Tag: algorithm

It Takes More than an Algorithm to Prove an Agreement: An Analysis of Gibson v. Cendyn Group

On May 8, 2024, Chief Judge Miranda Du of the U.S. District Court for the District of Nevada granted defendants’ motion to dismiss with prejudice the complaint in Gibson v. Cendyn Group, LLC, Docket No. 2:23-cv-00140-MMD-DJA, an antitrust case alleging that hotel operators on the Las Vegas Strip used algorithms to inflate room prices in violation of Section One of the Sherman Act. The court’s reasoning provides litigants on both sides with a framework for future cases.

Plaintiffs claimed that Caesars Entertainment, Inc., Treasure Island, LLC, and Wynn Resorts Holdings, LLC (hereinafter, the “Hotel Operators”) charged supercompetitive prices for rooms through GuestRev (individual rooms) and GroupRev (rooms for groups), which are shared-revenue management systems licensed by the Cendyn Group. Cendyn allegedly spearheaded a hub-and-spoke conspiracy[1] through an algorithm that used price and occupancy data to recommend room rates. The algorithm’s “optimal” rate was visible to individual hotel operators, who were discouraged by system prompts from overriding the recommendation. To establish anticompetitive effects in the relevant market, the plaintiffs relied on third-party economic analyses of revenue and price trends as well as circumstantial evidence known as “plus factors”—e.g., the motive and opportunity to conspire, market structure, the interchangeability of hotel rooms, and inelastic demand.

Before the court entered judgment in favor of defendants, Judge Du closely scrutinized plaintiffs’ claims. In an October 23, 2023 order dismissing plaintiff’s original complaint with leave to amend, the court asked plaintiffs to address: (i) when the conspiracy began and who participated; (ii) whether the Hotel Operators colluded to adopt a shared set of pricing algorithms; (iii) whether the Hotel Operators must accept the price recommendations; and (iv) whether the algorithm facilitated the exchange of non-public information.[2]

In its 2024 decision, the court ruled that plaintiffs’ amended complaint failed to meet these threshold requirements. First, the court disagreed with plaintiffs’ contention that the initial timing of the conspiracy was irrelevant because the Hotel Operators renewed their licensing agreements every year. Because defendants started using Cendyn’s technology at various points in time over a 10-year period, there was “no existing agreement to fix prices that a late-arriving spoke could join” and “a tacit agreement among [the Hotel Operators] was implausible.”[3]

Nor did plaintiffs allege that the Hotel Operators “agreed to be bound by [Cendyn’s] recommendations, much less that they all agreed to charge the same prices.”[4] To the contrary, plaintiffs maintained that Cendyn had difficulty getting customers to accept the recommendations. Even drawing all inferences in plaintiffs’ favor, the court determined that the Hotel Operators were independently reacting to similar pressures within an interdependent market, consistent with lawful conscious parallelism.

Finally, the court rejected plaintiffs’ contention that the Hotel Operators used Cendyn to exchange confidential information or, in the alternative, that Cendyn used machine learning and algorithms to facilitate the exchange of confidential information. The court reasoned that without more evidence, “using data across all your customers for research does not plausibly suggest that one customer has access to the confidential information of another customer—it instead plausibly suggests that Cendyn uses data from various customers to improve its products.”[5] The Cendyn dismissal will not be the last word on the “relatively novel antitrust theory premised on algorithmic pricing.”[6] Pricing algorithms are the focus of three class action lawsuits pending in different jurisdictions.[7] As algorithms become a mainstream tool for pricing, more are certain to follow.

[1] A hub-and-spoke antitrust conspiracy consists of (i) a leading party (“the hub”); (ii) co-conspirators (“the spokes”); and (iii) connecting agreements (“the rim”).

[2] See generally Order, Gibson v. Cendyn Group, Inc., 2:23-cv-00140-MMD-DJA (D. Nev. Oct. 23, 2023).

[3] Order, Gibson v. Cendyn Group, Inc., 2:23-cv-00140-MMD-DJA at 4 (D. Nev. May 8, 2024).

