Series Eleven, Episode Five – Insights, Intelligence and Innovation: The data and science of diversity

Julia: Hello. My name is Julia Streets, and welcome to the DiverCity Podcast. Talking about diversity and inclusion in financial services. On every episode, we seek to shine a light on positive progress, call out areas requiring further focus and offer lots of ideas to inspire change. Before we get started today, I just want to take a moment to thank our friends at City A.M. for their continued support of DiverCity Podcast, publishing and promoting both our episodes and our supporting blog series, so their readers can stay on top of the very latest diversity and inclusion debate. Now you might want to check out their own podcast called The City View, for all the latest news and opinion from the city, because we at DiverCity Podcast are huge fans.

In this season of DiverCity Podcast, we wanted to broaden our focus and bring in more of an international flavour to our discussions. Of course, reaching out to commentators around the world does bring some timing challenges. So in this episode we depart from our usual format and instead bring two interviews, not at the same time, but in two parts in this episode.

Today I’m joined by Anna Tan from Singapore and Rana Gujral , from San Francisco. Rana Gujral is an entrepreneur speaker, investor and CEO at Behavioural Signals, an enterprise software company that excels at distinguishing behavioural signals in speech data, with its proprietary, Deep Learning technology. He is a thought leader in this space, and that’s not surprising that he’s often leading keynote discussions and also joining as a panellist at industry events around the world. He’s also a contributing columnist at TechCrunch and Forbes. He’s won many awards, most notably the prestigious recognition as entrepreneur of the month by CIO magazine. Rana, it’s great to have you on the show. Thanks so much for being with us.

Rana: Thank you, Julia. It’s a real pleasure to be here.

Julia: I’m really keen because as many of the listeners will know, I spend my day job talking about technology and Artificial intelligence comes into this a great deal, and the discussion, therefore, around data, Deep Learning, analytics, et cetera. So first of all, tell us, what are you focused on right now?

Rana: At Behavioural Signals, we use intelligent and actionable insights from conversations. If you think about it, human communication is a very complex process that depends on obviously the words being spoken, but also for the most part as the way they’re being expressed. So what we do at Behavioural Signals is, we have built some very advanced AI models that unravelled signals from the speech data, with a proprietary Deep Tech that focuses on the cues. So we focus on the intonations and other interaction signals like tonality and prosody and unravel a variety of signals. Emotion signals, behavioural signals, and we’ve also built some advanced classifiers on those raw features, which we could say are intent signals. So for example, predicting what the participant in the conversation will do in the near future. For example, will the buyer not buy, or if a debt holder will pay or not pay.

That’s what our focus area is. If you look at just general NLP, landscape, the traditional NLP and west interaction offerings, largely focus on what is being said, and what we do is, we focus on how something is being said. That’s what we do at Behavioural Signal.

Julia: And of course the conversation about data artificial intelligence often turns to some key questions concerning bias. I’d love to hear your thoughts because we’re having a broader conversation about conscious, unconscious confirmation bias. I’d love to hear about your thoughts about what we should be paying attention to in the world of artificial intelligence. When you think about those signals and the conversations you’re listening into.

Rana: Bias is  a very important aspect of consideration if you’re going to be focused on building an AI model or for that matter, any AI related product. It is extremely hard to implement correctly, first off. If I was to think about a product like ours, we have to consume tonnes of acoustics data, which are essentially phone calls and conversations between two humans. Now, in order to build an AI engine that can accurately decipher the cognitive state of mind and derive from that, behaviours that annunciate and create accurate conversational profiles of the humans, you need to then build it in a form that is bias free.

Different people speak differently. Males do differently from females, different age groups interact differently, then there are differences between different cultures, different countries. And so, if you’re now going to build a bias free engine, you have to first ensure that the data that you’re sourcing, to have the engines learn from, are bias free, and that it has diversity. And the annotations and other advanced models that you’re going to decipher based on human intervention and human assessment are also bias free.

That’s easier said than done. There needs to be a tremendous amount of checks and balances, not just at your level in the company that is building the product, but also oversight on all the other partners that are either helping on certain aspects of that product relevance. It could be annotations or other aspects of data sourcing. It’s a continuous area of focus. It’s a continuous area of compliance and monitoring that one has to absolutely implement in order to build what you call a bias free engine, but I would classify that as a useful engine because a biased engine is not useful. It’s useless. So if you’re going to build a product, it better be useful, it better work. And if it’s not bias free, it actually is useless. It doesn’t work. There’s a silver lining in this whole equation that, at least in the AI world, if you’re going to build a biased AI model, essentially you will fail as a business in the long term.

