Mediaboss Marketing

SYRACUSE, N.Y. and BETHLEHEM, Pa., Nov. 12, 2021 /PRNewswire/ --With millions of mostly international users around the world, Lomotif (the short form video social media application owned by a joint venture of ZASH Global Media and Entertainment (ZASH) and Vinco Ventures, Inc. (NASDAQ: BBIG) (Vinco)) is already a stronghold of international users, but has faced questions about its potential to engage a domestic U.S. user base. On the weekend of October 22nd, Lomotif leveraged its partnership with EDC Las Vegas as a proving ground in the U.S. cultural landscape, eyeing a U.S. launch in 2022. Partnering with megastars Lil Nas X and The Kid Laroi, Lomotif used its reach and frequency tool under AdRizer for a broad reach into U.S. social media culture and cross platform pollination, outperforming expectations across every metric and delivering record engagement. Vinco and ZASH recently announced a letter of intent to acquire AdRizer.

The EDC activation and partnership was Lomotif's biggest domestic push to date to build awareness of the Lomotif platform. Lomotif tested cross platform pollination between Lomotif and other major social media platforms, including TikTok and Instagram. The number of users reached and engaged far exceeded Lomotif's expectations:

During the EDC festival, Lomotif also arranged surprise appearances and performances from headliner superstars Lil Nas X and The Kid Laroi, as Ted Farnsworth (Chairman of Lomotif parent company ZVV Media Partners, LLC, a joint venture of Vinco and ZASH) and Olivia Rudensky (CEO and Founder of FANMADE) made the 11th hour arrangements for the artists to perform for fans live, with millions able to share the experience remotely. At showtime, Lil Nas X was charting as the #1 artist on US Billboard Hot 100 and the #3 artist on Billboard's international Top 200, while The Kid Laroi was charting #1 internationally on Billboard and #2 on Billboard's US Hot 100. Lil Nas X shared a video of his performance on TikTok using #lomotif; that video alone recorded 7.5 million views on TikTok. Lomotif was chosen as an official streaming partner for the world's largest EDM festival alongside YouTube, Twitch, TikTok and Roblox. Lomotif streamed all performances on kineticFIELD, the main stage at EDC, including the performances by Lil Nas X and The Kid Laroi.

Beyond the cultural impact of the Lil Nas X and The Kid Laroi performances, EDC was a large scale testing ground for different initiatives through Lomotif and other companies within the ZASH ecosystem. ZASH tested cross platform pollination on platforms such as TikTok and Instagram, bringing cultural awareness to Lomotif as well as pointing the users of more entrenched platforms to Lomotif through exclusive content initiatives, including exclusive behind the scenes interviews with Lil Nas X, The Kid Laroi and other leading artists. After activating 150 top influencers during EDC Las Vegas, ZASH and Lomotif are continuing to build relationships with top artists and influencers across all platforms, who are expected to work in complement to the [social media accelerator platform] under AdRizer to amplify and substantiate Lomotif's reach.

"We received 57 million views on Instagram - all EDM or Lil Nas X fans, users that share similar interests," remarked Mr. Farnsworth. "This is the beginning of a powerful, engaged community on Lomotif," Mr. Farnsworth continued.

ZASH's beta test was also done in partnership with Socialkyte, one of the largest social media platforms for influencers in India and surrounding regions, and home to over 90,000 leading influencers. Socialkyte hosted a live streaming party at Bo Tai Switch in New Delhi, promoting Lomotif's EDC live stream to hundreds of millions of users. This was the first time Socialkyte has engaged with a US company to test any type of social media reach and frequency for a live streamed event, driving record engagement across the platform. Socialkyte's fan base spans 1.3 billion users across the globe, with the majority coming from India and surrounding nations.

Lomotif believes its social media accelerator, through AdRizer, will allow scalable monetization through this social media reach and frequency tool. This tool is expected to allow users to buy ads for as low as five dollars each to boost their personal pages, or for small businesses to market to hyper specific demographics and geographical regions anywhere in the world. Lomotif expects that advertisers on Lomotif will be able to control of how often the ads are served to different target audiences, when and where the ads will be served, and the ability to track their results and quickly adjust to real-time data.

"There's still work to be done to bring this tool to market, but knowing we can reach both broad and hyper specific demographics with peak reach and dynamic frequency, we believe this reach and frequency tool will facilitate advertisers to share their stories powerfully on our platform, as well as become an integral part of advertisers reaching users through other social platforms and digital advertising platforms and SEO optimization," continued Farnsworth. "We believe this is an industry leading, highly adaptable advertising tool that we expect will enable robust and scalable revenue streams, as we continue to expand our global reach."

