As the person in charge of Airbnb’s worldwide ban on parties, she’s spent more than three years figuring out how to battle party “collusion” by users, flag “repeat party houses” and, most of all, design an anti-party AI system with enough training data to halt high-risk reservations before the offender even gets to the checkout page.
It’s been a bit like a game of whack-a-mole: Whenever Banerjee’s algorithms flag some concerns, new ones pop up.
Airbnb defines a party as a gathering that occurs at an Airbnb listing and “causes significant disruption to neighbors and the surrounding community,” according to a company rep. To determine violations, the company considers whether the gathering is an open-invite one, and whether it involves excessive noise, trash, visitors, parking issues for neighbors, and other factors.
Bannerjee joined the company’s trust and safety team in May 2020 and now runs that group. In her short time at the company, she’s overseen a ban on high-risk reservations by users aged 25 and under, an pilot program for anti-party AI in Australia, heightened defenses on holiday weekends, a host insurance policy worth millions of dollars, and this summer, a global rollout of Airbnb’s reservation screening system.
Some measures have worked better than others, but the company says party reports dropped 55% between August 2020 and August 2022 — and since the worldwide launch of Banerjee’s system in May, more than 320,000 guests have been blocked or redirected from booking attempts on Airbnb.
Overall, the company’s business is getting stronger as the post-pandemic travel boom starts to fade. Last month, the company reported earnings that beat analysts’ expectations on earnings per share and revenue, with the latter growing 18% year-over-year, despite fewer-than-expected number of nights and experiences booked via the platform.
Turning parental party radar into an algorithm
Courtesy: Airbnb
Airbnb says the pandemic and hosts’ fears of property damage are the main drivers behind its anti-party push, but there have been darker incidents as well.
A Halloween party at an Airbnb in 2019 left five people dead. This year between Memorial Day and Labor Day weekends, at least five people were killed at parties hosted at Airbnbs. In June, the company was sued by a family who lost their 18-year-old son in a shooting at a 2021 Airbnb party.
When Banerjee first joined Airbnb’s trust team in summer 2020, she recalls people around her asking, “How do you solve this problem?” The stream of questions, from people above and below her on the corporate ladder, contributed to her anxiety. Airbnb’s party problem was complex, and in some ways, she didn’t know where to start.
As a mother of five, Banerjee knows how to sniff out a secretive shindig.
Last summer, Banerjee’s 17-year-old daughter had a friend who wanted to throw an 18th birthday party – and she was thinking about booking an Airbnb to do it. Banerjee recalls her daughter telling her about the plan, asking her whether she should tell her friend not to book an Airbnb because of the AI safeguards. The friend ended up throwing the party at her own home.
“Being a mother of teenagers and seeing teenage friends of my kids, your antenna is especially sharp and you have a radar for, ‘Oh my God, okay, this is a party about to happen,” Banerjee said. “Between our data scientists and our machine learning engineers and us, we started looking at these signals.”
For Banerjee, it was about translating that antenna into a usable algorithm.
In an April 2020 meeting with Nate Blecharczyk, the company’s co-founder and chief strategy officer, Banerjee recalls strategizing about ways to fix Airbnb’s party problem on three different time scales: “right now,” within a year, and in the general future.
For the “right now” scale, they talked about looking at platform data, studying the patterns and signals for current party reports, and seeing how those puzzle pieces align.
The first step, in July 2020, was rolling out a ban on high-risk reservations by users under the age of 25, especially those who either didn’t have much history on the platform or who didn’t have good reviews from hosts. Although Airbnb says that blocked or redirected “thousands” of guests globally, Banerjee still saw users trying to get around the ban by having an older friend or relative book the reservation for them. Two months later, Airbnb announced a “global party pan,” but that was mostly lip service – at least, until they had the technology to back it up.