That underscores why this is so important. Of course it’s important because you’re now going to put out a product that is going to decide something that might impact how a business or even a commercial activity, or maybe even a personal aspect of a human gets impacted. And so you better be making accurate and proper decisions and bias free decisions. But also from that perspective, if you’re going to build a successful commercial entity, it’s very important to focus on a bias-free learning model.

Julia: Because one of the things we think about a lot in the world of FinTech innovation, but also, a lot of our listeners are working huge global institutions around the world, which is if you’re starting up, being very mindful about building out data sets that are unbiased, as you say, all the time useful engines, what advice do you give organisations that have got legacy data, and how they can best tackle and almost retrospectively cleanse it in order to tackle the bias dynamic?

Rana: There’s a very interesting argument that’s emerging in the industry right now. And to be honest, it’s somewhat controversial because it has different flavours to it and different lenses to it. So I’ll answer that question by going on a slightly different tangent, we’ll come back to this. If you think about how humans learn, the humans learn to their ecosystems. So we’re not born with certain mindsets. We learned those mindsets from the ecosystem. From our schools, from our peers, but for the most part about 80 to 90% of that is our parents. If our parents are biased and they have some inherent worldviews, they impart those on us and we learn those, and of course we enhance them and change those, we’re also influenced by our own individual ecosystems, but that’s an amalgam of who we become as a person and our worldviews is a combination of what we’ve absorbed from the ecosystem.

Now, this whole aspect of AI and Machine Learning is really taking that human learning model and putting it in a software programme, which is rather than have the engine be programmed or the software be programmed to do X or Y, you’re creating this self learning entity. So you have access to all the data, you’re going to process the data, you’re going to make a hypothesis, you’re going to make an assessment on what should happen based on the data, and then you go do that action. You look at the result, and if the result is what you expect, then you confirm those assessments and you’ve doubled down. And if not, then you revise those assessments and you reanalyse that data. That’s how a self learning Machine Learning model works, which is the more data you have, the more things to do it learns from the ecosystem.

With that, if you’re going to truly build an AI engine with no interference, with no oversight, and have the engine truly properly evolve, it would develop biases. It should develop biases because it’s a free-thinking engine, and a free-thinking mind will develop biases. Just like a free-thinking human mind will develop biases, relatively free-thinking, from the ecosystem standpoint. There are some good biases and there’s some bad biases, and the question is, how do you qualify a good bias? Your good bias may be different from my good bias. And who’s the higher authority deciding what’s a good bias and a bad bias. So now you have this debate is that to truly develop a bias free engine, you have to put in checks and balances. You have to influence the model’s thinking in terms of where to go.

If it’s going X don’t go there that’s bad, don’t discriminate based on gender and sex and other things. But if the natural thinking based on the data that has, takes the engine in a certain direction, and you’re now influencing that through certain checks and balances, that is also going to allow for people to influence it in other ways. And so where is that line? When you’re talking about legacy data and you’re talking about potentially cleansing and influencing, or maybe selecting what parts of data get processed, what parts of data not get processed, how do you influence the thinking of some of these engines? There’s a big debate. One part of the thinking is, let it go the way it’s supposed to let it think like a human would, and then, let the chips fall where they may, and the other is no, that’s extremely dangerous because humans are flawed and we don’t want our computer systems to be flawed, we want them to be pristine in their thinking.

And that leads to the debate of who decides what is pristine? Who is that higher authority? And that high authority now has all the power to decide the pristine mindset for everybody. It is a very interesting debate. It’s a very complex answer to that, but I think right now, certainly there are some obvious answers in terms of, well, there’s some things which are obviously wrong, so yes, it’s okay to influence certain aspects of that learning to make sure those aspects are bias free. There are certain biases which are obviously wrong, so let’s make sure that we don’t go down that route. But then there needs to be also a line in the sand somewhere, which restricts that experiential learning and that be allowed for some free thinking in there as well, and not go too far with that. So, that’s the big challenge ahead of AI, but also I guess all bias free systems are at this moment.