Lomotif's EDC partnership and its associated reach and frequency beta test come in pursuit of greater engagement and are expected work hand in glove feeding potential advertisers to have the greatest relevance, reach and cost efficacy in their ad campaigns.

Lomotif also utilized its partnership with EDC to launch LoMo TV -an entertainment and lifestyle network offering original programming within the Lomotif app. During EDC, LoMo TV featured exclusive backstage content and interviews at the concert, as well as an exclusive performance from Lil Nas X and a new song debuting from the superstar artist. As a full time entertainment and lifestyle TV network, LoMo TV will run on Lomotif and plans to launch on linear TV. LoMo TV featured social media personality Matt Peterson (mattpeterson_), and hundreds of popular digital influencers backstage at EDC giving viewers exclusive interviews and content throughout the weekend.

LoMo TV is Lomotif's most recent brand extension, following the launch of LoMo Records, the L.A. based independent immersive record label. Continuing its mission to empower independent artists and creators, LoMo Records is partnering with artists and creators to provide expert label services and global distribution to breaking new talent from the Lomotif platform and work with other music labels. LoMo Records leverages ZASH and Lomotif's entire ecosystem including significantly expanding the artists global reach through their social media accelerator platform under AdRizer. LoMo Records bridges the gap between the traditional music standards and the emerging creator economy.

Lomotif is a popular video-sharing social networking platform. One of the fastest growing video-sharing social networking platforms in its category over the last three years, there are currently 225+ million installations of the Lomotif app globally in 200+ countries in 300+ languages. Over 300 million videos are watched on the platform per month and over 10 billion atomic clips (User Generated Content (UGC)) have been used to create more than 740+ million videos on the platform since its launch. To watch EDC 2021 on the Lomotif platform, users can download the app on the Apple Storeor Google Play for Android.

About Lomotif

Lomotif is a video-sharing social networking platform that is democratizing video creation. Since the company was co-founded by video enthusiastPaul Yangin 2014, Lomotif has been granted three technology patents focused on empowering creators to share and watch short videos with ease through remix and collaboration. Yang's vision is to build the world's largest video vocabulary to accelerate the world's transition to video-first expression. Lomotif, available in the Apple and Google stores, is a downloadable app that has grown worldwide as a grassroots social community with dedicated users spanning from Asia to South America to the U.S.For additional information about Lomotif, please visit Lomotif's website atwww.lomotif.com.

About Vinco Ventures, Inc.

Vinco Ventures, Inc. (BBIG) ("Vinco") is focused on the development of digital media andcontent technologies. Vinco's consolidated subsidiary, ZVV Media Partners, LLC, a joint venture of Vinco and Zash Global Media and Entertainment Corp. ("Zash"), has an 80% ownership interest in Lomotif Private Limited. Vinco and Zash have announced their plan to complete a merger transaction. For more information visitinvestors.vincoventures.com.

Forward-Looking Statements and Disclaimers This press release contains "forward-looking statements" as defined in the safe harbor provisions of the U.S. Private Securities Litigation Reform Act of 1995, which are based upon beliefs of, and information currently available to, Vinco's management as well as estimates and assumptions made by Vinco's management. These statements can be identified by the fact that they do not relate strictly to historic or current facts. When used in this presentation the words "estimate," "expect," "intend," "believe," "plan," "anticipate," "projected," and other words or the negative of these terms and similar expressions as they relate to the applicable company or its management identify forward-looking statements. Such statements reflect the current view of Vinco with respect to future events and are subject to risks, uncertainties, assumptions and other factors relating to Lomotif and ZASH's industry, operations and results of operations. Such factors include, but are not limited to, uncertainties as to the completion and timing of the merger between Vinco and ZASH and Vinco's expected acquisition of AdRizer, the expected financial benefits of Lomotif's acquisition of and partnership with AdRizer, the expected financial benefits of Lomotif's participation in and partnership with live entertainment events such as EDC, and such other risks and uncertainties described more fully in documents filed by Vinco with or furnished to the Securities and Exchange Commission, including the risk factors discussed in Vinco's Annual Report on Form 10-K for the period ended December 31, 2020 filed on April 15, 2021, and Vinco's Quarterly Reports on Form 10-Q filed thereafter, which are available atwww.sec.gov. Should one or more of these risks or uncertainties materialize, or the underlying assumptions prove incorrect, actual results may differ significantly from those anticipated, believed, estimated, expected, intended, or planned. Although each company believes that the expectations reflected in the forward-looking statements are reasonable, the companies cannot guarantee future results, performance, or achievements. Except as required by applicable law, including the securities laws ofthe United States, neither company intends to update any of the forward-looking statements to conform these statements to actual results.

