Technology

Meet Airbnb’s official party pooper, who reduced partying by 55% in two years

Published

1 year ago

September 19, 2023

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Naba Banerjee, Airbnb

Source: Prashant Joshi | Airbnb

Naba Banerjee is a proud party pooper.

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.”

Technology

Mark Zuckerberg slams Apple on its lack of innovation and ‘random rules’

Published

18 hours ago

January 11, 2025

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Mark Zuckerberg slams Apple on its lack of innovation and 'random rules'

Meta CEO Mark Zuckerberg appears at the Meta Connect event in Menlo Park, California, Sept. 25, 2024.

David Paul Morris | Bloomberg | Getty Images

Meta CEO Mark Zuckerberg slammed rival tech giant Apple for lackluster innovation efforts and “random rules” in a lengthy podcast interview on Friday.

“On the one hand, [the iPhone has] been great, because now pretty much everyone in the world has a phone, and that’s kind of what enables pretty amazing things,” Zuckerberg said in an episode of the “Joe Rogan Experience.” “But on the other hand … they have used that platform to put in place a lot of rules that I think feel arbitrary and [I] feel like they haven’t really invented anything great in a while. It’s like Steve Jobs invented the iPhone, and now they’re just kind of sitting on it 20 years later.”

Zuckerberg added that he thought iPhone sales were struggling because consumers are taking longer to upgrade their phones because new models aren’t big improvements from prior iterations.

“So how are they making more money as a company? Well, they do it by basically, like, squeezing people, and, like you’re saying, having this 30% tax on developers by getting you to buy more peripherals and things that plug into it,” Zuckerberg said. “You know, they build stuff like Air Pods, which are cool, but they’ve just thoroughly hamstrung the ability for anyone else to build something that can connect to the iPhone in the same way.”

Apple defends itself from pushback from other companies by saying that it doesn’t want to violate consumers’ privacy and security, according to Zuckerberg. But he said that the problem would be solved if Apple fixed its protocol, like building better security and using encryption.

“It’s insecure because you didn’t build any security into it. And then now you’re using that as a justification for why only your product can connect in an easy way,” Zuckerberg said.

Zuckerberg said that if Apple stopped applying its “random rules,” Meta’s profit would double.

He also took shots at Apple’s Vision Pro headset, which had disappointing U.S. sales. Meta sells its own virtual headsets called the Meta Quest.

“I think the Vision Pro is, I think, one of the bigger swings at doing a new thing that they tried in a while,” Zuckerberg said. “And I don’t want to give them too hard of a time on it, because we do a lot of things where the first version isn’t that good, and you want to kind of judge the third version of it. But I mean, the V1, it definitely did not hit it out of the park.”

“I heard it’s really good for watching movies,” he added.

Apple did not immediately respond to a request for comment from CNBC.

Technology

Why Meta had to ‘bend the knee to Trump’ ahead of his inauguration

Published

1 day ago

January 11, 2025

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Why Meta had to 'bend the knee to Trump' ahead of his inauguration

Jakub Porzycki | Nurphoto | Getty Images

Mark Zuckerberg’s announcement this week that Meta would pivot its moderation policies to allow more “free expression” was widely viewed as the company’s latest effort to appease President-elect Donald Trump.

More than any of its Silicon Valley peers, Meta has taken numerous public steps to make amends with Trump since his election victory in November.

That follows a highly contentious four years between the two during Trump’s first term in office, which ended with Facebook — similar to other social media companies — banning Trump from its platform.

As recently as March, Trump was using his preferred nickname of “Zuckerschmuck” when talking about Meta’s CEO and declaring that Facebook was an “enemy of the people.”

With Meta now positioning itself to be a key player in artificial intelligence, Zuckerberg recognizes the need for White House support as his company builds data centers and pursues policies that will allow it to fulfill its lofty ambitions, according to people familiar with the company’s plans who asked not to be named because they weren’t authorized to speak on the matter.

“Even though Facebook is as powerful as it is, it still had to bend the knee to Trump,” said Brian Boland, a former Facebook vice president, who left the company in 2020.

Meta declined to comment for this article.

In Tuesday’s announcement, Zuckerberg said Meta will end third-party fact-checking, remove restrictions on topics such as immigration and gender identity and bring political content back to users’ feeds. Zuckerberg pitched the sweeping policy changes as key to stabilizing Meta’s content-moderation apparatus, which he said had “reached a point where it’s just too many mistakes and too much censorship.”

