I can honestly say, making the decision to apply for the Imperial MBA is one of the best I’ve ever made. These first two months have been intensive but have provided a lot of space for self-reflection and growth.

Here are three lessons I’ve learned so far while studying an Imperial MBA programme.

Find yourself a mentor and coach

As part of the Full-Time MBA, we are matched up with a Careers Consultant and also have the opportunity to connect with a mentor from a past cohort. My Consultant has helped me define my career goals and build a plan moving forward. She challenges me to think of my professional growth opportunities in a new way and further pursue my goals. 

My mentor completed his MBA as part of last year’s cohort and provides a unique perspective of how to get the most out of the programme. Both people have given me advice that has helped shaped how I tackle the programme for the rest of the year. 

Be open to new experiences

My world has grown since starting the on the Imperial MBA. I’ve found myself in a cohort of people from a variety of backgrounds, both professionally and culturally. I learn something new from my classmates and their past experiences every day. I’ve also been pushed by my Careers Consultant to explore different post-MBA career paths, which has opened doors to jobs and potential opportunities I had never looked at before. This year is a chance to explore new paths and ways of thinking. 

Take time for self-reflection

A large part of the induction and early stages of the MBA has been self-reflection. We’ve examined our strengths, how we work in teams and our personal leadership styles. Sitting down and looking at the way that I work has changed the way I approach group work, class assignments and career decisions.

I know how to lean into my strengths and be aware of my weaknesses in order to get work done effectively and be a strong team member.  It can be hard to find time for this level of self-reflection in the working world, but the MBA programme has given me a platform to not only reflect, but also think through how I can use this knowledge and reflection in my everyday life.

Yes, the first two months have gone by quickly, they’ve been packed with workshops, classes, networking events and a lot of fun. I’m excited to see what the rest of the year has in store for me.

Technology has a tendency to amplify pre-existing inequities, but how far does machine learning extend these biases to mortgage approvals?

In January 2019, US Congresswoman Alexandria Ocasio-Cortez made headlines when she claimed that algorithms “have racial inequities that get translated”. In this case, she was referring primarily to facial recognition technology, but the point echoed existing concerns over the growing use of machine learning technologies.

By analysing huge datasets and identifying patterns and features within them, machine learning programs can accelerate and improve the accuracy of decisions in such areas as recruitment, marketing, healthcare and finance. The problem is the algorithms that underpin this technology are designed by humans and learn from historical human decision data, which means they risk taking on and, potentially, exacerbating human biases.

Learning from the past

What is undoubtedly true is that decisions made on the basis of algorithmic predictions have the ability to make finer judgements based on more subtle factors than conventional methods.

Take mortgage applications as an example: lenders primarily care about ensuring the loans they make are repaid, so they approve or deny applications (and set interest rates) based on the risk of default. Conventionally, this is done using a statistical model that benchmarks each applicant’s default risk based on income, job security, previous borrowing and a number of other factors.

Machine learning systems take this further, not just comparing applicants against statistical benchmarks but continuously combing vast amounts of historical data to find an applicant’s statistical neighbours (previous applicants with similar material factors).

Decisions can then be made on the basis of how reliable these neighbours were as borrowers. Over time, as the system learns more and receives more data, its decisions should become increasingly accurate in terms of avoiding likely defaulters and charging the ideal interest rate.

Hidden factors

In our research, we tested this by taking a dataset of nine million US mortgage approvals from 2009 to 2013 and tracking them over the next three years. Based on this data, we put together a conventional statistical model of default risk and a more sophisticated machine learning model.

The latter did indeed predict default with greater accuracy, identifying (among other things) marginal applicant “winners”, i.e. individuals who would have been deemed unacceptable credit risks or offered high interest rates by the conventional model, but are understood to be reliable borrowers at attractive rates by the algorithmic approach.

The proportion of winners was around 65 per cent for white and Asian borrowers, but only around 50 per cent for black and Hispanic borrowers.

Proportionally, these winners increase across the board, but it’s here that racial inequality enters the discussion: in our model, the proportion of winners was around 65 per cent for white and Asian borrowers, but only around 50 per cent for black and Hispanic borrowers.

What’s driving this disparity? It goes without saying that race was not included as a variable in our research, but it is possible that the algorithm has effectively learned to triangulate race, i.e. to work out a borrower’s likely race based on the other factors included. Equally, it’s possible the well-documented social injustices that certain ethnic groups make them, through no fault of their own, more likely to pose an objective default risk, and the algorithm is picking up on this.

No simple solution

In order to work out which of these causes was at play, we tested the model again, this time including race as a factor. The results were only marginally different, indicating that triangulation was most likely not the main factor at play – in other words, the algorithm is not inherently racist in the sense of calculating ethnic background and discriminating on the basis of it.

That’s not to say we can rule out triangulation as a factor, as it does play a part, but rather it is primarily the flexibility of machine learning in predicting default based on permissible factors that leads to the racial disparity in the proportion of winners.

Can machine learning algorithms morally be relied on?

