects on Robocalls and Robotexts

AI is on the tip of everyone’s tongue it seems these days. The Dame brought you a recap of President Biden’s orders addressing AI at the beginning of the month. This morning at the FCC’s open meeting they were presented with a request for a Notice of Inquiry (NOI) to gather additional information about the benefits and harms of artificial intelligence and its use alongside “robocall and robotext”. The following five areas of interest are as follows:

First, the NOI seeks, on whether and if so how the commission should define AI technologies for purposes of the inquiry this includes particular uses of AI technologies that are relevant to the commission’s statutory response abilities under the TCPA, which protects consumers from nonemergency calls and texts using an autodialer or containing an artificial or prerecorded voice.
Second, the NOI seeks comment on how technologies may impact consumers who receive robocalls and robotexts including any potential benefits and risks that the emerging technologies may create. Specifically, the NOI seeks information on how these technologies may alter the functioning of the existing regulatory framework so that the commission may formulate policies that benefit consumers by ensuring they continue to receive privacy protections under the TCPA.
Third, the NOI seeks comment on whether it is necessary or possible to determine at this point whether future types of AI technologies may fall within the TCPA’s existing prohibitions on autodial calls or texts and artificial or prerecorded voice messages.
Fourth, NOI seeks comment on whether the commission should consider ways to verify the authenticity and legitimately generate AI voice or text content from trusted sources such as through the use of watermarks, certificates, labels, signatures, or other forms of labels when callers rely on AI technology to generate content. This may include, for example, emulating a human voice on a robocall or creating content in a text message.
Lastly, seeks comment on what next steps the commission should consider to further the inquiry.

While all the commissioners voted to approve the NOI they did share a few insightful comments. Commissioner Carr stated “ If AI can combat illegal robocalls, I’m all for it” but he also expressed that he does “…worry that the path we are heading down is going to be overly prescriptive” and suggests “…Let’s put some common-sense guardrails in place, but let’s not be so prescriptive and so heavy-handed on the front end that we end up benefiting large incumbents in the space because they can deal with the regulatory frameworks and stifling the smaller innovation to come.”

Commissioner Starks shared “I, for one, believe this intersectionality is clinical because the future of AI remains uncertain, one thing is clear — it has the potential to impact if not transform every aspect of American life, and because of that potential, each part of our government bears responsibility to better understand the risks, opportunities within its mandate, while being mindful of the limits of its expertise, experience, and authority. In this era of rapid technological change, we must collaborate, lean into our expertise across agencies to best serve our citizens and consumers.” Commissioner Starks seemed to be particularly focused on AI’s ability to facilitate bad actors in schemes like voice cloning and how the FCC can implement safeguards against this type of behavior.

“AI technologies can bring new challenges and opportunities. responsible and ethical implementation of AI technologies is crucial to strike a balance, ensuring that the benefits of AI are harnessed to protect consumers from harm rather than amplifying the risks in increasing the digital landscape” Commissioner Gomez shared.

Finally, the topic around the AI NOI wrapped up with Chairwoman Rosenworcel commenting “… I think we make a mistake if we only focus on the potential for harm. We needed to equally focus on how artificial intelligence can radically improve the tools we have today to block unwanted robocalls and robotexts. We are talking about technology that can see patterns in our network traffic, unlike anything we have today. They can lead to the development of analytic tools that are exponentially better at finding fraud before it reaches us at home. Used at scale, we cannot only stop this junk, we can use it to increase trust in our networks. We are asking how artificial intelligence is being used right now to recognize patterns in network traffic and how it can be used in the future. We know the risks this technology involves but we also want to harness the benefits.”

Under the GDPR, Are Companies that Utilize Personal Information to Train Artificial Intelligence (AI) Controllers or Processors?

The EU’s General Data Protection Regulation (GDPR) applies to two types of entities – “controllers” and “processors.”