[4] Id. at 6.

[5] Id. at 10.

[6] Id. at 5.

[7] See Cornish-Adebiyi v. Caesars Entertainment, Inc., 1:23-cv-02536-KMW-EAP (D. N.J. filed Mar. 28, 2024); Duffy v. Yardi Sys. Inc., 2:23-cv-01391-RSL (W.D. Wash. filed on Mar. 1, 2024); In re: RealPage, Rental Software Antitrust Litig., 3:23-md-03071 (M.D. Tenn. filed on Nov. 15, 2023).

Copyright © 2024 Robinson & Cole LLP. All rights reserved.
For more news on Algorithmic Price Inflation Lawsuits, visit the NLR Antitrust & Trade Regulation.

The Double-Edged Impact of AI Compliance Algorithms on Whistleblowing

As the implementation of Artificial Intelligence (AI) compliance and fraud detection algorithms within corporations and financial institutions continues to grow, it is crucial to consider how this technology has a twofold effect.

It’s a classic double-edged technology: in the right hands it can help detect fraud and bolster compliance, but in the wrong it can snuff out would-be-whistleblowers and weaken accountability mechanisms. Employees should assume it is being used in a wide range of ways.

Algorithms are already pervasive in our legal and governmental systems: the Securities and Exchange Commission, a champion of whistleblowers, employs these very compliance algorithms to detect trading misconduct and determine whether a legal violation has taken place.

There are two major downsides to the implementation of compliance algorithms that experts foresee: institutions avoiding culpability and tracking whistleblowers. AI can uncover fraud but cannot guarantee the proper reporting of it. This same technology can be used against employees to monitor and detect signs of whistleblowing.

Strengths of AI Compliance Systems:

AI excels at analyzing vast amounts of data to identify fraudulent transactions and patterns that might escape human detection, allowing institutions to quickly and efficiently spot misconduct that would otherwise remain undetected.

AI compliance algorithms are promised to operate as follows:

  • Real-time Detection: AI can analyze vast amounts of data, including financial transactions, communication logs, and travel records, in real-time. This allows for immediate identification of anomalies that might indicate fraudulent activity.
  • Pattern Recognition: AI excels at finding hidden patterns, analyzing spending habits, communication patterns, and connections between seemingly unrelated entities to flag potential conflicts of interest, unusual transactions, or suspicious interactions.
  • Efficiency and Automation: AI can automate data collection and analysis, leading to quicker identification and investigation of potential fraud cases.

Yuktesh Kashyap, associate Vice President of data science at Sigmoid explains on TechTarget that AI allows financial institutions, for example, to “streamline compliance processes and improve productivity. Thanks to its ability to process massive data logs and deliver meaningful insights, AI can give financial institutions a competitive advantage with real-time updates for simpler compliance management… AI technologies greatly reduce workloads and dramatically cut costs for financial institutions by enabling compliance to be more efficient and effective. These institutions can then achieve more than just compliance with the law by actually creating value with increased profits.”

Due Diligence and Human Oversight

Stephen M. Kohn, founding partner of Kohn, Kohn & Colapinto LLP, argues that AI compliance algorithms will be an ineffective tool that allow institutions to escape liability. He worries that corporations and financial institutions will implement AI systems and evade enforcement action by calling it due diligence.

“Companies want to use AI software to show the government that they are complying reasonably. Corporations and financial institutions will tell the government that they use sophisticated algorithms, and it did not detect all that money laundering, so you should not sanction us because we did due diligence.” He insists that the U.S. Government should not allow these algorithms to be used as a regulatory benchmark.

Legal scholar Sonia Katyal writes in her piece “Democracy & Distrust in an Era of Artificial Intelligence” that “While automation lowers the cost of decision making, it also raises significant due process concerns, involving a lack of notice and the opportunity to challenge the decision.”