Of course, this knowledge does not solve the problem. At the end of the day, our results show Ocasio-Cortez’s concern was valid: machine learning does reflect existing inequities, even if the cause is not inherent in the technology itself.

A lender genuinely concerned only with maximising financial gain and minimising loss will nonetheless see its approach have an inadvertent negative social effect. What’s more, as this is the case, can machine learning algorithms morally be relied on? And what level of regulation is required to mitigate these effects?

These questions will rightly be at the vanguard of research as the technology develops, and we can only hope the knowledge of what needs to change will prove a valuable tool in making that change a reality, leading us into a future in which machine learning can be an equalising force.

This article, originally published on IB Knowledge, draws on findings from the paper “Predictably Unequal? The Effects of Machine Learning on Credit Markets” by Andreas Fuster (Swiss National Bank), Paul Goldsmith-Pinkham (Federal Reserve Banks), Tarun Ramadorai and Ansgar Walther (Imperial London).

My name is Briana Johnston and I moved to London from the United States to start a Full-Time MBA programme at Imperial Business School. I came to London by myself and booked an Airbnb to stay in for my first week, with the plan to find a flat within that week and move in. Fortunately, I had teamed up with another woman in my programme to live together prior to moving, so I had someone to experience the joys and struggles of moving abroad with! Even with almost three weeks to get settled in, time seemed to fly, but for those who may not have as much time to settle in, I’ve compiled six tips for students moving to London from abroad.

Give yourself at least one week to get settled before starting classes

If you have the flexibility to move to London before your programme begins, I would recommend giving yourself at least a week to really settle in. This will allow you time to get rid of any jet lag, set-up a bank account, find a place to live, and start to get familiar with your neighbourhood.

It’s also nice to have a bit of a break before starting a programme at Imperial Business School, because the year will start off quite intense and will only get more demanding!

Set up a bank account ASAP

Setting up a bank account in the UK can be a bit of a catch-22 because it requires you to have a permanent address, plus, you can’t rent a flat without a bank account. However, there is a way around this, with online banks such as Monzo and Revolut, it is extremely easy to set up a bank account online. I would recommend setting up an account with one of them before trying to go through the harder process of setting up a bank account with larger banks such as Barclays or HSBC. 

In order to set up a Monzo or Revolut account, you will only need to provide an address where they can mail you your card. If you have a friend in London, send the card to them. However, you could also have it mailed to the accommodation where you’re staying. 

If you’re able to set up the account right before you move, you could have a bank account ready to go by the time you arrive in London, making it much easier to rent a flat.

Use TransferWise to move foreign currency into your UK bank account

The TransferWise app was extremely helpful and straightforward in my moving process, as I was not only able to transfer money from my US bank account to my UK bank account, but I was also able to secure my flat by transferring money to my landlord for the holding deposit via the app, which only took a few hours (it could take longer depending on your country and the time of day/day of week you initiate the transfer).

Act quickly when renting a flat and don't be afraid to negotiate 

If you are not planning to stay in graduate accommodation offered by Imperial, be prepared to act extremely fast when looking for a flat in London.  Whether you’re going through a letting agent, OpenRent (the landlord directly), or SpareRoom (a flatmate finder website), flats can go off the market within a day, so be prepared to pay the holding deposit needed to secure a flat immediately after a viewing.

Also, don’t be afraid to negotiate rent down as it is very common to do so in London. In my case, I found the flat that I currently live in through OpenRent and viewed it 30 minutes after the previous tenant had moved out. I had already seen a few flats by that point and knew that this was the best of what I’d seen, so my roommate and I immediately expressed interest during our viewing and negotiated the rent down by £50 per month. We then transferred over our holding deposit via TransferWise, once the landlord agreed with everything, the flat went off the market that day!

Prepare to pay six months rent upfront 

As a student with no income, if you do not have a UK guarantor, be prepared to pay six month’s rent upfront when securing a flat. This is typically not the case if you’re renting a room via SpareRoom or Gumtree, but for letting agents and landlords, this is what they typically require. 

It’s not fun to see all of that money leave in one go, so make sure you have budgeted enough money and have it ready in your bank account to make this transaction if necessary.

Take advantage of student discounts

One of the great things about being a student again is all of the student discounts London offers – and there are quite a few! 

Firstly, sign up to UNiDAYS, where you can get discounts from various stores and restaurants, such as Pizza Express and ASOS. 

Secondly, if you’re renting a flat, make sure you get exempt from paying council tax (you can google how to do this). 

Lastly, if you plan to take the tube throughout London or trains around the UK, sign-up for a student Oyster card and a UK Railcard.  A student Oyster card gives you discounted tube fares, but only for travel cards (i.e. monthly, annual passes), not for single rides. A Railcard gives you discounted fares for trains travelling outside of London (e.g. Oxford, Bath, etc.).  Depending on your age, there is a 16-25 Railcard and a 26-30 Railcard.  Being a student actually doesn’t matter for Railcard eligibility, however, it’s a good thing to have to save money on your journeys if you plan to explore more of the UK during your time in London. 