SOURCE Lomotif

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Lomotif and ZASH Beta Test Social Media Reach and Frequency Tool in Partnership with EDC Las Vegas, Exceeding Expectations - PRNewswire

The Off-Facebook Activity tool addresses privacy concerns.

If you haven't been using theOff-Facebook Activityprivacy feature, now's the time to start. The tool, introduced by Facebook in 2019, lets you see and control data that apps and websites share with the platform -- and monitor the kind of information third-party apps can access.

With the privacy feature, you can clear the history of apps and websites that have shared your data. You can also toggle off Future Off-Facebook Activity, which tells Facebook to disconnect any information the company has shared from your account. Or you can selectively choose which companies you want to stop sharing your activity, and it'll stop showing those targeted ads.

Here's how to get a better grip on your Facebook privacy.

The Facebook tool allows you to control which sites share your information.

Using Facebook's business tools, you can see what information apps and websites have sent to the company. From there, you can clear the information from your account and turn off future "off-Facebook activity" tracking from your account. You'll be able to control this for all apps and websites so they'll no longer be able to share your search activity with Facebook.

To get started, go toSettings & Privacy > Settings >Your Facebook Information > Off-Facebook Activity. From there, you can manage your Off-Facebook Activity, clear all history and turn off Future Off-Facebook Activity for your account.

Once you clear the activity managed by the tool, Facebook will remove your identifying information that the apps and websites share. That means Facebook won't know which websites you visited or what you looked at, so you won't see targeted ads from those sites.

Turn off your activity.

If you'd like to control which ads you see (or don't) on Facebook, go to your Settings on your phone or desktop and select Ad Preferences.

Under Advertisers and Businesses, you can see which advertisers have run ads using a list uploaded to Facebook containing your information. If you select a company and chooseDon't Allow, you won't see ads from advertisers when they use a list from that company.

Learn smart gadget and internet tips and tricks with our entertaining and ingenious how-tos.

You can also go to Ad Settings and turn off ads based on data from partners, ads based on your activity on Facebook Company Products that you see elsewhere and ads that include your social media actions. However, doing this won't delete any data and you'll still see the same number of ads as before. The Off-Facebook Activity feature is the best way to remove your data.

If you're an iPhone user, a feature introduced in iOS 14.5 called App Tracking Transparency requires you to give permission to apps including Facebook before they can use your data for targeted ads. (Here's how to use App Tracking Transparency in iOS 14.5.)

Want to know how to further control your privacy online? Here's how to find and delete your Google data now and thebrowser privacy settings you should change immediately. Plus, what digital security experts wish you'd do to protect your phone app privacy.

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Take control of your privacy online with the Off-Facebook Activity tool - CNET

RIO DE JANEIRO Shortly after President Donald J. Trump was banned from Twitter early this year, Brazils like-minded leader made a plea to his millions of followers on the site.

Sign up for my official channel on Telegram, President Jair Bolsonaro requested.

Since then, Telegram, an encrypted messaging and social media platform run by an elusive Russian exile, has racked up tens of millions of new users in Brazil.

Its growing popularity in Brazil and elsewhere is being fueled by conservative politicians and commentators for whom it has become the most permissive disseminator of problematic content including disinformation in a social media ecosystem facing mounting pressure to combat fake news and polarization.

While WhatsApp remains by far the dominant messaging platform in Brazil, Telegram is making inroads fast. By August, it had been installed in 53 percent of all smartphones in Brazil, up from 15 percent two years earlier, according to a report.

Founded in 2013, Telegram has become a tool coveted by activists, dissidents and politicians many in repressive nations like Iran and Cuba to communicate privately.

But Brazilian government officials and experts worry the app could become a powerful vector for lies and vitriol before next years presidential elections a tense political moment in the country.

Mr. Bolsonaro, his re-election prospects endangered by his diminishing popularity, has followed the Trump playbook and begun sowing doubts about the integrity of Brazils voting system, raising the possibility of a disputed outcome. His unfounded claim that electronic voting machines will be rigged has unnerved the opposition and the countrys top judges, who say the abundance of disinformation in Brazilian politics is doing lasting damage to its democracy.

We know that systemic disinformation is produced by structures that are very well organized and financed, said Aline Osrio, a secretary general at Brazils electoral court who heads its program against misinformation.

