How Faculty Director Kai Larsen went from designing banking systems to training tomorrow鈥檚 analytics leaders
For听Faculty Director Kai Larsen, the future of business analytics presents exciting problem-solving possibilities and challenging questions about ethics. He鈥檚 been honing his expertise in analytics for over 30 years, and his unconventional journey lends a world perspective to his teachings. Through his unique professional experiences and insights on this dynamic discipline, he鈥檚 training students to drive value in the workplace.
The problem-solving possibilities of analytics
At the beginning of his analytics career, Professor Larsen served as a consultant in bringing Norwegian banking into the future. His work helped create the first Norwegian Internet banking system, as well as a central billing system鈥攚hich altered the way people received bills in the mail and automated their payment process.听
鈥淚nstead of a bill per day, all bills would arrive in one envelope every two weeks,鈥� said Larsen. 鈥淚f you did nothing, then they would just get paid. So that鈥檚 sort of a case of automating an existing process and making it easier.鈥澨�
Using analytics to design automated systems was an exciting combination for Larsen, and it meant the ability to solve problems in a way that hadn鈥檛 been possible before.听
鈥淭o me, it was always about innovating processes鈥攁utomating and innovating,鈥� said Larsen. 鈥淪o, can you take something people are doing and make it way easier? Analytics and artificial intelligence is just one more really cool toolset for tackling processes and problems."
An introduction to machine learning
While a consultant in Norway, Larsen began working on expert systems and automating processes that normally require human expertise. By interviewing area experts, such as loan processing officers, he was then able to create enormous collections of 鈥榠f鈥搕hen鈥� statements based on their knowledge. This would serve as a foundation for automating these processes with machine learning.
鈥淚f someone walked in asking for a business loan, a loan processing officer would follow up with a set of questions. At the end of those questions, that officer would then say if you could get a loan or not. So, what we would focus on there was developing systems that built that expert鈥檚 knowledge into technology. These 鈥榚xpert systems鈥� were expensive to build, and it was never quite clear whether automating past processes would lead to fair and correct answers.鈥�
Shortly after his consulting work on expert systems in Norway, Larsen moved to the U.S. to pursue a PhD. At that time, he became more familiar with machine learning, its role in cutting the individual expert out of the process, and it鈥檚 relationship with the human knowledge and biases it鈥檚 created from.
Defining systems from data
In using analytics to automate processes, Larsen explained that the method relies upon past evidence, or the history of data, that鈥檚 available. That data makes it possible to then determine the guidelines, or rules, that will drive an automated system.听
鈥淚f you鈥檝e already given out ten thousand loans, then we know how those people who received loans behaved鈥攁nd we can set up a definition of success or failure,鈥� said Larsen. 鈥淪o maybe they didn鈥檛 make a payment in 60 days, or maybe they paid the whole loan on time. You can define what 鈥榮uccess鈥� and 鈥榝ailure鈥� for a system looks like.鈥�
In addition to people鈥檚 loan repayment behaviors, all of the collected information about those individuals is also fed into the system鈥攕uch as purchase history, credit rating, demographic information and more. Using all of this data, an expert鈥攐r in automated cases, the algorithm鈥攖ries to figure out the 鈥榬ules鈥� that will drive future decisions, like whether or not someone qualifies for a loan.听
鈥淏ut there鈥檚 enormous bias in that decision-making, right? We know for a fact that certain groups of people, often because of their race, ethnicity or gender, have traditionally had a hard time getting loans because of the biases of the loan officers and other systemic issues.鈥�
For Larsen, this question of human biases in analytics and the consequent ethics of machine learning means it鈥檚 crucial for students to be properly trained on these issues before they enter this increasingly complex discipline.听听
鈥淚nitially, we were excited about machine learning because we would give that algorithm all the data and it would come up with a 鈥榤odel鈥� that behaved like an expert. But what people often forget in cases like that, is that the machine and its data is still just based off the biased intelligence and social systems it was created from.鈥�
Training tomorrow鈥檚 leaders
Now an associate professor and faculty director at Leeds, Larsen puts his expertise to use by preparing future generations in the field. His comprehensive approach to teaching business analytics includes shaping students into experts while incorporating ethics and social issues into their coursework. From the听undergraduate Business Analytics Track听to the听Master鈥檚 in Business Analytics program, students are challenged to think critically about data and to create models in ways that glean insights and drive value. The skills that Larsen鈥檚 students develop throughout the programs mean that, by the time they graduate, they鈥檙e prepared to operate in a variety of business landscapes and to always have an eye for ethical business.听听听
鈥淢y classes tend to start very technical at the undergraduate level. First, I teach students all about transforming data and everything they can do. Then, we go into machine learning and how to develop models. They have to have these skills before we can teach them how to use this knowledge ethically. And for our ten-month graduate program, we first make students into business analytics experts and then get more into the social issues in the spring.鈥澨�
Read more on听Data, ethics and AI: Learning about the Leeds MS in Business Analytics听to discover more insights on artificial intelligence from Faculty Director Kai Larsen.