There are also many student discounts offered at movie theatres and other restaurants, so be on the lookout for those as well.

It feels like decades since I’ve been in a proper classroom. After getting five years of work experience under my belt, I found that my knowledge of academic life was a bit rusty and that I needed to learn how to be a student again.

Within the first couple of weeks of my Full-Time MBA programme, I already had tonnes of readings, exams scheduled and group assignments -  add into the mix networking events, recruitment presentations and a healthy social life, it seemed to be too much, too fast.

If like me, you were accustomed to a full-time job and have decided to get back into student life, follow my tips below to make the transition a little bit easier.

Get ahead of the deadlines (don't procrastinate!)

At first, the workload can seem overwhelming considering that your brain still isn’t in full student mode. We have several group and individual assignments for each class and most of us probably haven’t written an essay in a while, so it is best to have as much time as possible to complete them.

Creating a schedule with all your upcoming deadlines will help you stay organised. Plus, planning ahead will keep you from stressing out last minute. Also, it allows you to have extra time to check with professors if you have any questions!
 

Control your budget

Studying the Imperial Full-Time MBA programme means you will take one year off work, which can be scary leaving a regular income behind. Be sure that you are aware of this from the beginning and make sure your finances are in place throughout the year to take the stress off being a full-time student.

Remember, there are plenty of student discounts to take advantage of!

As the huge potential for data science, machine learning, and AI, to augment human performance emerges, two imperatives become clear. First, it is essential that these powerful technologies are directed towards social good and not just for commercial profit; and secondly, to get the best from these technologies, there needs to be a special focus on developing human capability alongside them - to innovate, to manage change, and to ensure ethical practices are maintained.

The new £250 million fund for a ‘National AI Lab’ to accelerate the use of machine learning in the UK’s National Health Service, announced this year, is a sign of things to come. Public as well as private organisations are beginning to respond to unmissable opportunities to improve operational and economic performance being presented by artificial intelligence.

The potential for machine learning to speed up the diagnosis of killer diseases, to collect and integrate genome sequencing data with disease research data, and to revolutionize NHS outpatient services, is huge. In harnessing that potential the challenge will be to develop the leadership and staff capability able to innovate new systems and manage the transformational changes this will imply.

On average, Google now processes more than 40,000 searches every second (or 3.5 billion searches each day). It is a statistic illustrative of an online world now awash with vast amounts of data, which is the essential fuel enabling AI and machine learning technologies to progress.

“Data is transforming the way business and society work,” as Imperial Business School Dean Professor Francisco Veloso observes.

Experts at Imperial Business School’s Gandhi Centre for Inclusive Innovation are now focusing on how data science and machine learning can unleash the potential of information for social good; how these new tools can be used to address the United Nations Sustainable Development Goals, for example - those related to poverty, inequality, climate, environmental degradation, prosperity, and peace and justice.

"Technology has gifted us with rich seams of data that have had a massive economic benefit. And yet, much of the potential for positive social impact is still locked up"

says Kieran Arasaratnam, professor of practice and the Associate Director of the Gandhi Centre.

To unlock this potential the technology has to be universally accessible and the principles of ethical data science must be applied, says Arasaratnam. These principles can be defined as:

1. Creating systems and infrastructure able to collect data and document collection processes in a way that is professional, legal and ethical.

2. Data obtained from an individual with their consent should not carry any trace of their identity when published or used by other organisations.

3. Restrictions on the use of sensitive data (financial, medical and personal) should be honoured.

4. Individuals should have a transparent view of how their data is used.

5. Data should not be used to infer predictions not relevant to their original purpose.

6. Action should be taken to avoid the unconscious biases that machine learning algorithms may absorb from a population.

For data science, AI, and machine learning to help us achieve these wider social goals, NGOs and other organisations involved must also address the same challenges faced by commercial businesses and by public sector organisations such as the NHS. They will have to innovate and embrace change.

In order to successfully integrate machine learning technologies into an organisation (into its practices, systems and culture) – innovation will be needed in three key areas:

• Innovation directly related to the technology – i.e. digital transformation.

• Innovation related to human capital – i.e. recruitment, leadership development, and training to create an agile and digitally literate workforce.

• Innovation related to strategy – i.e. embracing an innovation mindset to encourage different models and approaches (as explored in a recent article by Dr Anu Wadhwa, Associate Professor of Strategy and Entrepreneurship at Imperial Business School).

Across all sectors, public and private organisations are (or are soon to be) dealing with the disruptive potential of these data-driven technologies. With disruption comes the opportunity for breakthroughs, for innovation and change for the better, not only for business but for society too. Unleashing the huge potential of these technologies will be as much about developing human capabilities, as it will be about developing the technology.

There’s never been a better time to study an MBA. Students across our four MBA programmes – Full-Time MBA, Executive MBA, Global Online MBA and Weekend MBA – share why studying an MBA is so valuable.

Two of our Global Online MBA alumni, Sheikh Shahrir and Feliciana Wienathan, meeting up at Google in Indonesia