Ms. Osrio said the court had established constructive working relationships with executives from other social media companies that have become vehicles for misinformation campaigns. But its efforts to reach Telegram, which is based in Dubai, have been unsuccessful.

Telegram has no representatives in Brazil, and this has made it difficult to establish a partnership in the same way weve done with other platforms, she said.

Telegram did not respond to a request for an interview. Press queries are submitted through a bot on the platform.

Experts say political content and conversations have migrated substantially to Telegram in recent years in Brazil and other countries, largely because of the apps capacity to mass-reproduce content.

Group chats can include up to 200,000 users, exponentially more than WhatsApps limit of 256. WhatsApp curbed users ability to forward messages after coming under criticism in Brazil and elsewhere for the role it played in misinformation campaigns during recent elections.

In addition to group chats, Telegram hosts channels, a one-way mass-communication tool used by corporations, artists and politicians to distribute messages, videos and audio files. Mr. Bolsonaros channel surpassed one million followers in recent weeks, putting him among the worlds most followed politicians on the platform.

While rival apps have adopted stricter and more clearly defined policies on abuse and disinformation, Telegrams guidelines are vague, and the service takes a hands-off approach to content in individual and group chats.

That makes it a safe space for incendiary figures, including politicians, who have been banned from other platforms. In Brazil, the Twitter and Instagram accounts of a lawmaker, Daniel Silveira, and a conservative journalist, Allan dos Santos, were suspended as part of a Supreme Court investigation into disinformation campaigns that included threats against justices.

But Telegram remains a portal to their followers. That has enabled Mr. dos Santos to raise funds for his legal defense and call the justice who got him banned from other sites a psychopath.

The network is clearly benefiting from the removal of users from other platforms, Fabrcio Benevenuto, a computer science professor at the Federal University of Minas Gerais, said of Telegram. Politicians have noticed it makes no effort to remove accounts, so it is becoming an appealing network for more radical groups.

Farzaneh Badiei, an internet governance expert who published a paper on Telegram at Yale Law School this year, said that Telegrams founder, Pavel Durov, had been unwilling to meaningfully grapple with the problem of disinformation that goes viral.

Their approach is very disorganized and very opaque, she said. We dont see a systemic approach to solving these problems.

Mr. Durov left Russia in 2014 after battling government efforts to censor content on the social networking site he founded, VKontakte. He has said he designed Telegram as an ultra private means of communicating based on the persecution he says he endured in his native country.

Twitter, Facebook, WhatsApp and YouTube played critical roles in Mr. Bolsonaros stunning victory in 2018, and the far-right leader has continued to rely heavily on social media to energize his base, attack opponents and make false claims that go largely unchallenged.

But in recent months, the platforms that enabled Mr. Bolsonaros rise have reined him in over his false or misleading claims about measures to contain the coronavirus. Social media companies put him on notice by taking down a handful of videos and tweets that they deemed dangerous.

Mr. Bolsonaro and his followers have railed against those removals as forms of censorship. In September, he argued that disinformation was now a permanent feature of politics and dismissed it as a trivial issue.

Fake news is part of our life, he said. Who has never told a little lie to their girlfriend?

Telegram has drawn critical scrutiny in Brazil for more than its disruptive role in politics. Investigations by news organizations found that it was hosting illegal arms networks and enabling the distribution of child pornography.

Brazilian lawmakers are debating legislation that would require platforms like Telegram to have legal representation in Brazil or risk being banned. However, users have easily circumvented such bans in countries like Iran and Russia by using software that lets them disguise their location.

Diogo Rais, a professor at Mackenzie University in So Paulo and a co-founder of the Digital Freedom Institute, called blocking apps a drastic measure that would be ineffective.

We need to deal with digital challenges realizing that our laws are from 2009 and limited to our physical territory, he said. The digital world has no such limit. This is a global challenge.

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Brazils Far-Right Disinformation Pushers Find a Safe Space on Telegram - The New York Times

A man demonstrates the uSens Inc. Impression Pi virtual reality and augmented reality interactive device at CES Unveiled, a media preview event for CES International, Monday, Jan. 4, 2016, in Las Vegas.

John Locher | AP

The so-called metaverse has a "big time" investment case, according to Puerto Rican billionaire businessman Orlando Bravo.

Bravo, co-founder and managing partner of private equity firm Thoma Bravo, told CNBC that he thinks "metaverse" is the big word of 2021.

"It's investable and it's going to be very big," Bravo said in an interview with CNBC's Annette Weisbach on Friday.