A “controller” refers to an entity that “determines the purposes and means” of how personal information will be processed.[1] Determining the “means” of processing refers to deciding “how” information will be processed.[2] That does not necessitate, however, that a controller makes every decision with respect to information processing. The European Data Protection Board (EDPB) distinguishes between “essential means” and “non-essential means.[3] “Essential means” refers to those processing decisions that are closely linked to the purpose and the scope of processing and, therefore, are considered “traditionally and inherently reserved to the controller.”[4] “Non-essential means” refers to more practical aspects of implementing a processing activity that may be left to third parties – such as processors.[5]

A “processor” refers to a company (or a person such as an independent contractor) that “processes personal data on behalf of [a] controller.”[6]

Data typically is needed to train and fine-tune modern artificial intelligence models. They use data – including personal information – in order to recognize patterns and predict results.

Whether an organization that utilizes personal information to train an artificial intelligence engine is a controller or a processor depends on the degree to which the organization determines the purpose for which the data will be used and the essential means of processing. The following chart discusses these variables in the context of training AI:

The following chart discusses these variables in the context of training AI:

Function	Activities Indicative of a Controller	Activities Indicative of a Processor
Purpose of processing
Why the AI is being trained.	If an organization makes its own decision to utilize personal information to train an AI, then the organization will likely be considered a “controller.”	If an organization is using personal information provided by a third party to train an AI, and is doing so at the direction of the third party, then the organization may be considered a processor.
Essential means
Data types used in training.	If an organization selects which data fields will be used to train an AI, the organization will likely be considered a “controller.”	If an organization is instructed by a third party to utilize particular data types to train an AI, the organization may be a processor.
Duration personal information is held within the training engine	If an organization determines how long the AI can retain training data, it will likely be considered a “controller.”	If an organization is instructed by a third party to use data to train an AI, and does not control how long the AI may access the training data, the organization may be a processor.
Recipients of the personal information	If an organization determines which third parties may access the training data that is provided to the AI, that organization will likely be considered a “controller.”	If an organization is instructed by a third party to use data to train an AI, but does not control who will be able to access the AI (and the training data to which the AI has access), the organization may be a processor.
Individuals whose information is included	If an organization is selecting whose personal information will be used as part of training an AI, the organization will likely be considered a “controller.”	If an organization is being instructed by a third party to utilize particular individuals’ data to train an AI, the organization may be a processor.

[1] GDPR, Article 4(7).

[2] EDPB, Guidelines 07/2020 on the concepts of controller and processor in the GDPR, Version 1, adopted 2 Sept. 2020, at ¶ 33.

[3] EDPB, Guidelines 07/2020 on the concepts of controller and processor in the GDPR, Version 1, adopted 2 Sept. 2020, at ¶ 38.

[4] EDPB, Guidelines 07/2020 on the concepts of controller and processor in the GDPR, Version 1, adopted 2 Sept. 2020, at ¶ 38.

[5] EDPB, Guidelines 07/2020 on the concepts of controller and processor in the GDPR, Version 1, adopted 2 Sept. 2020, at ¶ 38.

[6] GDPR, Article 4(8).

For more Privacy Legal News, click here to visit the National Law Review.

Automating Entertainment: Writers Demand that Studios Not Use AI

When the Writers Guild of America (WGA) came with their list of demands in the strike that has already grinded production on many shows to a halt, chief among them was that the studios agree not to use artificial intelligence to write scripts. Specifically, the Guild had two asks: First, they said that “literary material,” including screenplays and outlines, must be generated by a person and not an AI; Second, they insisted that “source material” not be AI-generated.

The Alliance of Motion Picture and Television Producers (AMPTP), which represents the studios, rejected this proposal. They countered that they would be open to holding annual meetings to discuss advancements in technology. Alarm bells sounded as the WGA saw an existential threat to their survival and that Hollywood was already planning for it.

Writers are often paid at a far lower rate to adapt “source material” such as a comic book or a novel into a screenplay than they are paid to generate original literary material. By using AI tools to generate an outline or first draft of an original story and then enlisting a human to “adapt” it into screenplay, production studios potentially stand to save significantly.