Around the same time, Banerjee sent out a series of invitations. Rather than to a party, they were invites to attend party risk reduction workshops, sent to Airbnb designers, data scientists, machine learning engineers and members of the operations and communications teams. In Zoom meetings, they looked at results from the booking ban for guests under age 25 and started putting further plans in motion: Banerjee’s team created a 24/7 safety line for hosts, rolled out a neighborhood support line, and decided to staff up the customer support call center.
One of the biggest takeaways, though, was to remove the option for hosts to list their home as available for gatherings of more than 16 people.
Courtesy: Airbnb
Now that they had a significant amount of data on how potential partiers might act, Banerjee’s had a new goal: Build the AI equivalent of a neighbor checking on the house when the high-schooler’s parents leave them home alone for the weekend.
Around January 2021, Banerjee recalled hearing from Airbnb’s Australia offices that disruptive parties at Airbnbs were up and coming, just like they were in North America, as travel had come to a relative standstill and Covid was in full swing. Banerjee considered rolling out the under-25 ban in Australia, but after chatting with Blecharczyk, she decided to experiment with a party-banning machine learning model instead.
But Banerjee was nervous. Soon after, she phoned her father in Kolkata, India – it was between 10pm and 11pm for her, which was mid-morning for him. As the first female engineer in her family, Banerjee’s father is one of her biggest supporters, she said, and typically the person she calls during the most difficult moments of her life.
Banerjee said, “I remember talking to him saying, ‘I’m just very scared – I feel like I’m on the verge of doing one of the most important things of my career, but I still don’t know if we are going to succeed, like we have the pandemic going on, the business is hurting… We have something that we think is going to be great, but we don’t know yet. I’m just on this verge of uncertainty, and it just makes me really nervous.'”
Banerjee recalled her father telling her that this has happened to her before and that she’d succeed again. He’d be more worried, he told her, if she was overconfident.
In October 2021, Banerjee’s team rolled out the pilot program for their reservation screening AI in Australia. The company saw a 35% drop in parties between regions of the country that had the program versus those that did not. The team spent months analyzing the results and upgraded the system with more data, as well as safety and property damage incidents and records of user collusion.
How the AI system works to stop parties
Listings on Airbnb
Source: Airbnb
Imagine you’re a 21-year-old planning a Halloween party in your hometown. Your plan: Book an Airbnb house for one night, send out the “BYOB” texts and try to avoid posting cliched Instagram captions.
There’s just one problem: Airbnb’s AI system is working against you from the second you sign on.
The party-banning algorithm looks at hundreds of factors: the reservation’s closeness to the user’s birthday, the user’s age, length of stay, the listing’s proximity to where the user is based, how far in advance the reservation is being made, weekend vs. weekday, the type of listing and whether the listing is located in a heavily crowded location rather than a rural one.
Deep learning is a subset of machine learning that uses neural networks – that is, the systems process information in a way inspired by the human brain. The systems are certainly not functionally comparable to the human brain, but they do follow the pattern of learning by example. In the case of Airbnb, one model focuses specifically on the risk of parties, while another focuses on property damage, for instance.
“When we started looking at the data, we found that in most cases, we were noticing that these were bookings that were made extremely last-minute, potentially by a guest account that was created at the last minute, and then a booking was made for a potential party weekend such as New Year’s Eve or Halloween, and they would book an entire home for maybe one night,” Banerjee told CNBC. “And if you looked at where the guest actually lived, that was really in close proximity to where the listing was getting booked.”
After the models do their analysis, the system assigns every reservation a party risk. Depending on the risk tolerance that Airbnb has assigned for that country or area, the reservation will either be banned or greenlit. The team also introduced “heightened party defenses” for holiday weekends such as the Fourth of July, Halloween and New Year’s Eve.
Source: Airbnb
In some cases, like when the right decision isn’t quite clear, reservation requests are flagged for human review, and those human agents can look at the message thread to gauge party risk. But the company is also “starting to invest in a huge way” in large language models for content understanding, to help understand party incidents and fraud, Banerjee said.
“The LLM trend is something that if you are not on that train, it’s like missing out on the internet,” Banerjee told CNBC.