While AI can be used as a powerful tool for identifying fraud, there is still no method for it to contact authorities with its discoveries. Compliance personnel are still required to blow the whistle, given societies standard due process. These algorithms should be used in conjunction with human judgment to determine compliance or lack thereof. Due process is needed so that individuals can understand the reasoning behind algorithmic determinations.

The Double-Edged Sword

Darrell West, Senior Fellow at Brookings Institute’s Center for Technology Innovation and Douglas Dillon Chair in Governmental Studies warns about the dangerous ways these same algorithms can be used to find whistleblowers and silence them.

Nowadays most office jobs (whether remote or in person) conduct operations fully online. Employees are required to use company computers and networks to do their jobs. Data generated by each employee passes through these devices and networks. Meaning, your privacy rights are questionable.

Because of this, whistleblowing will get much harder – organizations can employ the technology they initially implemented to catch fraud to instead catch whistleblowers. They can monitor employees via the capabilities built into our everyday tech: cameras, emails, keystroke detectors, online activity logs, what is downloaded, and more. West urges people to operate under the assumption that employers are monitoring their online activity.

These techniques have been implemented in the workplace for years, but AI automates tracking mechanisms. AI gives organizations more systematic tools to detect internal problems.

West explains, “All organizations are sensitive to a disgruntled employee who might take information outside the organization, especially if somebody’s dealing with confidential information, budget information or other types of financial information. It is just easy for organizations to monitor that because they can mine emails. They can analyze text messages; they can see who you are calling. Companies could have keystroke detectors and see what you are typing. Since many of us are doing our jobs in Microsoft Teams meetings and other video conferencing, there is a camera that records and transcribes information.”

If a company is defining a whistleblower as a problem, they can monitor this very information and look for keywords that would indicate somebody is engaging in whistleblowing.

With AI, companies can monitor specific employees they might find problematic (such as a whistleblower) and all the information they produce, including the keywords that might indicate fraud. Creators of these algorithms promise that soon their products will be able to detect all sorts of patterns and feelings, such as emotion and sentiment.

AI cannot determine whether somebody is a whistleblower, but it can flag unusual patterns and refer those patterns to compliance analysts. AI then becomes a tool to monitor what is going on within the organization, making it difficult for whistleblowers to go unnoticed. The risk of being caught by internal compliance software will be much greater.

“The only way people could report under these technological systems would be to go offline, using their personal devices or burner phones. But it is difficult to operate whistleblowing this way and makes it difficult to transmit confidential information. A whistleblower must, at some point, download information. Since you will be doing that on a company network, and that is easily detected these days.”

But the question of what becomes of the whistleblower is based on whether the compliance officers operate in support of the company or the public interest – they will have an extraordinary amount of information about the company and the whistleblower.

Risks for whistleblowers have gone up as AI has evolved because it is harder for them to collect and report information on fraud and compliance without being discovered by the organization.

West describes how organizations do not have a choice whether or not to use AI anymore: “All of the major companies are building it into their products. Google, Microsoft, Apple, and so on. A company does not even have to decide to use it: it is already being used. It’s a question of whether they avail themselves of the results of what’s already in their programs.”

“There probably are many companies that are not set up to use all the information that is at their disposal because it does take a little bit of expertise to understand data analytics. But this is just a short-term barrier, like organizations are going to solve that problem quickly.”

West recommends that organizations should just be a lot more transparent about their use of these tools. They should inform their employees what kind of information they are using, how they are monitoring employees, and what kind of software they use. Are they using detection? Software of any sort? Are they monitoring keystrokes?

Employees should want to know how long information is being stored. Organizations might legitimately use this technology for fraud detection, which might be a good argument to collect information, but it does not mean they should keep that information for five years. Once they have used the information and determined whether employees are committing fraud, there is no reason to keep it. Companies are largely not transparent about length of storage and what is done with this data and once it is used.

West believes that currently, most companies are not actually informing employees of how their information is being kept and how the new digital tools are being utilized.

The Importance of Whistleblower Programs:

The ability of AI algorithms to track whistleblowers poses a real risk to regulatory compliance given the massive importance of whistleblower programs in the United States’ enforcement of corporate crime.

