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UK report spotlights the huge investment gap facing diverse founders

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New research looking into how UK VC has been invested over the past decade according to race, gender and educational background makes for grim reading — with all-ethnic teams and female entrepreneurs receiving just a fraction of available funding vs all-white teams and male founders.

The finding of baked in bias holds true across all funding stages, per the findings.

The report, by the not-for-profit community interest company Extend Ventures, looked at how VC has been invested in the UK between 2009 and 2019 — providing data on 3,784 entrepreneurs who started 2,002 companies over this period. It found that all-ethnic teams received an average of just 1.7% of the venture capital investments made at seed, early and late stage over this decade.

The UK’s Black and Multi-Ethnic communities, meanwhile, now comprise 14% of the UK population.

“While all ethnic entrepreneurs are underfunded, those who are Black experience the poorest outcomes of all,” the report notes, finding just 38 Black entrepreneurs received VC funding over this decade. “Alongside their teams, they received just 0.24% of the total sum invested,” it adds.

Extend Ventures used machine learning and computer vision technology as a tool to understand demographic factors — “including age, perceived gender, ethnicity and educational background of founding members” — relying on a perception of ethnicity or gender to categorise founders for the research, based on analysis of publicly available images of entrepreneurs.

“Despite ethnicity usually being a self-determined categorisation, we believe this is justified because the data we collect is subsequently anonymised and is being used to improve access to capital,” they note on that, adding: “Ethnic or gender prejudice is dependent on the perception of the person holding the purse strings to funds.”

On gender the research underlines the scale of the challenge UK female entrepreneurs face in accessing VC funding vs male counterparts.

The report found that a large majority (68.33%) of the capital raised across the seed, early and late VC funding stages went to all-male teams; 28.80% to mixed gender teams; and just 2.87% to all-female teams, with female teams also raising lower sums of money than their male counterparts at each funding stage.

The picture is starkest for Black female entrepreneurs in the UK who were found to experience the poorest outcomes.

“A total of 10 female entrepreneurs of Black appearance received venture capital investment (0.02% of the total amount invested) across the 10-year period, with none so far receiving late-stage funding,” the report notes.

It also found just one early stage (Series A or B) venture capital investment recorded for a Black female, compared to 194 early stage investments in White female entrepreneurs.

Extend Ventures’ research also looked at educational background — spotlighting the role of elite universities in the distribution of venture capital in the country.

Here the report found that 42.72% of UK VC invested at seed stage during the period was invested in founding teams with at least one member from an elite educational background (narrowly defined to mean Oxford, Cambridge, Harvard, Stanford and their respective business schools).

In the UK, the debate about how to widen access for underrepresented students to the country’s top two universities has been raging for years — with progress towards diversification of the Oxbridge student body still hard to see.

The report illustrates one impact of this long-standing inequality around access to the elite education — as it shows it carries through to decreased opportunity, post-university, for accessing VC funding.

The implications for social justice and social mobility are clear.

“The data we have shown today is stark and makes for uncomfortable reading,” Extend Ventures’ co-founder and technology entrepreneur, Tom Adeyoola, told TechCrunch. “Only 0.24% of venture funding over the last 10 years going to (38) Black founders, 0.02% going to Black female founders. In addition 43% of all seed funding went to teams with at least one team member who went to an elite university.”

The report makes a series of recommendations — including calling for all venture funds to make data on their investments publicly available so they can be tracked to enable inclusive ongoing reporting on the industry’s performance on diversity.

It also suggests VC firms need to do more work to understand and establish what it describes as “the possible resilience criteria independent of race, gender and education that are indicators of success” — to use in their filtering processes going forward, as a way to guard against biased decisions.

Another recommendation is for the UK government to create an ‘Investing in Ethnic Founders Code’, mirroring the existing Investing in Women Code.

The report also calls for government to support inclusion via the creation of a Diverse Co-Investment Fund — which it suggests should be set at £1.8BN (14% of the $13.2BN annual UK VC total) — as a strategy to de-risk and improve the deployment of equity investment into Black, Asian and Ethnic-led venture capital funds.

We’ve reached out to the Treasury for comment on the recommendations.