The metaverse is a sci-fi concept whereby humans put on some sort of headset or smart glasses that allows them to live, work and play in a virtual world much like the one depicted in the "Ready Player One" novel and movie. Depending on your point of view, it's either a utopian dream or a dystopian nightmare.

The term metaverse was thrust into the spotlight last month by Facebook co-founder Mark Zuckerberg when he changed Facebook's name to Meta and said the new company was going to focus on the metaverse.

"The metaverse is the next frontier just like social networking was when we got started," he said at the time.

The announcement was mocked in avideo published last week by Inspired by Iceland, a marketing campaign for Icelandic tourism. In the video, a Zuckerberg lookalike introduces viewers to "Icelandverse," a place of "enhanced actual reality without silly-looking headsets."

Dozens of other companies including Microsoft, Roblox, Nvidia and Britain's Improbable are already trying to build the software and hardware that could power the metaverse.

Thoma Bravo has more than $83 billion in assets under management and a portfolio that comprises more than 40 software companies. It has invested in the likes of cybersecurity firms McAfee and Barracuda, as well as enterprise software firm Dynatrace.

In addition to the metaverse, Bravo is also bullish on crypto and he owns an undisclosed amount of bitcoin.

"How could you not love crypto?" Bravo said at CNBC's Delivering Alpha conference in September. "Crypto is just a great system. It's frictionless. It's decentralized. And young people want their own financial system. So it is here to stay."

Correction: Facebook co-founder Mark Zuckerberg announced in October he changed Facebook's name to Meta. An earlier version misstated the timing.

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The metaverse is investable and it's going to be big, says tech billionaire - CNBC

We now present the results indicating the effects of social networks on clinicians revisions to their diagnostic assessments and their treatment recommendations. In the following analyses, diagnostic accuracy is defined as the absolute number of percentage points between a clinicians diagnostic assessment and the most accurate diagnostic assessment. For clarity of presentation, we normalize diagnostic accuracy on a 01 scale by applying min-max normalization to the absolute error of clinicians diagnostic assessments. Under this procedure, the minimum possible accuracy (indicated by 0) corresponds to the diagnostic assessment with the greatest absolute error (i.e. an estimate that is as far as possible from the most accurate answer of 16%, which in this case is 84 percentage points), while the maximum possible accuracy (indicated by 1) corresponds to a diagnostic assessment that is 0 percentage points away from the most accurate answer, such that they are equivalent (SI, Statistical Analyses). As above, in the discussion of our results we refer to the patient-actors in the standardized patient videos as patients.

Clinicians initial assessments and treatment recommendations were made independently. Figure1 shows that for the initial responses of all clinicians in the study, there were no significant differences in the accuracy of the diagnostic assessments (Fig.1a, b) given to the Black female patient and the white male patient (p>0.5, n=28, Wilcoxon Rank Sum Test, Two-sided); nor were there any significant differences in the accuracy of initial diagnostic assessments when controlling for experimental condition using a regression approach (=1.06, CI=[3.79 to 5.92], p=0.67, Supplementary Table6). However, consistent with previous studies of bias in medical care2,3,4,5,6, despite clinicians providing both patients with similar diagnostic assessments, clinicians treatment recommendations varied significantly between patients. Across all clinicians, their initial treatment recommendations (Fig.1c, d) show a significant disparity in the rate at which the guideline-recommended treatment was recommended for the white male patient versus the Black female patient. Overall, clinicians recommended Option C, referral to the emergency department for immediate evaluation, for the white male patient in 22% of responses, while only making this recommendation for the Black female patient in 14% of responses (p=0.02, n=28 observations, Wilcoxon Rank Sum Test, Two-sided).

Panels a and b show the change (from the initial assessment to the final assessment) in the average diagnostic accuracy of clinicians. Panel a shows the control conditions. Panel b shows the network conditions. The insets in both panels show the total improvement (in percentage points) in the accuracy of clinicians diagnostic assessments. Error bars display 95% confidence intervals; data points display the mean change for each of the trials (N=7) in each condition. Panels c and d show the change (from the initial recommendation to the final recommendation) in the proportion of clinicians recommending the guideline-recommended treatment recommendationreferral to the emergency department for immediate cardiac evaluation (Option C)for the white male patient-actor and Black female patient-actor. Panel c shows the control conditions. Panel d shows the network conditions. The insets in both panels show the total improvement (in percentage points) in the percent of clinicians recommending the guideline-recommended treatment. Error bars display 95% confidence intervals; data points display the mean change for each of the trials (N=7) in each condition. Panels e and f show the change (from the initial response to the final response) in the odds of clinicians recommending option A (unsafe undertreatment) rather than option C (highest quality, guideline-recommended treatment) for each patient-actor. Panel e shows the control conditions. Panel f shows the network conditions. The insets in both panels show the total reduction in the likelihood that clinicians would recommend unsafe undertreatment rather than the guideline-recommended treatment for each patient-actor. Error bars display 95% confidence intervals; data points display the mean change for each of the trials (N=7) in each condition.