Many industries have embraced the workflow of an AI-generated “first draft” that the human then punches up. And the WGA has said that its writers’ using AI as a tool is acceptable: There would essentially be a robot in the writers’ room with writers supplementing their craft with AI-generated copy, but without AI wholly usurping their jobs.

Everyone appears in agreement that AI could never write the next season of White Lotus or Succession, but lower brow shows could easily be AI aped. Law and Order, for instance, is an often cited example. Not just because it’s formulaic but because AIs are trained on massive data sets of copyrighted content and there are 20 seasons of Law and Order for the AI to ingest. And as AI technology gets more advanced who knows what it could do? Chat GPT was initially released last November and as of writing we’re on GPT-4, a far more powerful version of a platform that is advancing exponentially.

The studios’ push for the expanded use of AI is not without its own risks. The Copyright Office has equivocated somewhat in its determination that AI-generated art is not protectable. In a recent Statement of Policy, the Office said that copyright will only protect aspects of the work that were judged to have been made by the authoring human, resulting in partial protections of AI-generated works. So, the better the AI gets—the more it contributes to cutting out the human writer—the weaker the copyright protection for the studios/networks.

Whether or not AI works infringe the copyrights on the original works is an issue that is currently being litigated in a pair of lawsuits against Stability AI, the startup that created Stable Diffusion (an AI tool with the impressive ability to turn text into images in what some have dubbed the most massive art heist in history). Some have questioned whether the humans who wrote the original episodes would get compensated, and the answer is maybe not. In most cases the scripts were likely works for hire, owned by the studios.

If the studios own the underlying scripts, what happens to the original content if the studios take copyrighted content and put it through a machine that turns out uncopyrightable content? Can you DMCA or sue someone who copies that? As of this writing, there are no clear answers to these questions.

There are legal questions and deeper philosophical questions about making art. As the AI improves and humans become more cyborgian, does the art become indistinguishable? Prolific users of Twitter say they think their thoughts in 280 characters. Perhaps our readers can relate to thinking of their time in 6 minute increments, or .1’s of an hour. Further, perhaps our readers can relate to their industry being threatened by automation. According to a recent report from Goldman Sachs, generative artificial intelligence is putting 44% of legal jobs at risk.

For more Employment Legal News, click here to visit the National Law Review.

To AI or Not to AI: U.S. Copyright Office Clarifies Options

The U.S. Copyright Office has weighed in with formal guidance on the copyrightability of works whose generation included the use of artificial intelligence (AI) tools. The good news for technology-oriented human creative types: using AI doesn’t automatically disqualify your work from copyright protection. The bad news for independent-minded AI’s: you still don’t qualify for copyright protection in the United States.

On March 16, 2023, the Copyright Office issued a statement of policy (“Policy”) to clarify its practices for examining and registering works that contain material generated by the use of AI and how copyright law’s human authorship requirements will be applied when AI was used. This Policy is not itself legally binding or a guarantee of a particular outcome, but many copyright applicants may breathe a sigh of relief that the Copyright Office has formally embraced AI-assisted human creativity.

The Policy is just the latest step in an ongoing debate over the copyrightability of machine-assisted products of human creativity. Nearly 150 years ago, the Supreme Court ruled at photographs are copyrightable. See Burrow-Giles Lithographic Company v. Sarony, 111 U.S. 53 (1884). The case involved a photographer’s claim against a lithographer for 85,000 unauthorized copies of a photograph of Oscar Wilde. The photo, Sarony’s “Oscar Wilde No. 18,” is shown below:

The argument against copyright protection was that a photograph is “a reproduction, on paper, of the exact features of some natural object or of some person” and is therefore not a product of human creativity. Id. at 56. The Supreme Court disagreed, ruling that there was sufficient human creativity involved in making the photo, including posing the subject, evoking the desired expression, arranging the clothing and setting, and managing the lighting.