The policy change was the latest strategic shift Meta has taken to buddy up with Trump and Republicans since Election Day.

A day earlier, Meta announced that UFC CEO Dana White, a longtime Trump friend, is joining the company’s board.

And last week, Meta announced that it was replacing Nick Clegg, its president of global affairs, with Joel Kaplan, who had been the company’s policy vice president. Clegg previously had a career in British politics with the Liberal Democrats party, including as a deputy prime minister, while Kaplan was a White House deputy chief of staff under former President George W. Bush.

Kaplan, who joined Meta in 2011 when it was still known as Facebook, has longstanding ties to the Republican Party and once worked as a law clerk for the late conservative Supreme Court Justice Antonin Scalia. In December, Kaplan posted photos on Facebook of himself with Vice President-elect JD Vance and Trump during their visit to the New York Stock Exchange.

Joel Kaplan, Facebook’s vice president of global policy, on April 17, 2018.

Niall Carson | PA Images | Getty Images

Many Meta employees criticized the policy change internally, with some saying the company is absolving itself of its responsibility to create a safe platform. Current and former employees also expressed concern that marginalized communities could face more online abuse due to the new policy, which is set to take effect over the coming weeks.

Despite the backlash from employees, people familiar with the company’s thinking said Meta is more willing to make these kinds of moves after laying off 21,000 employees, or nearly a quarter of its workforce, in 2022 and 2023.

Those cuts affected much of Meta’s civic integrity and trust and safety teams. The civic integrity group was the closest thing the company had to a white-collar union, with members willing to push back against certain policy decisions, former employees said. Since the job cuts, Zuckerberg faces less friction when making broad policy changes, the people said.

Zuckerberg’s overtures to Trump began in the months leading up to the election.

Following the first assassination attempt on Trump in July, Zuckerberg called the photo of Trump raising his fist with blood running down his face “one of the most badass things I’ve ever seen in my life.”

A month later, Zuckerberg penned a letter to the House Judiciary Committee alleging that the Biden administration had pressured Meta’s teams to censor certain Covid-19 content.

“I believe the government pressure was wrong, and I regret that we were not more outspoken about it,” he wrote.

After Trump’s presidential victory, Zuckerberg joined several other technology executives who visited the president-elect’s Mar-a-Lago resort in Florida. Meta also donated $1 million to Trump’s inaugural fund.

On Friday, Meta revealed to its workforce in a memo obtained by CNBC that it intends to shutter several internal programs related to diversity and inclusion in its hiring process, representing another Trump-friendly move.

The previous day, some details of the company’s new relaxed content-moderation guidelines were published by the news site The Intercept, showing the kind of offensive rhetoric that Meta’s new policy would now allow, including statements such as “Migrants are no better than vomit” and “I bet Jorge’s the one who stole my backpack after track practice today. Immigrants are all thieves.”

Recalibrating for Trump

Zuckerberg, who has been dragged to Washington eight times to testify before congressional committees during the last two administrations, wants to be perceived as someone who can work with Trump and the Republican Party, people familiar with the matter said.

Though Meta’s content-policy updates caught many of its employees and fact-checking partners by surprise, a small group of executives were formulating the plans in the aftermath of the U.S. election results. By New Year’s Day, leadership began planning the public announcements of its policy change, the people said.

Meta typically undergoes major “recalibrations” after prominent U.S. elections, said Katie Harbath, a former Facebook policy director and CEO of tech consulting firm Anchor Change. When the country undergoes a change in power, Meta adjusts its policies to best suit its business and reputational needs based on the political landscape, Harbath said.

“In 2028, they’ll recalibrate again,” she said.

After the 2016 election and Trump’s first victory, for example, Zuckerberg toured the U.S. to meet people in states he hadn’t previously visited. He published a 6,000-word manifesto emphasizing the need for Facebook to build more community.

The social media company faced harsh criticism about fake news and Russian election interference on its platforms after the 2016 election.

Following the 2020 election, during the heart of the pandemic, Meta took a harder stand on Covid-19 content, with a policy executive saying in 2021 that the “amount of COVID-19 vaccine misinformation that violates our policies is too much by our standards.” Those efforts may have appeased the Biden administration, but it drew the ire of Republicans.

Meta is once again reacting to the moment, Harbath said.