Banerjee said her team has seen a higher risk of parties in the U.S. and Canada, and the next-riskiest would probably be Australia and certain European countries. In Asia, reservations seem to be considerably less risky.
The algorithms are trained partly on tickets labeled as parties or property damage, as well as hypothetical incidents and past ones that occurred before the system went live to see if it would have flagged them. They’re also trained on what “good” guest behavior looks like, such as someone who checks in and out on time, leaves a review on time, and has no incidents on the platform.
But like many forms of AI training data, the idea of “good” guests is ripe for bias. Airbnb has introduced anti-discrimination experiments in the past, such as hiding guests’ photos, preventing hosts from viewing a guest’s full name before the booking is confirmed, and introducing a Smart Pricing tool to help address earnings disparities, although the latter unwittingly ended up widening the gap.
Airbnb said its reservation-screening AI has been evaluated by the company’s anti-discrimination team and that the company often tests the system in areas like precision and recall.
Going global
Courtesy: Airbnb
Almost exactly one year ago, Banerjee was at a plant nursery with her husband and mother-in-law when she received a call from Airbnb CEO Brian Chesky.
She thought he’d be calling about the results of the Australia pilot program, but instead he asked her about trust in the platform. Given all the talk she did about machine learning models and features, she recalled him asking her, would she feel safe sending one of her college-bound kids to stay at an Airbnb – and if not, what would make her feel safe?
That phone call ultimately resulted in the decision to expand Banerjee’s team’s reservation screening AI worldwide the following spring.
Things kicked into high gear, with TV spots for Banerjee, some of which she spotted in between pull-ups on the gym television. She asked her daughter for advice on what to wear. The next thing she knew, the team was getting ready for a live demo of the reservation screening AI with Chesky. Banerjee was nervous.
Last fall, the team sat down with Chesky after working with front-end engineers to create a fake party risk, showing someone booking an entire mansion during a holiday weekend at the last minute and seeing if the model would flag it in real-time. It worked.
Chesky’s only feedback, Banerjee recalled, was to change the existing message – “Your reservation cannot be completed at this point in time because we detect a party risk” – to be more customer-friendly, potentially offering an option to appeal or book a different weekend. They followed his advice. Now, the message reads, “The details of this reservation indicate it could lead to an unauthorized party in the home. You still have the option to book a hotel or private room, or you can contact us with any questions.”
Over the next few months, Banerjee remembers a frenzy of activity but also feeling calm and confident. She went to visit her family in India in April 2023 for the first time in about a year. She told her father about the rollout excitement, which happened in batches the following month.
This past Labor Day, Banerjee was visiting her son in Texas as the algorithm blocked or redirected 5,000 potential party bookings.
But no matter how quickly the AI models learn, Banerjee and her team will need to continue to monitor and change the systems as party-inclined users figure out ways around the barriers.
“The interesting part about the world of trust and safety is that it never stays static,” Banerjee said. “As soon as you build a defense, some of these bad actors out there who are potentially trying to buck the system and throw a party, they will get smarter and they’ll try to do something different.”
Elon Musk’s business empire is sprawling. It includes electric vehicle maker Tesla, social media company X, artificial intelligence startup xAI, computer interface company Neuralink, tunneling venture Boring Company and aerospace firm SpaceX.
Some of his ventures already benefit tremendously from federal contracts. SpaceX has received more than $19 billion from contracts with the federal government, according to research from FedScout. Under a second Trump presidency, more lucrative contracts could come its way. SpaceX is on track to take in billions of dollars annually from prime contracts with the federal government for years to come, according to FedScout CEO Geoff Orazem.
Musk, who has frequently blamed the government for stifling innovation, could also push for less regulation of his businesses. Earlier this month, Musk and former Republican presidential candidate Vivek Ramaswamy were tapped by Trump to lead a government efficiency group called the Department of Government Efficiency, or DOGE.