“There is no longer any excuse for transparency and action to overcome clear biases,” said Adeyoola. “You can’t improve what you don’t measure and for all the talk around the Rose Review [UK Treasury-commissioned report into female entrepreneurship] and Black Lives Matter, action needs to translate into real investment into diverse founders to ensure that as a nation we are making the most of the diverse talent and resources we have.”

“The British Business Bank report released last week has already shown that there is no lack of ambition — just, as we now lay bare, a clear lack of financial capital,” he added.

Tweeting in support of the report, ex-Dragons Den investor and black businessman, Piers Linney, wrote: “We are leaving tens of billions on the table that would benefit the wealth of every citizen. We now have undeniable and depressing data showing that something is very wrong. Quietly filing these reports away is unacceptable.”

Reached for a response, UK founder network organization Tech Nation, which is credited with supporting the research, told us: “The Extend VC report highlights that just 12% of funding went to female founders, which is why Tech Nation is proactively working with Playfair Capital to provide office hours for female founders with leading VCs on November 5 and 12.

“Today’s report also showed that 91.5% of seed stage funding went to white founders compared to 1.1% to black founders, so Tech Nation has also partnered with 10×10 VC and Founders Factory to host black founder office hours on November 26,” CEO Gerard Grech also said, adding that the organization “will continue to support research when it comes to increasing inclusivity in tech and support I&D programmes and interventions which will make a real and positive difference”.

Passion Capital partner Eileen Burbidge — a female VC who, in 2018, was named on a list of the UK’s top 100 black and ethnic minority leaders by the Financial Times — also welcomed the research when we reached out.

“It’s great to see this data out there and I’m so glad that Extend Ventures, Impact X Capital Partners and Tech Nation have taken the time to collect and analyse the data,” she told TechCrunch.

“Sadly I’m not surprised by the findings and at Passion, given that one of the founding partners is of an ethnic minority group, we’ve always tried to be as inclusive as possible. But you can’t change or affect what isn’t measured, so this is a fantastic first step.”

“I’m glad this report will expand and further develop the conversation about how to make venture capital more accessible to all… across all educational backgrounds, social classes and ethnic & gender groups,” Burbidge added, saying she supports all the recommendations — “especially the ones that can have immediate action/impact” — and said she’d welcome being part of conservations aimed at making progress.

(As it happens, one of Passion Capital’s portfolio companies — the insurtech startup Marshmallow, which is led by two black twin co-founders, Oliver and Alexander Kent-Braham — has just announced a $30M fund raise on a $310M valuation for a product that also focuses on serving underserved segments of society.)

Lyron Foster is a Hawaii based African American Musician, Author, Actor, Blogger, Filmmaker, Philanthropist and Multinational Serial Tech Entrepreneur.

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Mike Cagney is testing the boundaries of the banking system for himself — and others

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Founder Mike Cagney is always pushing the envelope, and investors love him for it. Not long sexual harassment allegations prompted him to leave SoFi, the personal finance company that he cofounded in 2011, he raised $50 million for new lending startup called Figure that has since raised at least $225 million from investors and was valued a year ago at $1.2 billion.

Now, Cagney is trying to do something unprecedented with Figure, which says it uses a blockchain to more quickly facilitate home equity, mortgage refinance, and student and personal loan approvals. The company has applied for a national bank charter in the U.S., wherein it would not take FDIC-insured deposits but it could take uninsured deposits of over $250,000 from accredited investors.

Why does it matter? The approach, as American Banker explains it, would bring regulatory benefits. As it reported earlier this week, “Because Figure Bank would not hold insured deposits, it would not be subject to the FDIC’s oversight. Similarly, the absence of insured deposits would prevent oversight by the Fed under the Bank Holding Company Act. That law imposes restrictions on non-banking activities and is widely thought to be a deal-breaker for tech companies where banking would be a sidelight.”

Indeed, if approved, Figure could pave the way for a lot of fintech startups — and other retail companies that want to wheel and deal lucrative financial products without the oversight of the Federal Reserve Board or the FDIC — to nab non-traditional bank charters.

As Michelle Alt, whose year-old financial advisory firm helped Figure with its application, tells AB: “This model, if it’s approved, wouldn’t be for everyone. A lot of would-be banks want to be banks specifically to have more resilient funding sources.” But if it’s successful, she adds, “a lot of people will be interested.”

