Eight articles cover topics such as AI in human resources management, the role of AI in personalized marketing, organizational decision-making in the age of AI, and how AI will launch the “feeling economy,” where interpersonal skills are more valuable than ever.
“Artificial intelligence is a rather fuzzy concept and is actually not that easy to define,” says Andreas Kaplan, a marketing professor at France’s ESCP Business School, who guest-edited the issue with ECSP marketing Prof. Michael Haenlein. “We define artificial intelligence as a system’s ability to interpret external data correctly, to learn from such data, and to use these learnings to achieve specific goals and tasks through flexible adaptation.”
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“Managers of the future will need to consider AI and the associated systems of automation as a central part of their future workforce,” Haenlein says in the introduction. “An average employee performs dozens if not hundreds of different tasks in a day, and only some can be taken over by a machine. Instead of talking about job replacement, we should be talking about job enhancement, because AI systems can help employees do their jobs more efficiently.”
California Management Review is Berkeley Haas’ premier management journal. Edited at the University of California for more than 60 years, the journal publishes cutting-edge research useful to management education, and presents new insights into the practice of management.
Face-to-face meetings between mortgage officers and homebuyers have been rapidly replaced by online applications and algorithms, but lending discrimination hasn’t gone away.
A new University of California, Berkeley study has found that both online and face-to-face lenders charge higher interest rates to African American and Latino borrowers, earning 11 to 17 percent higher profits on such loans. All told, those homebuyers pay up to half a billion dollars more in interest every year than white borrowers with comparable credit scores do, researchers found.
The findings raise legal questions about the rise of statistical discrimination in the fintech era, and point to potentially widespread violations of U.S. fair lending laws, the researchers say. While lending discrimination has historically been caused by human prejudice, pricing disparities are increasingly the result of algorithms that use machine learning to target applicants who might shop around less with higher-priced loans.
“The mode of lending discrimination has shifted from human bias to algorithmic bias,” said study co-author Adair Morse, a finance professor at UC Berkeley’s Haas School of Business. “Even if the people writing the algorithms intend to create a fair system, their programming is having a disparate impact on minority borrowers—in other words, discriminating under the law.”
A key challenge in studying lending discrimination has been that the only large data source that includes race and ethnicity is the Home Mortgage Disclosure Act (HDMA), which covers 90 percent of residential mortgages but lacks information on loan structure and property type. Using machine learning techniques, researchers merged HDMA data with three other large datasets—ATTOM, McDash, and Equifax—connecting, for the first time ever, details on interest rates, loan terms and performance, property location, and borrower’s credit with race and ethnicity.
The researchers—including professors Nancy Wallace and Richard Stanton of the Haas School of Business and Prof. Robert Bartlett of Berkeley Law—focused on 30-year, fixed-rate, single-family residential loans issued from 2008 to 2015 and guaranteed by Fannie Mae and Freddie Mac.
This ensured that all the loans in the pool were backed by the U.S. government and followed the same rigorous pricing process—based only on a grid of loan-to-value and credit scores—put in place after the financial crisis. Because the private lenders are protected from default by the government guarantee, any additional variations in loan pricing would be due to the lenders’ competitive decisions. The researchers could thus isolate pricing differences that correlate with race and ethnicity apart from credit risk.
The analysis found significant discrimination by both face-to-face and algorithmic lenders:
Black and Latino borrowers pay 5.6 to 8.6 basis points higher interest on purchase loans than White and Asian ethnicity borrowers do, and 3 basis points more on refinance loans.
For borrowers, these disparities cost them $250M to $500M annually.
For lenders, this amounts to 11 percent to 17 percent higher profits on purchase loans to minorities, based on the industry average 50-basis-point profit on loan issuance.
“Algorithmic strategic pricing”
Morse said the results are consistent with lenders using big data variables and machine learning to infer the extent of competition for customers and price loans accordingly. This pricing might be based on geography—such as targeting areas with fewer financial services—or on characteristics of applicants. If an AI can figure out which applicants might do less comparison shopping and accept higher-priced offerings, the lender has created what Morse calls “algorithmic strategic pricing.”