In the mid-1960’s, the Copyright Office rejected a musical composition, Push Button Bertha, that was created by a computer, reasoning that it lacked the “traditional elements of authorship” as they were not created by a human.

In 2018, the U.S. Court of Appeals for the Ninth Circuit ruled that Naruto, a crested macaque (represented by a group of friendly humans), lacked standing under the Copyright Act to hold a copyright in the “monkey selfie” case. See Naruto v. Slater, 888 F.3d 418 (9th Cir. 2018). The “monkey selfie” is below:

In February 2022, the Copyright Office rejected a registration (filed by interested humans) for a visual image titled “A Recent Entrance to Paradise,” generated by DABUS, the AI whose claimed fractal-based inventions are the subject of patent applications around the world. DABUS’ image is below:

Litigation over this rejected application remains pending.

And last month, the Copyright Office ruled that a graphic novel consisting of human-authored text and images generated using the AI tool Midjourney could, as a whole, be copyrighted, but that the images, standing alone, could not. See U.S. Copyright Office, Cancellation Decision re: Zarya of the Dawn (VAu001480196) at 2 (Feb. 21, 2023).

The Copyright Office’s issuing the Policy was necessitated by the rapid and remarkable improvements in generative AI tools over even the past several months. In December 2022, generative AI tool Dall-E generated the following images in response to nothing more than the prompt, “portrait of a musician with a hat in the style of Rembrandt”:

If these were human-generated paintings, or even photographs, there is no doubt that they would be copyrightable. But given that all four images were generated in mere seconds, with a single, general prompt from a human user, do they meet the Copyright Office’s criteria for copyrightability? The answer, now, is a clear “no” under the Policy.

However, the Policy opens the door to registering AI-assisted human creativity. The toggle points will be:

“…whether the ‘work’ is basically one of human authorship, with the computer [or other device] merely being an assisting instrument, or whether the traditional elements of authorship in the work (literary, artistic, or musical expression or elements of selection, arrangement, etc.) were actually conceived and executed not by man but by a machine.”

In the case of works containing AI-generated material, the Office will consider whether the AI contributions are the result of “mechanical reproduction” or instead of an author’s “own original mental conception, to which [the author] gave visible form.”

The answer will depend on the circumstances, particularly how the AI tool operates and how it was used to create the final work. This will necessarily be a case-by-case inquiry.”

See Policy (citations omitted).

Machine-produced authorship alone will continue not to be registerable in the United States, but human selection and arrangement of AI-produced content could lead to a different result according to the Policy. The Policy provides select examples to help guide registrants, who are encouraged to study them carefully. The Policy, combined with near future determinations by the Copyright Office, will be critical to watch in terms of increasing likelihood a registration application will be granted as the Copyright Office continues to assess the impacts of new technology on the creative process. AI tools should not all be viewed as the “same” or fungible. The type of AI and how it is used will be specifically considered by the Copyright Office.

In the short term, the Policy provides some practical guidance to applicants on how to describe the role of AI in a new copyright application, as well as how to amend a prior application in that regard if needed. While some may view the Policy as “new” ground for the Copyright Office, it is consistent with the Copyright Office’s long-standing efforts to protect the fruits of human creativity even if the backdrop (AI technologies) may be “new.”

As a closing note, it bears observing that copyright law in the United Kingdom does permit limited copyright protection for computer-generated works – and has done so since 1988. Even under the U.K. law, substantial questions remain; the author of a computer-generated work is considered to be “the person by whom the arrangements necessary for the creation of the work are undertaken.” See Copyright, Designs and Patents Act (1988) §§ 9(3), 12(7) and 178. In the case of images generated by a consumer’s interaction with a generative AI tool, would that be the consumer or the generative AI provider?

Article By Nadia G. Aram and Dr. Christian E. Mammen of Womble Bond Dickinson (US) LLP

For more intellectual property legal news, click here to visit the National Law Review.

Tag: Artificial Intelligence

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