One can only guess at what the ripple effects would be, though the Bank of Amazon wouldn’t surprise anyone who follows the company.

In the meantime, the strategy would seemingly be a high-stakes, high-reward development for a smaller outfit like Figure, which could operate far more freely than banks traditionally but also without a safety net for itself or its customers. The most glaring danger would be a bank run, wherein those accredited individuals who are today willing to lend money to the platform at high interest rates began demanding their money back at the same time. (It happens.)

Either way, Cagney might find a receptive audience right now with Brian Brooks, a longtime Fannie Mae executive who served as Coinbase’s chief legal officer for two years before jumping this spring to the Office of the Comptroller of the Currency (OCC), an agency that ensures that national banks and federal savings associations operate in a safe and sound manner.

Brooks was made acting head of the agency in May and green-lit one of the first national charters to go to a fintech, Varo Money, this past summer. In late October, the OCC also granted SoFi preliminary, conditional approval over its own application for a national bank charter.

While Brooks isn’t commenting on speculation around Figure’s application, in July, during a Brookings Institution event, he reportedly commented about trade groups’ concerns over his efforts to grant fintechs and payments companies charters, saying: “I think the misunderstanding that some of these trade groups are operating under is that somehow this is going to trigger a lighter-touch charter with fewer obligations, and it’s going to make the playing field un-level . . . I think it’s just the opposite.”

Christopher Cole, executive vice president at the trade group Independent Community Bankers of America, doesn’t seem persuaded. Earlier this week, he expressed concern about Figure’s bank charter application to AB, saying he suspects that Brooks “wants to approve this quickly before he leaves office.”

Brooks’s days are surely numbered. Last month, he was nominated by President Donald to a full five-year term leading the federal bank regulator and is currently awaiting Senate confirmation. The move — designed to slow down the incoming Biden administration — could be undone by President-elect Joe Biden, who can fire the comptroller of the currency at will and appoint an acting replacement to serve until his nominee is confirmed by the Senate.

Still, Cole’s suggestion is that Brooks still has enough time to figure out a path forward for Figure — and if its novel charter application is approved, and it stands up to legal challenges — a lot of other companies, too.

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We read the paper that forced Timnit Gebru out of Google. Here’s what it says

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On the evening of Wednesday, December 2, Timnit Gebru, the co-lead of Google’s ethical AI team, announced via Twitter that the company had forced her out. 

Gebru, a widely respected leader in AI ethics research, is known for coauthoring a groundbreaking paper that showed facial recognition to be less accurate at identifying women and people of color, which means its use can end up discriminating against them. She also cofounded the Black in AI affinity group, and champions diversity in the tech industry. The team she helped build at Google is one of the most diverse in AI, and includes many leading experts in their own right. Peers in the field envied it for producing critical work that often challenged mainstream AI practices.

A series of tweets, leaked emails, and media articles showed that Gebru’s exit was the culmination of a conflict over another paper she co-authored. Jeff Dean, the head of Google AI, told colleagues in an internal email (which he has since put online) that the paper “didn’t meet our bar for publication” and that Gebru had said she would resign unless Google met a number of conditions, which it was unwilling to meet. Gebru tweeted that she had asked to negotiate “a last date” for her employment after she got back from vacation. She was cut off from her corporate email account before her return.

Online, many other leaders in the field of AI ethics are arguing that the company pushed her out because of the inconvenient truths that she was uncovering about a core line of its research—and perhaps its bottom line. More than 1,400 Google staff and 1,900 other supporters have also signed a letter of protest.

Many details of the exact sequence of events that led up to Gebru’s departure are not yet clear; both she and Google have declined to comment beyond their posts on social media. But MIT Technology Review obtained a copy of the research paper from  one of the co-authors, Emily M. Bender, a professor of computational linguistics at the University of Washington. Though Bender asked us not to publish the paper itself because the authors didn’t want such an early draft circulating online, it gives some insight into the questions Gebru and her colleagues were raising about AI that might be causing Google concern.

