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Lex

FT’S EXCLUSIVE AGENDA SETTING COLUMN ON BUSINESS AND FINANCE

Lex is a premium daily commentary service from the Financial Times. It is the oldest and arguably the most influential business and finance column of its kind in the world. It helps readers make better investment decisions by highlighting key emerging risks and opportunities.

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SoftBank/Vision Fund: Uberoptimistic

Group remains reliant on the mature and fiercely competitive telecoms industry

Purplebricks: red faces

Struggles in Australia and US have hit group’s credibility as a digital disrupter

Capital Markets

Wall Street grafts new technology on to old-school fundraisings

Bankers hope artificial intelligence will boost efficiency of stock and bond sales

Bankers on Wall Street have always tended to look for an edge in old-fashioned ways: a firm handshake, an expensive suit, an invitation for a client to a top sporting event.

But now technology could be about to change the traditional ways of doing things, just as it has in other old-school business lines such as the trading of stocks and bonds. The next frontier is the “primary” side of the banks’ capital markets businesses, which helps companies issue debt and equity.

Bank of America Merrill Lynch, for example, has spent time collecting and cleaning up internal information on initial public offerings it has managed, and fed the 50-gigabyte data set into a machine-learning tool it calls “Predictive Intelligence Analytics Machine”, or Priam.

Since September the bank has used Priam to help predict which investors will be interested in European listings, and it now plans to apply the tool to US and Asian deals too. “Once we’d cleaned all the data, it was unbelievable what we could do,” says Elif Bilgi Zapparoli, the bank’s co-head of global capital markets. “[Priam] is learning and improving with every deal.”

Some senior bankers are dismissive, sneering at efforts to graft artificial intelligence on to what is an essentially relationship-driven business. And even enthusiasts argue that AI will not be a game-changer, given it works best on vast sets of data with billions of observations, rather than the thousands of IPOs and bond deals that banks tend to have in their databases. Humans will always remain at the centre of most of the work, they insist.

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Nonetheless, some bankers are optimistic that the often knotty work of arranging debt and equity deals could become much faster and more efficient. Projects under the banner of “digital capital markets” are proliferating across the industry, with banks seeking computer-driven clues on which clients might want to issue, when, and at what maturity and currency. This could result in banks needing fewer people to do the work, at a time when many are still struggling to boost returns to shareholders in an age of low interest rates and tougher post-crisis regulation.

HSBC, for example, recently opened up its internal “MyDeal” app that collects and analyses every titbit of deal information to the bank’s corporate clients. This combines with third-party investment banking products such as WeConvene — which help organise investor meetings and communication — to allow the British bank to streamline every aspect of bond deals, according to Jean-Marc Mercier, global co-head of debt capital markets. He describes the new tech as “like an exoskeleton that allows me to run three times faster”.

Richard Rivero, who heads up a “strats” team within Goldman Sachs’ investment banking unit, points out some of these efforts have been under way for more than a decade. What is different now, he says, is “the volume of data you can collect from our internal processes or external sources . . . and the speed with which you can produce computations”.

He adds: “We are trying to use these techniques to understand how or when companies will come to the capital market to raise funds, to drive targeting for our bankers to help make their coverage more efficient.”

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One of the more intriguing efforts to modernise the primary capital markets business is "Project Mars”, a platform for corporate bond issuance that is backed by a consortium of banks including Citigroup, BofA and JPMorgan. It looks set to rival a similar offering from Ipreo, a technology provider, that has attracted several European banks, along with Goldman.

Details are hazy, but people familiar with Mars say it will probably be some kind of online platform that will be integrated with fund managers’ existing order management systems, allowing bond underwriters to relay legal documents, credit rating reports and pricing information to investors, while collecting both indications of interest and firm orders.

That could streamline what is often an unwieldy process. When multiple banks work on deals, say analysts, their salespeople can often end up spamming potential investors with reams of duplicative information and requests through email, Bloomberg chats and calls.