The whistleblower programs at the Securities and Exchange Commission (SEC) and Commodity Futures Trading Commission (CFTC) respond to individuals who voluntarily report original information about fraud or misconduct.

If a tip leads to a successful enforcement action, the whistleblowers are entitled to 10-30% of the recovered funds. These programs have created clear anti-retaliation protections and strong financial incentives for reporting securities and commodities fraud.

Established in 2010 under the Dodd-Frank Act, these programs have been integral to enforcement. The SEC reports that whistleblower tips have led to over $6 billion in sanctions while the CFTC states that almost a third of its investigations stem from whistleblower disclosures.

Whistleblower programs, with robust protections for those who speak out, remain essential for exposing fraud and holding organizations accountable. This ensures that detected fraud is not only identified, but also reported and addressed, protecting taxpayer money, and promoting ethical business practices.

If AI algorithms are used to track down whistleblowers, their implementation would hinder these programs. Companies will undoubtedly retaliate against employees they suspect of blowing the whistle, creating a massive chilling effect where potential whistleblowers would not act out of fear of detection.

Already being employed in our institutions, experts believe these AI-driven compliance systems must have independent oversight for transparency’s sake. The software must also be designed to adhere to due process standards.

Copyright Kohn, Kohn & Colapinto, LLP 2024. All Rights Reserved.
For more news on AI Compliance and Whistleblowing, visit the NLR Communications, Media & Internet section.

To Satisfy New Search Algorithms, Legal Websites Need Quality Content

The success of a law-firm website is determined by how many clients and potential clients visit the site, spend time there and take action based on what they discover.

Over the years, law-firm marketers focused on keyword and link strategies to enhance search engine results and increase traffic to their websites.  While these are still valuable tools, recent developments in the search universe have shifted the emphasis to content strategy.

Quality content includes well-written articles, blog posts, videos, webcasts, presentation slide decks, infographics, eBooks and white papers.  Quality content addresses client needs.

Sixty-seven percent of the time, online searchers use Google to find what they are looking for.  To provide the best results, Google is constantly tweaking its search algorithm. (An algorithm is a process or set of rules to be used by a computer in calculations or other problem-solving operations.)  These algorithms are designed to maintain search engine integrity and punish violators.

Sara Downey Robinson and Chris Davis discussed the changing landscape of digital marketing and search engine optimization at the monthly meeting of the Rocky Mountain Chapter of the Legal Marketing Association, held May 13 at Guard and Grace in LoDo Denver.

Davis is business development director at Burns Marketing, a full-service B2B marketing agency that combines traditional and digital marketing to help clients drive demand.   Robinson is marketing coordinator at Inflow, a top inbound-marketing firm specializing in search.

Panda, Penguin and Hummingbird

Panda and Penguin are two major changes to the existing Google algorithm made in 2011 and 2012, respectively.   In 2013, Google released a totally new algorithm called Hummingbird (which incorporates and enhances the updates made by Panda and Penguin).  These three developments have completely changed the way law firms must look at search.

“Law-firm sites that regularly showed up on page one now find themselves on page 20,” said Robinson.  “Since searchers rarely go beyond the second page of results in an online search, this is a real problem.”

Google Panda focuses on keywords.  Sites with keyword “stuffing” are demoted or flagged as spam.  Panda also penalizes low-quality content, thin content, duplicate content and the amount of advertising compared with the amount of useful content on a site.

Google Penguin focuses on links.  It focuses on “black hat” tactics like links that come from poor-quality sites, from sites that aren’t topically relevant to a target market, paid links, and links where the anchor text is overly optimized (exact-match anchor text).  Use natural language in your links, and vary it.

“Quality inbound links are not found at garage sales, “said Robinson.  “Steer clear of link farms.  A few high-quality, carefully developed links perform much better than a large number of weak, irrelevant links.  It takes time and perhaps a dedicated staff person to develop and nurture quality links.”