“There are a number of reasons that ethnic minority groups may shop around less—it could be because they live in financial deserts with less access to a range of products and more monopoly pricing, or it could be that the financial system creates an unfriendly atmosphere for some borrowers,” Morse said. “The lenders may not be specifically targeting minorities in their pricing schemes, but by profiling non-shopping applicants they end up targeting them.”
This is the type of price discrimination that U.S. fair lending laws are designed to prohibit, Bartlett notes. Several U.S. courts have held that loan pricing differences that vary by race or ethnicity can only be legally justified if they are based on borrowers’ creditworthiness. “The novelty of our empirical design is that we can rule out the possibility that these pricing differences are due to differences in credit risk among borrowers,” he said.
Overall decline in lending discrimination
The data did reveal some good news: Lending discrimination overall has been on a steady decline, suggesting that the rise of new fintech platforms and simpler online application processes for traditional lenders has boosted competition and made it easier for people to comparison shop—which bodes well for underserved homebuyers.
The researchers also found that fintech lenders did not discriminate on accepting minority applicants. Traditional face-to-face lenders, however, were still 5 percent more likely to reject them.
The Startup Roundup series spotlights students and alumni who are starting a new business or enterprise.
Federico Alvarez del Blanco, MBA 18
John Kim, PhD 18 (UC Berkeley/UCSF Bioengineering)
Hector Neira. PhD 18 (UC Berkeley/UCSF Bioengineering)
Robert Kim PhD candidate (UCSD MD/PhD, Neuroscience)
It’s not unheard of for busy surgical teams to inadvertently leave an instrument inside a patient—it’s estimated to happen about 1,500 times a year in the U.S. alone, according to research. Less frightening, but still problematic, is the considerable cost to hospitals of instruments that are brought into an operating room and never used, but still must be sterilized or restocked, as well as the delays that happen when the required instruments fail to make it to the surgical tray.
Solving those problems is the focus of Vidi, a fledgling company launched last November by Federico Alvarez del Blanco, MBA 18, and three other University of California graduates. “Tracking surgical instruments, is slow, manual, and error-prone,” Alvarez del Blanco says.
The team’s inspiration came while they were attending a workshop on visual recognition sponsored by information technology company NEC on the Cal campus. “We realized that the technology being used to develop self-driving cars could have wider applications in the medical field,” he says.
The heart of the Vidi system is a camera mounted in the operating room and connected to a computer. The system scans the surgical tray, recognizes the instruments on it, and keeps track of them. When the surgery is concluded, the system gives the team a readout of each item that was in the cart at the beginning of the procedure and lets them know if anything is missing.
The really difficult part of developing the system is training machines to correctly recognize hundreds of instruments, Alvarez del Blanco says. It’s similar to the technology self-driving cars need to recognize objects and react accordingly. That’s why Vidi team members have advanced degrees in fields such as bioengineering, neuroscience, and image recognition.
Although Vidi, which means “to see” in Latin, is very young, it has already gained a good deal of recognition. The team was awarded a Haas Dean’s Seed Fund grant last year; earned a second-place win at the University of California Big Ideas Competition in 2018; and won awards from NEC and the National Science Foundation’s I-Corps program.
Alvarez Del Blanco says his time in the MBA program helped him build the connections he needed to launch Vidi. “Haas has an interdisciplinary approach that gave me access to ideas and people across the entire University of California system,” del Blanco says.
Kourosh Zamanizadeh, BS 09, MBA 18
Ryan Alshak, BS 09 (Political Science)
If you’ve ever had dealings with a law firm, you’ve probably gotten a detailed bill with line items for everything from reviewing files to drafting documents to answering emails. While it may seem cut-and-dried, billing clients is actually a burdensome, error-prone task that costs law firms potentially billions in wasted time and lost revenue, says Kourosh Zamanizadeh, MBA 18, co-founder and COO of Ping.