In a recent commentary piece in the Wall Street Journal, Musk and Ramaswamy wrote that DOGE will “pursue three major kinds of reform: regulatory rescissions, administrative reductions and cost savings.” They went on to say that many existing federal regulations were never passed by Congress and should therefore be nullified, which President-elect Trump could accomplish through executive action. Musk and Ramaswamy also championed the large-scale auditing of agencies, calling out the Pentagon for failing its seventh consecutive audit.
“The number one way Elon Musk and his companies would benefit from a Trump administration is through deregulation and defanging, you know, giving fewer resources to federal agencies tasked with oversight of him and his businesses,” says CNBC technology reporter Lora Kolodny.
To learn how else Elon Musk and his companies may benefit from having the ear of the president-elect watch the video.
Elon Musk attends the America First Policy Institute gala at Mar-A-Lago in Palm Beach, Florida, Nov. 14, 2024.
Carlos Barria | Reuters
X’s new terms of service, which took effect Nov. 15, are driving some users off Elon Musk’s microblogging platform.
The new terms include expansive permissions requiring users to allow the company to use their data to train X’s artificial intelligence models while also making users liable for as much as $15,000 in damages if they use the platform too much.
The terms are prompting some longtime users of the service, both celebrities and everyday people, to post that they are taking their content to other platforms.
“With the recent and upcoming changes to the terms of service — and the return of volatile figures — I find myself at a crossroads, facing a direction I can no longer fully support,” actress Gabrielle Union posted on X the same day the new terms took effect, while announcing she would be leaving the platform.
“I’m going to start winding down my Twitter account,” a user with the handle @mplsFietser said in a post. “The changes to the terms of service are the final nail in the coffin for me.”
It’s unclear just how many users have left X due specifically to the company’s new terms of service, but since the start of November, many social media users have flocked to Bluesky, a microblogging startup whose origins stem from Twitter, the former name for X. Some users with new Bluesky accounts have posted that they moved to the service due to Musk and his support for President-elect Donald Trump.
Bluesky’s U.S. mobile app downloads have skyrocketed 651% since the start of November, according to estimates from Sensor Tower. In the same period, X and Meta’s Threads are up 20% and 42%, respectively.
X and Threads have much larger monthly user bases. Although Musk said in May that X has 600 million monthly users, market intelligence firm Sensor Tower estimates X had 318 million monthly users as of October. That same month, Meta said Threads had nearly 275 million monthly users. Bluesky told CNBC on Thursday it had reached 21 million total users this week.
Here are some of the noteworthy changes in X’s new service terms and how they compare with those of rivals Bluesky and Threads.
Artificial intelligence training
X has come under heightened scrutiny because of its new terms, which say that any content on the service can be used royalty-free to train the company’s artificial intelligence large language models, including its Grok chatbot.
“You agree that this license includes the right for us to (i) provide, promote, and improve the Services, including, for example, for use with and training of our machine learning and artificial intelligence models, whether generative or another type,” X’s terms say.
Additionally, any “user interactions, inputs and results” shared with Grok can be used for what it calls “training and fine-tuning purposes,” according to the Grok section of the X app and website. This specific function, though, can be turned off manually.
X’s terms do not specify whether users’ private messages can be used to train its AI models, and the company did not respond to a request for comment.
“You should only provide Content that you are comfortable sharing with others,” read a portion of X’s terms of service agreement.
Though X’s new terms may be expansive, Meta’s policies aren’t that different.
The maker of Threads uses “information shared on Meta’s Products and services” to get its training data, according to the company’s Privacy Center. This includes “posts or photos and their captions.” There is also no direct way for users outside of the European Union to opt out of Meta’s AI training. Meta keeps training data “for as long as we need it on a case-by-case basis to ensure an AI model is operating appropriately, safely and efficiently,” according to its Privacy Center.
Under Meta’s policy, private messages with friends or family aren’t used to train AI unless one of the users in a chat chooses to share it with the models, which can include Meta AI and AI Studio.