The new Google Hummingbird algorithm looks for a steady stream of high-quality, relevant content and natural language on webpages – and rewards those who provide it.  Hummingbird attempts to decipher a search engine query by using the context of a question rather than the specific keywords within the question.  Thin content, keyword stuffing and lack of relevant content will cause significant demotions.

“Content marketing is a technique that creates and distributes valuable, relevant and consistent content to attract and acquire a clearly defined audience,” said  Davis, “with the objective of driving profitable customer action.”

Identify client personas and clarify their needs

Before a law firm can create relevant content, it needs to know with whom it is communicating.  In marketing talk, this is called the “user persona” – or target market.

“In user-centered design and marketing, personas are user types that might use a legal service in a similar way,” said Davis.  “A small law firm might target one user persona.  A large law firm will target numerous user personas.”

One law-firm user persona might be high-income individuals going through divorce.  Another might be small businesses in need of venture capital.  Another might be large medical equipment manufacturers facing product liability lawsuits.  The more specific the persona, the more specific a law firm’s content can be.  Relevant content will answer the questions these users are asking, using natural language.

A user personal is a representation of the goals and behavior of a hypothesized group of users.  In most cases, personas are synthesized from data collected from user interviews.

“An effective law firm website will focus not on the firm’s capabilities, but on the identified needs of a persona or personas,” said Davis.  “It will use industry- or interest-specific terminology within a context familiar to the targeted persona.”

Create relevant content

Law firms that want to prevent or correct loss of search engine result page rankings and traffic should publish meaningful, original content on a regular basis.  The goal is content that will establish a firm, practice group or lawyer as a though leader in an area relevant to a user persona.

“Take the time to discover the common questions your clients have, and provide the answers to these questions,” said Davis.  “Relevant content can be written, but it also can and should be visual.  Video content posted on YouTube (which is owned by Google) is particularly powerful as ‘Google juice.’”

Instead of using keywords like “car accident,” use more specific terms like “car accident lawsuit” or “car accident insurance”, or better yet natural language terms like “What should I do if I am sued for a DUI car accident?” or “What should I look for when buying car insurance for an older vehicle?”  Think in terms of full-fledged questions that a person might ask Siri on a smartphone.

Once search brings users to a law firm’s site, there must be a way to create and nurture a relationship and convert the potential client into a real client over time.  Each item of posted content should contain a call to action – some way for the user to interact with the site so that the firm can capture data.  This could be a way to comment on a white paper or download information about an upcoming event.

Use analytics to measure success

“Take advantage of Google Analytics to collect data that can be used to improve the quality of your webpages – adding more of what works and eliminating what does not,” said Robinson.  “In Google Analytics, which is currently free, law firms can set up specific goals to study how users are entering and interacting with your website.”

Google Analytics lets a law firm know which content is most-viewed and acted upon, so that similar content can be added.  It lets the firm know which content is ignored, so that it can be eliminated or improves.  It lets a firm know the exact path users take through its site, so that adjustments can be made to create a better user experience.

If observation and analytics show that a law firm website is not getting the results it wants, an audit can help determine the source of the problem, take steps to fix the problem, measure the results of these steps, and look for any others areas that could be improved.

“Increasing inbound traffic to your website is not magic – it is a combination of art and science,” said Robinson.  “You should select any agency that makes you feel comfortable and uses language that is easy to understand.  You should never feel intimidated.

“At the same time, do not expect miracles,” said Robinson.  “Go into the process with reasonable expectations.   It takes time to make changes, add quality content and wait for the search engines to find and reward this content.  Each day, more than one million pieces of new content are posted to the Internet.  It takes time to rise above the fray.”

A law firm that has experienced worsening search engine results in the wake of Panda, Penguin and Hummingbird can take positive steps to restore performance.   Google will continue to reward webpages with strong content marketing efforts, including answer-driven content.  It also rewards sites that generate social media buzz – especially an active presence on its proprietary YouTube and Google+ platforms.

Article By:

Janet Ellen Raasch
Of:
Strategic Writing And Ghostwriting