Julia: It’s interesting because it’s quite easy to put a simplistic view over the top of it. You go, it should be ultimately completely bias free, which I think may be the opinion of many. We should find a way to cleanser the bias out, make it a perfect set. Is there ever a perfect set? But the other point of view is, actually that in and of itself may not be helpful. So therefore, where are the lines and who decides? Really fascinating. Thank you so much for your thoughts on that. I just want to come back to almost closer to home in terms of thinking about leaders in the industry. I’d love to get your thoughts about the conversation about diversity and inclusion, which you’ve already drawn out in the discussions about the bias in data, and the mindsets of leaders, because it strikes me from many conversations and hosting many discussions is, the conversation about bias in data is increasingly if not well heard. Whether it’s acted upon of course is a second thing, but it’s certainly heard because it’s being brought up in many, many conferences as well.

I wonder how, when we think about leaders in the industry, and what advice you’d give in terms of the mindsets and how we can change their mindsets to really understand the value of the diversity in the data.

Rana: I believe in a mindset that is rooted in more down to earth elements of practicality. So, if you now take a step back and think about, why have some decisions that have been made, made that way? What has led to those biases? And of course, we’ve seen that let’s say in the VC and the investor community, we’ve seen very little diversity across the board. You’ve had the compositions of the leadership teams and the venture capital firms, pretty much all X one type, that’s it. Very little female ratio, almost non-existent, very little diversity in terms of race.

And so the question is, and then, that percolates into the entrepreneurial community in terms of who gets the investment and who doesn’t. So we’ve had a lot of discussions about why that is the case and what leads to some of those biases, and it’s somewhat over simplified, but of course there are generational issues, there are cultural issues, but the short answer is that, these entities like corporations are results-driven and they are essentially doing back matching, which is, you invest and you create, and you replicate certain patterns that you feel, give you the best chance for success.

If you look at it and say, “Hey, I’m going to invest in the company.” And now you have three choices, one is say, an all male presumable group of people coming in from say, I believe graduates, you’d see a certain pattern emerge. It was like, “Yeah, I’ve seen that pattern or perceived pattern to be successful. I’m going to go for that.” Versus maybe minority female-led venture, which you haven’t seen many of those. So you’re worried, and you’re scared, goes like, “I don’t know, is my money going to get me better return? And, is that going to be a successful venture because my pattern recognition says X and Y?”

So, that’s what leading to that bias. Is it really gender based, is it really race based or is it purely pattern recognition, is it purely based on what leads me to more success? So, now we’ve got to turn that apple cart and say, “Okay, so if that’s what’s driving the thinking, presumably it’s really not gender based or race based or sex based. It’s purely based on what you think will give you the best results. So let’s look at the results.” And I think that’s where the pattern changes.

Now, if you stop to look at the results, you’d say, “Female-led entrepreneurial ventures are on order of magnitude more successful than male-led ventures.” That’s not a stack most people understand or have looked at or has been publicised. But that is a fact, and that is also  true for minority led entrepreneurial ventures, which is, they’re just order of magnitude more successful, including, If you look at the first generation Americans or immigrants and the ventures that are created by them are order of magnitude more successful. And so now, if you put out those stats, and so thats what we’ve done, we’ve worked with a bunch of investment firms who have done an amazing job in some of these areas. You go out and you bring these facts out because this entrepreneurial community, the venture community, the financial community loves numbers.

They love stats. You look at those stats and say, “Actually your chances are better from a successful investment standpoint, if you invest in a woman, founder or a minority founder or immigrant founder.” And then there’s no discussion, because their results are driven. And so that’s what we’re seeing right now. There was an uphill battle around like, Hey, do it because it’s the right thing to do it. Doesn’t get you too far. Do it because it actually works better is the right way to do it. So that’s what is also emerging in the corporate world. You’re seeing that boards and C-level teams that are diverse has, especially diverse from a gender aspect, more women C-level members, including in the board, leads to more balanced decision-making, those companies that are more successful, they have lesser other cultural issues, and they’re also just, better perceived in the market from a overall product dynamic and they’re more successful, more profitable companies.

People are actually starting to wake up to see that, “Hey, diversity is not important just because it’s the right thing to do, it’s important because it’s a better model. It gives you better results.” And that’s not too hard to understand because diverse thinking allows you to not only think of just X, but also Y and Z, because Y and Z were ignored because everybody you hire only thinks X. And now you’re thinking X, Y, and Z, you’re smarter. That’s a very simplistic argument that can be applied to everything else. The more diverse you are, the more perspectives you are, the more worldviews you are, your decision-making is more sophisticated and more advanced and better.