In the control conditions (Fig.1a), after two rounds of revision there was no significant change in the accuracy of clinicians assessments (i.e. diagnostic estimates) for either the white male patient (p>0.9, n=7, Fig.1a inset, Wilcoxon Signed Rank Test, Two-sided) or the Black female patient (p>0.9, n=7, Fig.1a inset, Wilcoxon Signed Rank Test, Two-sided). Correspondingly, Fig.1c shows that in the control conditions there was no significant change in the rate at which clinicians recommend the guideline-recommend treatment for either the Black female patient or the white male patient (Black female patient showed a 3 percentage point increase, p=0.81, n=7 observations, Wilcoxon Signed Rank Test, Two-sided; white male patient showed a 1 percentage point increase, p=0.93, n=7 observations, Wilcoxon Signed Rank Test, Two-sided; Fig.1c). Clinicians final treatment recommendations in the control conditions still showed a significant disparity between the white male patient and the Black female patient in their rates of referral to the emergency department (p=0.04, n=14 observations, Wilcoxon Signed Rank Test, Two-sided; Fig.1c).

Figure1b shows that in the network conditions there were significant improvements (from the initial response to the final response) in the accuracy of the assessments given to both the white male patient (p=0.04, n=7, Wilcoxon Signed Rank Test, Two-sided; Fig.1b inset) and the Black female patient (p=0.01, n=7 observations, Wilcoxon Signed Rank Test, Two-sided; Fig.1b inset). Figure1d shows that in the network conditions, after two rounds of revision there was no significant change in the rate at which clinicians recommended the guideline-recommended treatment for the white male patient (p=0.57, n=7 observations, Wilcoxon Signed Rank Test, Two-sided; Fig.1d inset). This lack of change is due to the fact that, regardless of the accuracy of their initial assessments for the white male patient, clinicians were initially significantly more likely to recommend the guideline-recommended treatment for white male patient (p<0.01, OR=1.78, CI=[1.22.6], Supplementary Table7). Consequently, improvements in assessment accuracy for the white male patient had a smaller positive impact on increasing clinicians likelihood of recommending the guideline-recommended treatment. By contrast, clinicians initially were significantly less likely to recommend the guideline-recommended treatment for the Black female patient (p<0.01, OR=0.56, CI=[0.380.83], Supplementary Table7), while they were significantly more likely to recommend unsafe undertreatment for this patient (p<0.05, OR=1.5, CI=[1.082.04], Supplementary Table8). Consequently, improvements in assessment accuracy had a substantially greater effect on the final treatment recommendations for the Black female patient (Fig.1d). In the network condition, the rate at which clinicians recommended guideline-recommended treatment for the Black female patient increased significantly, from 14% in initial response to 27% in final response (p<0.01, n=7 observations, Wilcoxon Signed Rank Test, Two-sided; Fig.1d). As a result, clinicians final treatment recommendations in the network conditions exhibited no significant disparity between the Black female patient and the white male patient in terms of referral rates to the emergency department (p=0.22, n=14 observations, Wilcoxon Rank Sum Test, Two-sided; See Supplementary Table11).

The primary pathway for bias reduction in the network condition was the effect of improvements in clinicians assessment accuracy on reducing the initially high rates at which unsafe undertreatment was recommended for the Black female patient. Figure1e, f shows the odds of clinicians recommending unsafe undertreatment rather than the guideline-recommended treatment for both patients in both conditions. Consistent with the above discussion, treatment recommendations for the white male patient did not exhibit any bias toward unsafe undertreatment (p=0.19, n=14, Wilcoxon Signed Rank Test, Two-sided). As expected, improvements in assessment accuracy in the network condition did not significantly impact clinicians odds of recommending the guideline-recommended treatment rather than unsafe undertreatment for the white male patient (p=0.21, n=7, Wilcoxon Signed Rank Test, Two-sided). By contrast, clinicians initially had significantly greater odds of recommending unsafe undertreatment rather than the guideline-recommended treatment for the Black female patient (Fig.1e, f; p<0.01, n=28 observations, Wilcoxon Signed Rank Test, Two-sided). Independent revision in the control conditions did not have any impact on the treatment recommendations for either the white male (p=1.0, n=7, Wilcoxon Signed Rank Test, Two-sided) or the Black female patient (p=0.81, n=7, Wilcoxon Signed Rank Test, Two-sided). However, assessment revisions in the network condition led to a significant change in the odds of clinicians recommending the guideline-recommended treatment rather than unsafe undertreatment for the Black female patient (Fig.1fp=0.01, n=7, Wilcoxon Signed Rank Test, Two-sided). By the final round in the network conditions, there was no significant difference between patients in their odds of having clinicians recommend the guideline-recommended treatment rather than unsafe undertreatment (Fig.1f, p=0.19, n=14, Wilcoxon Rank Sum Test, Two-sided).