A Berkeley Haas-nurtured startup, Ping uses artificial intelligence, machine learning, and cloud computing to automate legal billing. The software tracks, stores, and analyzes the time attorneys spend on a case, and then creates client-ready bills. It’s early days, but Ping has already attracted significant funding from top-tier venture capital firms (a public announcement is pending), along with a $5,000 grant from the Dean’s Seed Fund. It was named “Legal Tech Startup of the Year” in 2017 by the American Bar Association.
Ping has landed its first large client, Mishcon de Reya, a London-based law firm employing more than 800 people, says Zamanizadeh. Ping has already run a successful pilot and the firm has committed to expanding it company-wide within the year. Zamanizadeh also expects to start trials with a number of other global law firms later this year—a business expansion that will require a larger technology team.
Zamanizadeh and co-founder Ryan Alshak met while undergraduates and fraternity brothers at Cal a decade ago. “We always dreamed of starting a company together and decide to take the leap in 2016,” he says. “We both left our careers and just went for it.” The startup team has a deep lineup of relevant talent: Alshak is a former lawyer; Matt Bordas and Janesh Gupta are software engineers;Eric Zaarour is a designer; and Zamanizadeh has experience in business development and investment management.
This is the second startup for the five-member team, who made an earlier, unsuccessful attempt to build a company around an app for exchanging contacts. The team hit upon the idea of focusing on legal technology and they were accepted by Skydeck, the accelerator run by Berkeley Haas, the College of Engineering, and UC Berkeley, where they had a home base to develop their idea further.
“The startup ecosystem at Berkeley has very much matured since Ryan and I first met as undergrads. It’s truly world-class,” says Zamanizadeh, who credits Skydeck Executive Director Caroline Winnett and Ikhlaq Sidhu, chief scientist and founding director of the Sutarja Center for Entrepreneurship & Technology, for their extra support. “The environment has been very empowering and the help we’ve received couldn’t be any more genuine.”
Dustin Seely, EWMBA 18
Michael Brenndoerfer, M.Eng 18
Efficiently buying and selling bitcoins and hundreds of other cryptocurrencies is not a problem most people have. But as these hypermodern currencies become more of an investment and less of a curiosity, investors will need a simple way to manage their crypto-portfolios.
That’s the market Dustin Seely EWMBA 18, co-founder of Cryptonite, is going after. “We’re going to give investors a way to invest in the entire cryptocurrency market in one place, and do it in U.S. dollars,” he says.
Seely and co-founder Michael Brenndoerfer met in a Berkeley Haas entrepreneurship class, and then took the new, multidisciplinary “Blockchain and the Future of Technology, Business and the Law” course last spring, where they learned more about the technology underlying cryptocurrencies. Their young company was awarded a Dean’s Seed Fund grant and is expected to go live in the fall.
The cryptocurrency market is volatile and expanding, with a market cap of about $250 billion in mid-July (down from a peak of more than $800 billion in January). Although bitcoin is the most valuable and most widely known, there are now more than 1,600 cryptocurrencies sold on almost 12,000 scattered exchanges, according to CoinMarketCap. What’s more, many of those exchanges do not accept dollars, so doing business with them requires buyers to slog through complicated, multi-step trading procedures. Buying a cryptocurrency called Zilliqa, for example, means buying a bitcoin in dollars, and then using the Bitcoin to purchase the Zilliqa, Seely explains.
Cryptonite will serve as a middleman between investors and other exchanges. Account holders will be able to buy cryptos in dollars without dealing directly with other exchanges, and manage their portfolio on a mobile device, Seely says.
At the moment, cryptocurrencies are only lightly regulated, but Cryptonite is preparing for the future. “Securities regulations are coming to the space and we welcome it,” Seely says. “Regulation will give further legitimacy to the market and we can use it as a competitive advantage when we become fully compliant.”