And so with that, I think that’s what we need to do. I think what we need to do is, Including the show like yours and others that are focused on this initiative, which is commendable, is to bring more of those stats out because those stats exist, and they need to be front and centre. Let’s look at the stats of companies that have a diverse board and compare them with the companies that do not have a diverse board. Let’s look at their growth, let’s look at their revenues. Let’s look at VC firms that are led by diverse GPs, and let’s look at their fund performance. Let’s look at entrepreneurial venture started by a diverse set of founders, women, and minority and others, including LGBTQ and all of the other aspects of diversity. And let’s look at their success ratios versus non-diverse monocular setups.

That’s all we really need to do. That’s it. It’s not only the right thing to do, it’s the better model. I think that’s what’s missing, I’d say. I see a lot of narrative out there in terms of a preach, which is, “Do it this way.” And then you have the other side pushing back, which is like, “Don’t tell me what to do. We’re going to do base it.” But the meritocracy argument comes into play, which is to let the merit be a deciding factor, which is the usual defence of the right thing to do. But I think the right thing to do is the wrong argument. I think it’s the better thing to do. It’s a more successful model is the better argument because at the end, that is not only true, that is a much more successful argument in my opinion.

Julia: We’re going to be navigating some interesting economic times ahead. I’m going to ask you the question I ask all our guests, which is, why you believe diversity inclusion should remain high on a corporate agenda right now?

Rana: Well, I would say it needs to be high on the corporate agenda because it is what is going to make these corporates more successful. And it is not only going to make a better world for us and the next generation, the legacy that believe behind in terms of a more inclusive, balanced society, which is exactly what we should be aiming for, but to underscore and insert a capitalist agenda into it. It is the better model. It is the more successful model. It will make your company more successful, more profitable, grow faster and last longer. And I’d say that seals the deal. Why else would you do it another way?

Julia: Rana, it’s been great to have you on the show. Thank you for calling in from San Francisco.

Rana: Thanks Julia.

Julia: And so from San Francisco, we move on to Singapore, and today I’m delighted to be joined by Anna Tan. Anna has been living in Singapore for the last 10 years where she says that she has established herself as Asia’s leading corporate transformation and leadership coach and consultant. What is particularly interesting is that she examines the conversation about skills, talent, and diversity and inclusion through the lens of neuroscience, neuro linguistic programming, and social psychology. She facilitates the Singapore chapter for open for business where local and multinational companies meets quarterly to learn and share best practise in diversity and inclusion. Anna, it’s wonderful to see you. Thank you so much for being on the show.

Anna: Thanks Julia. Lovely to be here. I’m really excited in this conversation we’re going to have together.

Julia: Well, let’s get straight into it. So the first question I always ask all our guests is, what are you focused on right now?

Anna: A particular focus I’m working with corporate companies, especially in, how do you have high performance through the inclusion diversity lens as we’re now, hopefully coming through COVID. So there’s been some very interesting programmes that we’re starting to put into place to enable people to transition through this tough period of time.

Julia: It certainly has been a tough period of time. And one thing I’m really interested to get in to as quickly as we can is because I was fascinated about the fact you tackle this as a science of diversity and inclusion. As I mentioned in my opening remarks, one of the angles that you apply to the conversation about diversity and inclusion is regarding science. I’d love to hear your thoughts about, tell us more about that in terms of how do you apply science to this, and also we’d love to hear some of your findings.

Anna: What’s quite interesting is to look at diversity and inclusion through a totally different lens by stepping back, to firstly understand why do we have prejudice? Why do we have preferences? And where does bias comes from? So if you step back far enough and understand that as human beings, especially through neuroscience, when people are at their highest fears, what we do is that we’re looking to blame others for our problems and our difficulties and things that are going wrong. So when you can consider that in the biggest scheme of things, when people start to appreciate how to look at each other through a different lens, but not through the lens of fear and threat, but moving into more of the compassionate and reward state of the brain, can we then actually become more inclusive in our thinking rather than seeing each other as the enemy.

Julia: In terms of addressing that or bringing that into corporate life, how do you bring that in? Is that well received? Do organisations recognise that, or do they still see it as very much a kind of peripheral, “Yes, that’s all great, but we’ve got big business things to focus on right now?”

Anna: At the moment throughout the past year and a half with COVID, people’s well being and mental health has been going downhill, and you’re looking at a lot of organisations where not only are they pivoting, but that also trying to maintain business or look for new opportunities, but that’s difficult to do when their staff are feeling either depressed or they’re feeling unable to be creative and innovative. And so the positioning of this particular programme is to enable not only for the leaders to understand how do they have high performance, but how do they ensure that they bring people with them through being not only inclusive, but also understanding that a diversity of thinking will help promote their innovative and creative thought processes, and thus get back into the game.