Titled “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” the paper lays out the risks of large language models—AIs trained on staggering amounts of text data. These have grown increasingly popular—and increasingly large—in the last three years. They are now extraordinarily good, under the right conditions, at producing what looks like convincing, meaningful new text—and sometimes at estimating meaning from language. But, says the introduction to the paper, “we ask whether enough thought has been put into the potential risks associated with developing them and strategies to mitigate these risks.”

The paper

The paper, which builds off the work of other researchers, presents the history of natural-language processing, an overview of four main risks of large language models, and suggestions for further research. Since the conflict with Google seems to be over the risks, we’ve focused on summarizing those here. 

Environmental and financial costs

Training large AI models consumes a lot of computer processing power, and hence a lot of electricity. Gebru and her coauthors refer to a 2019 paper from Emma Strubell and her collaborators on the carbon emissions and financial costs of large language models. It found that their energy consumption and carbon footprint have been exploding since 2017, as models have been fed more and more data.

Strubell’s study found that one language model with a particular type of “neural architecture search” (NAS) method would have produced the equivalent of 626,155 pounds (284 metric tons) of carbon dioxide—about the lifetime output of five average American cars. A version of Google’s language model, BERT, which underpins the company’s search engine, produced 1,438 pounds of CO2 equivalent in Strubell’s estimate—nearly the same as a roundtrip flight between New York City and San Francisco.

Gebru’s draft paper points out that the sheer resources required to build and sustain such large AI models means they tend to benefit wealthy organizations, while climate change hits marginalized communities hardest. “It is past time for researchers to prioritize energy efficiency and cost to reduce negative environmental impact and inequitable access to resources,” they write.

Massive data, inscrutable models

Large language models are also trained on exponentially increasing amounts of text. This means researchers have sought to collect all the data they can from the internet, so there’s a risk that racist, sexist, and otherwise abusive language ends up in the training data.

An AI model taught to view racist language as normal is obviously bad. The researchers, though, point out a couple of more subtle problems. One is that shifts in language play an important role in social change; the MeToo and Black Lives Matter movements, for example, have tried to establish a new anti-sexist and anti-racist vocabulary. An AI model trained on vast swaths of the internet won’t be attuned to the nuances of this vocabulary and won’t produce or interpret language in line with these new cultural norms.

It will also fail to capture the language and the norms of countries and peoples that have less access to the internet and thus a smaller linguistic footprint online. The result is that AI-generated language will be homogenized, reflecting the practices of the richest countries and communities.

Moreover, because the training datasets are so large, it’s hard to audit them to check for these embedded biases. “A methodology that relies on datasets too large to document is therefore inherently risky,” the researchers conclude. “While documentation allows for potential accountability, […] undocumented training data perpetuates harm without recourse.”

Research opportunity costs

The researchers summarize the third challenge as the risk of “misdirected research effort.” Though most AI researchers acknowledge that large language models don’t actually understand language and are merely excellent at manipulating it, Big Tech can make money from models that manipulate language more accurately, so it keeps investing in them. “This research effort brings with it an opportunity cost,” Gebru and her colleagues write. Not as much effort goes into working on AI models that might achieve understanding, or that achieve good results with smaller, more carefully curated datasets (and thus also use less energy).

Illusions of meaning

The final problem with large language models, the researchers say, is that because they’re so good at mimicking real human language, it’s easy to use them to fool people. There have been a few high-profile cases, such as the college student who churned out AI-generated self-help and productivity advice on a blog, which went viral.

The dangers are obvious: AI models could be used to generate misinformation about an election or the covid-19 pandemic, for instance. They can also go wrong inadvertently when used for machine translation. The researchers bring up an example: In 2017, Facebook mistranslated a Palestinian man’s post, which said “good morning” in Arabic, as “attack them” in Hebrew, leading to his arrest.

Why it matters

Gebru and Bender’s paper has six co-authors, four of whom are Google researchers. Bender asked to avoid disclosing their names for fear of repercussions. (Bender, by contrast, is a tenured professor: “I think this is underscoring the value of academic freedom,” she says.)

The paper’s goal, Bender says, was to take stock of the landscape of current research in natural-language processing. “We are working at a scale where the people building the things can’t actually get their arms around the data,” she said. “And because the upsides are so obvious, it’s particularly important to step back and ask ourselves, what are the possible downsides? … How do we get the benefits of this while mitigating the risk?”