Bluesky, which has seen a user growth surge since Election Day, doesn’t do any generative AI training.
“We do not use any of your content to train generative AI, and have no intention of doing so,” Bluesky said in a post on its platform Friday, confirming the same to CNBC as well.
Liquidated damages
Another unusual aspect of X’s new terms is its “liquidated damages” clause. The terms state that if users request, view or access more than 1 million posts – including replies, videos, images and others – in any 24-hour period they are liable for damages of $15,000.
While most individual users won’t easily approach that threshold, the clause is concerning for some, including digital researchers. They rely on the analysis of larger numbers of public posts from services like X to do their work.
X’s new terms of service are a “disturbing move that the company should reverse,” said Alex Abdo, litigation director for the Knight First Amendment Institute at Columbia University, in an October statement.
“The public relies on journalists and researchers to understand whether and how the platforms are shaping public discourse, affecting our elections, and warping our relationships,” Abdo wrote. “One effect of X Corp.’s new terms of service will be to stifle that research when we need it most.”
Neither Threads nor Bluesky have anything similar to X’s liquidated damages clause.
Meta and X did not respond to requests for comment.
A recent Chinese cyber-espionage attack inside the nation’s major telecom networks that may have reached as high as the communications of President-elect Donald Trump and Vice President-elect J.D. Vance was designated this week by one U.S. senator as “far and away the most serious telecom hack in our history.”
The U.S. has yet to figure out the full scope of what China accomplished, and whether or not its spies are still inside U.S. communication networks.
“The barn door is still wide open, or mostly open,” Senator Mark Warner of Virginia and chairman of the Senate Intelligence Committee told the New York Times on Thursday.
The revelations highlight the rising cyberthreats tied to geopolitics and nation-state actor rivals of the U.S., but inside the federal government, there’s disagreement on how to fight back, with some advocates calling for the creation of an independent federal U.S. Cyber Force. In September, the Department of Defense formally appealed to Congress, urging lawmakers to reject that approach.
Among one of the most prominent voices advocating for the new branch is the Foundation for Defense of Democracies, a national security think tank, but the issue extends far beyond any single group. In June, defense committees in both the House and Senate approved measures calling for independent evaluations of the feasibility to create a separate cyber branch, as part of the annual defense policy deliberations.
Drawing on insights from more than 75 active-duty and retired military officers experienced in cyber operations, the FDD’s 40-page report highlights what it says are chronic structural issues within the U.S. Cyber Command (CYBERCOM), including fragmented recruitment and training practices across the Army, Navy, Air Force, and Marines.
“America’s cyber force generation system is clearly broken,” the FDD wrote, citing comments made in 2023 by then-leader of U.S. Cyber Command, Army General Paul Nakasone, who took over the role in 2018 and described current U.S. military cyber organization as unsustainable: “All options are on the table, except the status quo,” Nakasone had said.
Concern with Congress and a changing White House
The FDD analysis points to “deep concerns” that have existed within Congress for a decade — among members of both parties — about the military being able to staff up to successfully defend cyberspace. Talent shortages, inconsistent training, and misaligned missions, are undermining CYBERCOM’s capacity to respond effectively to complex cyber threats, it says. Creating a dedicated branch, proponents argue, would better position the U.S. in cyberspace. The Pentagon, however, warns that such a move could disrupt coordination, increase fragmentation, and ultimately weaken U.S. cyber readiness.
As the Pentagon doubles down on its resistance to establishment of a separate U.S. Cyber Force, the incoming Trump administration could play a significant role in shaping whether America leans toward a centralized cyber strategy or reinforces the current integrated framework that emphasizes cross-branch coordination.
Known for his assertive national security measures, Trump’s 2018 National Cyber Strategy emphasized embedding cyber capabilities across all elements of national power and focusing on cross-departmental coordination and public-private partnerships rather than creating a standalone cyber entity. At that time, the Trump’s administration emphasized centralizing civilian cybersecurity efforts under the Department of Homeland Security while tasking the Department of Defense with addressing more complex, defense-specific cyber threats. Trump’s pick for Secretary of Homeland Security, South Dakota Governor Kristi Noem, has talked up her, and her state’s, focus on cybersecurity.