As for BofA, it hopes to use Priam for debt issuance as well as equity, and to use it to predict which covenants — contractual restrictions — have a greater effect on a borrower’s ultimate cost of credit. That could improve efforts to price deals by striking a better balance between supply and demand.

The bank has also produced Insight, a mobile app for corporate clients that can give them detailed breakdowns of market conditions, research, investor demand, and the order book for any deals they are bringing to the market in real time.

“We are transforming ourselves,” says Ms Zapparoli. “It used to be purely about relationships, now it’s about marrying that with data analytics . . . The possibilities are huge, and that’s what’s exciting.”

Special Report: AI & Robotics

EU leads on regulation while China and US forge ahead on technology

Race for innovation blunts efforts to safeguard basic rights

Lessons from Mary Poppins on ethical robots

In the nature-versus-nurture tech debate, we look to the world’s most famous nanny

Start-up makes robots small manufacturers can afford

Collaborative robots market expected to snowball to $12.3bn by 2025

China’s unchecked expansion of data-powered AI raises civic concerns

Local industry is growing fast but there are signs of a pushback

US export controls are no guarantee against China’s AI advances

Budget cuts and immigration curbs risk undermining fight for competitive edge

Japan lays bare the limitations of robots in unpredictable work

Forerunner in automation worried about progress being too slow rather than too fast

EU leads on regulation while China and US forge ahead on technology

Race for innovation blunts efforts to safeguard basic rights

Lessons from Mary Poppins on ethical robots

In the nature-versus-nurture tech debate, we look to the world’s most famous nanny

Start-up makes robots small manufacturers can afford

Collaborative robots market expected to snowball to $12.3bn by 2025

China’s unchecked expansion of data-powered AI raises civic concerns

Local industry is growing fast but there are signs of a pushback

US export controls are no guarantee against China’s AI advances

Budget cuts and immigration curbs risk undermining fight for competitive edge

Japan lays bare the limitations of robots in unpredictable work

Forerunner in automation worried about progress being too slow rather than too fast

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Artificial intelligence

How Big Tech is struggling with the ethics of AI

Companies criticised for overruling and even dissolving ethics boards

After Jack Poulson quit Google, he was ushered into a meeting with Jeff Dean, the head of the company’s artificial intelligence division.

Mr Poulson, a former Stanford professor who worked on machine intelligence for Google, had resigned in protest at “Project Dragonfly”, a plan to develop a censored search engine for China, saying the company had promised just two months earlier not to design or deploy technology that “contravenes [ . . .] human rights”.

The meeting, according to Mr Poulson, was supposed to make him “feel better about Google’s ethical red lines”.

“But what I actually found was the opposite,” he said. “The response was, ‘[human rights organisations] are just outsiders responding to public information, [and] we just disagree that that’s a violation.’ There was no respect for them on this issue.”

He left the company the next day. In recent months, other Google employees have protested at its bid for a Pentagon cloud computing contract, and its involvement in a US government AI weapons program.

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Google employees walk off the job to join a protest in New York © AP

The development and application of AI is causing huge divisions both inside and outside tech companies, and Google is not alone in struggling to find an ethical approach.

The companies that are leading research into AI in the US and China, including Google, Amazon, Microsoft, Baidu, SenseTime and Tencent, have taken very different approaches to AI and whether to develop technology that can ultimately be used for military and surveillance purposes.

For instance, Google has said it will not sell facial recognition services to governments, while Amazon and Microsoft both do so. They have also been attacked for the algorithmic bias of their programmes, where computers inadvertently propagate bias through unfair or corrupt data inputs.

In response to criticism not only from campaigners and academics but also their own staff, companies have begun to self-regulate by trying to set up their own “AI ethics” initiatives that perform roles ranging from academic research — as in the case of Google-owned DeepMind’s Ethics and Society division — to formulating guidelines and convening external oversight panels.

The efforts have led to a fragmented landscape of efforts that both supporters and critics agree have not yet had demonstrable outcomes beyond igniting a debate around the topic of AI and its social implications.