The network mechanism responsible for improvements in the accuracy of clinicians assessments, and the corresponding reduction of race and gender disparity in their treatment recommendations, is the disproportionate impact of accurate individuals in the process of belief revision within egalitarian social networks13,15,16. As demonstrated in earlier studies of networked collective intelligence13,15,16, during the process of belief revision in peer networks there is an expected correlation between the accuracy of an individuals beliefs and the magnitude of their belief revisions, such that accurate individuals revise their responses less; this correlation between accuracy and revision magnitude is referred to as the revision coefficient13. Within egalitarian social networks, a positive revision coefficient has been found to give greater de facto social influence to more accurate individuals, which is predicted to produce network-wide improvements in the accuracy of individual beliefs within the social network. These improvements in collective accuracy have been found to result in a corresponding reduction in biased responses among initially biased participants12,13,15,16. Figure2a tests this prediction for clinicians in our study. The results show, as expected, that there is a significant positive revision coefficient among clinicians in the network conditions (p<0.001, r=0.66, SE=0.1, clustered by trial, Supplementary Table14), indicating that less accurate clinicians made greater revisions to their responses while more accurate clinicians made smaller revisions, giving greater de facto influence in the social network to more accurate clinicians. This correlation holds equally for clinicians assessments for both the white male and Black female patients (Supplementary Table14). Figure2b shows that for both patients, improvements in assessment accuracy led to significant improvements in the quality of their treatment recommendations (p<0.05, OR=1.04, CI=[1.00, 1.09], Supplementary Table9). Importantly, for clinicians who initially recommended unsafe undertreatment (Option A), we find that improvements in assessment accuracy significantly predict an increased likelihood of recommending the guideline-recommended treatment (Option C) by the final round (p<0.01, OR=1.17, CI=[1.03, 1.33], Supplementary Table10). These improvements translated into a significant reduction in the inequity of recommended care for the Black female patient, for whom clinicians were initially significantly more likely to recommend unsafe undertreatment (see Fig.3, below).

Panel a shows clinicians propensity to revise their diagnostic assessments in the network conditions according to the initial error in their diagnostic assessments. Clinicians accuracy is represented as the absolute number of percentage points of a given assessment from the most accurate assessment of 16% (represented by 0 along the x-axis, indicating a distance of 0 percentage points from the most accurate response). Magnitude of revision is measured as the absolute difference (percentage points) between a clinicians initial diagnostic assessment and their final diagnostic assessment. Clinicians accuracy in their initial assessment significantly predicts the magnitude of their revisions between the initial to final response. Grey error band displays 95% confidence intervals for the fit of an OLS model regressing initial error of diagnostic assessment on magnitude of revision. Panel b shows the significant positive relationship between the improvement in clinicians diagnostic accuracy (from the initial to final assessment), and their likelihood of improving in their treatment recommendation (i.e. the probability of switching from recommending Option A, B, or D to Option C) for clinicians in the network conditions. The trend line shows the estimated probability of clinicians improving their treatment recommendations according to a logistic regression, controlling for an interaction between experimental condition (control or network) and patient-actor demographic (Black female or white male) (Supplementary Table9). Error bars show standard errors clustered at the trial level.

Each panel shows the fraction of clinicians providing each treatment recommendation at the initial and final response, averaged first within each of the trials in each condition (N=7), and then averaged across trials. Option A. 1 week follow-up (unsafe undertreatment). Option B. Stress test in 23 days (undertreatment). Option C. Immediate emergency department evaluation (guideline-recommended treatment). Option D. Immediate cardiac catheterization (overtreatment Panel a shows the change in control condition recommendations for the Black female patient-actor (initial recommendations light pink, final recommendations dark pink). Panel b shows the change in network condition recommendations for the Black female patient-actor (initial recommendations light pink, final recommendations dark pink). Panel c shows the change in control condition recommendations for the white male patient-actor (initial recommendations light blue, final recommendations dark blue). Panel d shows the change in network condition recommendations for the white male patient-actor (initial recommendations light blue, final recommendations dark blue).