Former Trump officials believe that a second Trump administration will take an aggressive stance on national security, fill gaps at the Energy Department, and reduce regulatory burdens on the private sector. They anticipate a stronger focus on offensive cyber operations, tailored threat vulnerability protection, and greater coordination between state and local governments. Changes will be coming at the top of the Cybersecurity and Infrastructure Security Agency, which was created during Trump’s first term and where current director Jen Easterly has announced she will leave once Trump is inaugurated.
Cyber Command 2.0 and the U.S. military
John Cohen, executive director of the Program for Countering Hybrid Threats at the Center for Internet Security, is among those who share the Pentagon’s concerns. “We can no longer afford to operate in stovepipes,” Cohen said, warning that a separate cyber branch could worsen existing silos and further isolate cyber operations from other critical military efforts.
Cohen emphasized that adversaries like China and Russia employ cyber tactics as part of broader, integrated strategies that include economic, physical, and psychological components. To counter such threats, he argued, the U.S. needs a cohesive approach across its military branches. “Confronting that requires our military to adapt to the changing battlespace in a consistent way,” he said.
In 2018, CYBERCOM certified its Cyber Mission Force teams as fully staffed, but concerns have been expressed by the FDD and others that personnel were shifted between teams to meet staffing goals — a move they say masked deeper structural problems. Nakasone has called for a CYBERCOM 2.0, saying in comments early this year “How do we think about training differently? How do we think about personnel differently?” and adding that a major issue has been the approach to military staffing within the command.
Austin Berglas, a former head of the FBI’s cyber program in New York who worked on consolidation efforts inside the Bureau, believes a separate cyber force could enhance U.S. capabilities by centralizing resources and priorities. “When I first took over the [FBI] cyber program … the assets were scattered,” said Berglas, who is now the global head of professional services at supply chain cyber defense company BlueVoyant. Centralization brought focus and efficiency to the FBI’s cyber efforts, he said, and it’s a model he believes would benefit the military’s cyber efforts as well. “Cyber is a different beast,” Berglas said, emphasizing the need for specialized training, advancement, and resource allocation that isn’t diluted by competing military priorities.
Berglas also pointed to the ongoing “cyber arms race” with adversaries like China, Russia, Iran, and North Korea. He warned that without a dedicated force, the U.S. risks falling behind as these nations expand their offensive cyber capabilities and exploit vulnerabilities across critical infrastructure.
Nakasone said in his comments earlier this year that a lot has changed since 2013 when U.S. Cyber Command began building out its Cyber Mission Force to combat issues like counterterrorism and financial cybercrime coming from Iran. “Completely different world in which we live in today,” he said, citing the threats from China and Russia.
Brandon Wales, a former executive director of the CISA, said there is the need to bolster U.S. cyber capabilities, but he cautions against major structural changes during a period of heightened global threats.
“A reorganization of this scale is obviously going to be disruptive and will take time,” said Wales, who is now vice president of cybersecurity strategy at SentinelOne.
He cited China’s preparations for a potential conflict over Taiwan as a reason the U.S. military needs to maintain readiness. Rather than creating a new branch, Wales supports initiatives like Cyber Command 2.0 and its aim to enhance coordination and capabilities within the existing structure. “Large reorganizations should always be the last resort because of how disruptive they are,” he said.
Wales says it’s important to ensure any structural changes do not undermine integration across military branches and recognize that coordination across existing branches is critical to addressing the complex, multidomain threats posed by U.S. adversaries. “You should not always assume that centralization solves all of your problems,” he said. “We need to enhance our capabilities, both defensively and offensively. This isn’t about one solution; it’s about ensuring we can quickly see, stop, disrupt, and prevent threats from hitting our critical infrastructure and systems,” he added.