In Google’s case, its external advisory council on AI lasted only a week before its employees revolted at the appointment of Kay Coles James, from the conservative Heritage Foundation think-tank, and shut it down earlier this month.

Luciano Floridi, who was one of the advisers and is director of the Digital Ethics Lab at Oxford, said the board has been planning to help Google navigate its trickiest dilemmas. “Some projects are perfectly legal, but may not be what people expect from a company like Google or the values it has committed to,” he said.

The most common practice has been to publish company principles for ethical AI. Microsoft, Google, IBM and others have all published lists of company ethics, while research bodies such as the Future of Life Institute, which developed the “Asilomar principles”, have tried to get scientists from around the world to sign on.

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Tech companies face criticism not only from campaigners and academics but also their own staff © Amnesty

But Mr Poulson said these ethics boards and principles lacked teeth. “When it comes to a major decision, literally only the CEO can say no,” he said.

AI Now, a non-profit set up to guide ethical development of the technology, also pointed to the lack of transparency and enforcement in its latest report.

“Ethical approaches in industry implicitly ask that the public simply take corporations at their word when they say they will guide their conduct in ethical ways,” the report said. “This does not allow insight into decision making, or the power to reverse or guide such a decision.”

In a clear example of this conflict, DeepMind shut down an independent review panel last November. It had set this up just two years earlier to scrutinise the company’s sensitive relationship with Britain’s National Health Service, after its health division was absorbed by Google.

Many companies have also joined wider bodies to work across academia, business and civil society. One such effort, the Partnership on AI, was founded in 2017 by Google, DeepMind, Facebook, Amazon, Microsoft and Apple. The partnership now numbers almost 90 groups, half of which are companies such as McKinsey, while the rest are non-profits, academics and other institutions.

“I know there are projects building repositories of AI ethics cases, or writing a white paper on the use of AI in judicial systems,” said Francesca Rossi, head of ethics at IBM and a founding member of the partnership. “I think the networking is very important too, the fact that these partners can openly connect with each other and work together in a way that is not available in any other place.”

Often the same people — usually executives of well-known tech companies — reappear on several different ethics boards. For instance, Mustafa Suleyman, a co-founder of DeepMind, runs the company’s own ethics unit, while also sitting on parent Google’s Advanced Technology Review Council and co-chairing the Partnership on AI.

Similarly, Mr Floridi, the Oxford professor, has sat on ethics boards for Cisco, Facebook, Google, IBM, Microsoft and Tencent.

“I have seen that more and more initiatives are coming out at the same time, and at this point, I think the AI ethics community recognises the need for co-ordination and convergence to be as efficient as possible,” Ms Rossi said.

Meanwhile, in China, companies are taking divergent approaches to ethical AI.

De Kai, a computer scientist at Hong Kong’s University of Science and Technology, and a member of Google’s shortlived ethics council, said that Chinese companies are generally more concerned with solving real-world problems as a way to do good, rather than focusing on abstract ethical principles.

How they define “doing good”, however, has been the subject of intense criticism from some quarters. AI companies such as CloudWalk, Yitu and SenseTime have partnered with the Chinese government to roll out facial recognition and predictive policing, particularly among minority groups such as the Uighur Muslims.

In March, Robin Li, Baidu’s chief executive, urged the “contribution of Chinese wisdom” to the ethics debate, and emphasised that providing a “good life to common people” was the ultimate goal. Tencent declares on its website that one of its goals is to “use technology to accelerate the development of the public good”.

Ultimately, experts say the field is still nascent, and a joint approach between the private and public sectors is required to build consensus.

“Unless you have an open debate and try lots of different experiments, how can we identify a solution?” said Mr Floridi. “We have three tools — law, self-regulation and public opinion. Let’s use them all.”

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FT Confidential Research

A RESEARCH SERVICE EXCLUSIVELY FROM THE FINANCIAL TIMES

FT Confidential Research provides data-based, analytical insight into China and Southeast Asia. Our experts and researchers combine findings from our proprietary surveys with on-the-ground research to deliver objective, predictive analysis of macro and industry-specific trends.

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