In his internal email, Dean, the Google AI head, said one reason the paper “didn’t meet our bar” was that it “ignored too much relevant research.” Specifically, he said it didn’t mention more recent work on how to make large language models more energy-efficient and mitigate problems of bias. 

However, the six collaborators drew on a wide breadth of scholarship. The paper’s citation list, with 128 references, is notably long. “It’s the sort of work that no individual or even pair of authors can pull off,” Bender said. “It really required this collaboration.” 

The version of the paper we saw does also nod to several research efforts on reducing the size and computational costs of large language models, and on measuring the embedded bias of models. It argues, however, that these efforts have not been enough. “I’m very open to seeing what other references we ought to be including,” Bender said.

Nicolas Le Roux, a Google AI researcher in the Montreal office, later noted on Twitter that the reasoning in Dean’s email was unusual. “My submissions were always checked for disclosure of sensitive material, never for the quality of the literature review,” he said.

Dean’s email also says that Gebru and her colleagues gave Google AI only a day for an internal review of the paper before they submitted it to a conference for publication. He wrote that “our aim is to rival peer-reviewed journals in terms of the rigor and thoughtfulness in how we review research before publication.”

Bender noted that even so, the conference would still put the paper through a substantial review process: “Scholarship is always a conversation and always a work in progress,” she said. 

Others, including William Fitzgerald, a former Google PR manager, have further cast doubt on Dean’s claim: 

Google pioneered much of the foundational research that has since led to the recent explosion in large language models. Google AI was the first to invent the Transformer language model in 2017 that serves as the basis for the company’s later model BERT, and OpenAI’s GPT-2 and GPT-3. BERT, as noted above, now also powers Google search, the company’s cash cow.

Bender worries that Google’s actions could create “a chilling effect” on future AI ethics research. Many of the top experts in AI ethics work at large tech companies because that is where the money is. “That has been beneficial in many ways,” she says. “But we end up with an ecosystem that maybe has incentives that are not the very best ones for the progress of science for the world.”

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Daily Crunch: Slack and Salesforce execs explain their big acquisition

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We learn more about Slack’s future, Revolut adds new payment features and DoorDash pushes its IPO range upward. This is your Daily Crunch for December 4, 2020.

The big story: Slack and Salesforce execs explain their big acquisition

After Salesforce announced this week that it’s acquiring Slack for $27.7 billion, Ron Miller spoke to Slack CEO Stewart Butterfield and Salesforce President and COO Bret Taylor to learn more about the deal.

Butterfield claimed that Slack will remain relatively independent within Salesforce, allowing the team to “do more of what we were already doing.” He also insisted that all the talk about competing with Microsoft Teams is “overblown.”

“The challenge for us was the narrative,” Butterfield said. “They’re just good [at] PR or something that I couldn’t figure out.”

Startups, funding and venture capital

Revolut lets businesses accept online payments — With this move, the company is competing directly with Stripe, Adyen, Braintree and Checkout.com.

Health tech venture firm OTV closes new $170M fund and expands into Asia — This year, the firm led rounds in telehealth platforms TytoCare and Lemonaid Health.

Zephr raises $8M to help news publishers grow subscription revenue — The startup’s customers already include publishers like McClatchy, News Corp Australia, Dennis Publishing and PEI Media.

Advice and analysis from Extra Crunch

DoorDash amps its IPO range ahead of blockbuster IPO — The food delivery unicorn now expects to debut at $90 to $95 per share, up from a previous range of $75 to $85.

Enter new markets and embrace a distributed workforce to grow during a pandemic — Is this the right time to expand overseas?

Three ways the pandemic is transforming tech spending — All companies are digital product companies now.

(Extra Crunch is our membership program, which aims to democratize information about startups. You can sign up here.)

Everything else

WH’s AI EO is BS — Devin Coldewey is not impressed by the White House’s new executive order on artificial intelligence.

China’s internet regulator takes aim at forced data collection — China is a step closer to cracking down on unscrupulous data collection by app developers.

Gift Guide: Games on every platform to get you through the long, COVID winter — It’s a great time to be a gamer.

The Daily Crunch is TechCrunch’s roundup of our biggest and most important stories. If you’d like to get this delivered to your inbox every day at around 3pm Pacific, you can subscribe here.

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