“There wasn’t a business risk here in Silicon Valley to be more right-leaning,” Harbath said.

While Trump has offered few specific policy proposals for his second administration, Meta has plenty at stake.

The White House could create more relaxed AI regulations compared with those in the European Union, where Meta says harsh restrictions have resulted in the company not releasing some of its more advanced AI technologies. Meta, like other tech giants, also needs more massive data centers and cutting-edge computer chips to help train and run their advanced AI models.

“There’s a business benefit to having Republicans win, because they are traditionally less regulatory,” Harbath said.

Meta’s CEO Mark Zuckerberg reacts as he testifies during the Senate Judiciary Committee hearing on online child sexual exploitation at the U.S. Capitol in Washington, U.S., January 31, 2024.

Evelyn Hockstein | Reuters

Meta isn’t alone in trying to cozy up to Trump. But the extreme measures the company is taking reflects a particular level of animus expressed by Trump over the years.

Trump has accused Meta of censorship and has expressed resentment over the company’s two-year suspension of his Facebook and Instagram accounts following the Jan. 6 attack on the Capitol.

In July 2024, Trump posted on Truth Social that he intended to “pursue Election Fraudsters at levels never seen before, and they will be sent to prison for long periods of time,” adding “ZUCKERBUCKS, be careful!” Trump reiterated that statement in his book, “Save America,” writing that Zuckerberg plotted against him during the 2020 election and that the Meta CEO would “spend the rest of his life in prison” if it happened again.

Meta spends $14 million annually on providing personal security for Zuckerberg and his family, according to the company’s 2024 proxy statement. As part of that security, the company analyzes any threats or perceived threats against its CEO, according to a person familiar with the matter. Those threats are cataloged, analyzed and dissected by Meta’s multitude of security teams.

After Trump’s comments, Meta’s security teams analyzed how Trump could weaponize the Justice Department and the country’s intelligence agencies against Zuckerberg and what it would cost the company to defend its CEO against a sitting president, said the person, who asked not to be named because of confidentiality.

Meta’s efforts to appease the incoming president bring their own risks.

After Zuckerberg announced the new speech policy Tuesday, Boland, the former executive, was among a number of users who took to Meta’s Threads service to tell their followers that they were quitting Facebook.

“Last post before deleting,” Boland wrote in his post.

Before the post could be seen by any of his Threads followers, Meta’s content moderation system had taken it down, citing cybersecurity reasons.

Boland told CNBC in an interview that he couldn’t help but chuckle at the situation.

“It’s deeply ironic,” Boland said.

— CNBC’s Salvador Rodriguez contributed to this report.

WATCH: Meta is returning to free speech tradition, says Facebook’s former chief privacy officer Chris Kelly

Technology

Apple’s market share slides in China as iPhone shipments decline, analyst Kuo says

Published

2 days ago

January 10, 2025

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Apple's market share slides in China as iPhone shipments decline, analyst Kuo says

Jaap Arriens | Nurphoto | Getty Images

Apple is losing market share in China due to declining iPhone shipments, supply chain analyst Ming-Chi Kuo wrote in a report on Friday. The stock slid 2.4%.

“Apple has adopted a cautious stance when discussing 2025 iPhone production plans with key suppliers,” Kuo, an analyst at TF Securities, wrote in the post. He added that despite the expected launch of the new iPhone SE 4, shipments are expected to decline 6% year over year for the first half of 2025.

Kuo expects Apple’s market share to continue to slide, as two of the coming iPhones are so thin that they likely will only support eSIM, which the Chinese market currently does not promote.

“These two models could face shipping momentum challenges unless their design is modified,” he wrote.

Kuo wrote that in December, overall smartphone shipments in China were flat from a year earlier, but iPhone shipments dropped 10% to 12%.

There is also “no evidence” that Apple Intelligence, the company’s on-device artificial intelligence offering, is driving hardware upgrades or services revenue, according to Kuo. He wrote that the feature “has not boosted iPhone replacement demand,” according to a supply chain survey he conducted, and added that in his view, the feature’s appeal “has significantly declined compared to cloud-based AI services, which have advanced rapidly in subsequent months.”

Apple’s estimated iPhone shipments total about 220 million units for 2024 and between about 220 million and 225 million for this year, Kuo wrote. That is “below the market consensus of 240 million or more,” he wrote.

Apple did not immediately respond to CNBC’s request for comment.

WATCH: Apple has to do something to justify its runup