Figure3 shows the changing rates at which clinicians recommended each option (Option A. unsafe undertreatment, Option B. undertreatment, Option C. guideline-recommended treatment, and Option D. overtreatment) for each patient, from the initial response to the final response, for all conditions. As discussed above, we are particularly interested in the inequity of patient care, defined as the rate at which clinicians made a clearly unsafe recommendation (Option A) versus recommending the guideline-recommended treatment (Option C)23,24. Initial responses exhibited significant inequity between patients. Initially, across both conditions, 29.9% of clinicians recommended the unsafe undertreatment for the Black female patient, while only 14.1% recommended the guideline-recommended treatment, resulting in a 15.7 percentage point difference in the rate at which clinicians recommended unsafe undertreatment rather than the guideline-recommended treatment for the Black female patient. By contrast, for the white male patient, 23.4% of clinicians recommended the unsafe undertreatment, while 21.4% of clinicians recommended the guideline-recommended treatment, resulting in a 2 percentage point difference in the likelihood of clinicians recommending unsafe undertreatment rather than the guideline-recommended treatment for the white male patient. This resulted in a 13.7 percentage point difference between the Black female patient and the white male patient in their likelihood of having clinicians recommend unsafe undertreatment rather than the guideline-recommended treatment (p=0.02, n=28 observations, Wilcoxon Rank Sum Test, Two-sided). Individual reflection did not reduce this inequity. The control conditions produced no significant change in the inequity between patients from the initial response to the final response (p=0.57, n=14 observations, Wilcoxon Signed Rank Test, Two-sided). Accordingly, in the final response in the control conditions, there was a 15.3 percentage point difference between the Black female patient and the white male patient in their likelihood of having the clinician recommend unsafe undertreatment rather than the guideline-recommended treatment (p=0.04, n=14 observations, Wilcoxon Rank Sum Test, Two-sided; see SI Eq. 2). Strikingly, however, improvements in diagnostic accuracy in the network condition produced a 20 percentage point reduction in the rate at which clinicians recommended unsafe undertreatment rather than the guideline-recommended treatment the Black female patient (p=0.04, n=14 observations, Wilcoxon Rank Sum Test, Two-sided). By the final response in the network conditions, inequity was eliminatedthe Black female patient was no longer more likely than the white male patient to have clinicians recommend unsafe undertreatment rather than the guideline-recommended treatment (p=0.16, n=14 observations, Wilcoxon Rank Sum Test, Two-sided).

Figure3 (panels ad) also shows that the network conditions improved the quality of clinical care recommended for both patients (white male and Black female). In particular, for both the Black female and white male patient, the network conditions produced significantly greater reductions in the proportion of clinicians recommending unsafe undertreatment (Option A) than the control conditions (1.6 percentage point reduction in the control conditions, 11.8 percentage point reduction in the network conditions; p<0.01, n=28 observations, Wilcoxon Signed Rank Test, Two-sided). This reduction in the recommendation of unsafe undertreatment (Option A) was associated with significant increases in recommendations for safer care for both patients. While Option B was not the guideline-recommended treatment, it represents a safer treatment than Option A. Correspondingly, the network conditions significantly increased the proportion of clinicians recommending safer undertreatment (Option B) than the control conditions (3.5 percentage point reduction in control conditions, +6.5 percentage point increase in the network conditions; p=0.03, n=28 observations, Wilcoxon Signed Rank Test, Two-sided). Strikingly, the rate of overtreatment (i.e. Option D, unnecessary invasive procedure) for both patients was significantly decreased in the network conditions, while it increased in the control conditions (2.8 percentage point reduction in the network conditions, +3.1 percentage point increase in the control conditions; p<0.01, n=28 observations, Wilcoxon Signed Rank Test, Two-sided).

These results reveal a tendency for clinicians in the control conditions to increase the acuity (i.e. urgency) of care for all patients as a result of independent reflection, leading to an increase in overtreatment. By contrast, in the network conditions, clinicians adjusted their recommendations toward safer, more equitable care for both patients, significantly reducing both unsafe undertreatment (Option A) and overtreatment (Option D). Additional sensitivity analyses show these findings to be robust to variations in clinicians characteristics26 (see SI, Sensitivity Analyses).

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The reduction of race and gender bias in clinical treatment recommendations using clinician peer networks in an experimental setting - Nature.com