Julia: As we talk through the lens of innovation and change and uncovering opportunity, having that diversity of thought around a table is incredibly important to be. And I love the fact, you were talking about bringing a scientific mindset to understanding how your diverse talent thinks, because if you imagine the potential of what you could truly unlock there, contributing to organisations to be really successful with that. That sounds just fascinating. Are you seeing many organisations are really coughing on to the potential this could deliver?

Anna: Yes, because we’re positioning this as leadership training, and positioning this for the leaders to understand their own emotional journey and their own thought process so that they can start to understand when they get triggered, when they move into the threat state and then become defensive or aggressive or feeling vulnerable. And once they have their own self awareness, are they then able to become a positive role model or to put into place other practises, other training, other support mechanisms to allow their staff to move from a difficult emotional and mental position to becoming more embracing with the change that’s happening globally?

Julia: I’ve been hosting so many discussions about the future of work, the future of leadership and the word empathy comes up time and time and time again. One of the things I’ve been really thinking about is, it’s all well and good to say to a leader, particularly leaders who have expectations upon them to change the diversity mix of their organisation, but therefore, how do you help somebody become more empathetic? And here’s the answer. It starts with science.

Anna: What’s quite interesting, we teach three modules first, the kickoff modules. And they are firstly for the leaders to understand what is the neuroscience of their brain. Dr. David Rock did some fantastic research. He has a model called the SCARF Model, S-C-A-R-F. That stands for Significance, Certainty, Autonomy, Relatedness, and Fairness. And this is all hard wired in every human being, that when you’re triggered negatively in any of these five domains, you go into the threat state and then you’re either fighting for your life or you’re running away from fear. When you can actually calm that down and move people into the rewards state, then suddenly people are then more collaborative, self innovative. This is when we show up as the best of ourselves. And this is when we are more able to accept and celebrate difference rather than activate another term called The Horn and The Halo effect.

The Horn effect is when I’ve labelled you in the negative way, and then I seek to prove myself right. That’s called confirmation bias. On the other side, I may see you as the star player and no matter what you do, even when you make mistakes, you can do no wrong. And so when leaders start to understand why we do what we do and how do we have confirmation bias, how do we use our belief systems that we often adopt? Not only from our communities, our parents, our environment, but how do we continue to prove ourselves right to our belief systems, and then that we go onto autopilot and we keep repeating the same perspectives, the same stories, the same way we recruit and so forth. How do we therefore break that pattern by becoming more conscious of why we do what we do to then choose something better.

Julia: It’s fabulous to hear your thoughts on this, because I think this is something that people are really trying to tackle at the moment about how do you shift those leadership mindsets and those models as well, and it’s a really practical ideas in there as well. So thank you so much for your thoughts on that. We talked about celebrating difference or recognising difference. I would love to hear your thoughts about whether you’re experiencing any marked differences in corporate culture. When you think about Asia Pacific compared with other parts of the world, I mentioned again, in the opening remarks, you lived in the UK, also now living in Singapore as well. I’d love to hear your thoughts about, are you witnessing any marked differences in corporates and cultures around the world?

Anna: Yes, it’s absolutely fascinating because I also do change management and I work across APAC and all the different markets in APAC. The difference between Japan to India, to China, to Hong Kong, to Singapore, Malaysia, Thailand, Australia, culturally very different. But what’s quite interesting in Asia, much more than in Europe, where I grew up in did a lot of my work. In Asia, there’s so much more focus on academic achievement and even for the beginners or junior role in any corporate, you have to have a degree. It’s quite incredible. Even if your degree isn’t in something relevant, or if you’re looking to climb the ladder in corporate companies, they’re looking for an MBA. And they’re looking for them, how they would say it in a job advert would be a distinguished university. And so there’s almost a snobbery of academic achievement, but we all know statistically that there is no correlation between high achievement in a corporate role versus high achievement academically, even though people think there is, there’s a lot of research to show that there isn’t that necessary correlation. So that’s one example.

Another example in terms of a distinction, in Asia, as an expat, so typically someone from Europe, from north America, from Australia coming into work in Asia, there’s still almost that kind of racism in so far as you are thought as of being better than the locals, inverted commas, because you’re expected to have more of a worldly view or your education is deemed higher than some of the local locally attained qualifications.

What’s quite amusing for me personally, is that a lot of people in Asia will scramble to get into Oxford, Cambridge, Harvard, and so forth. But the most interesting thing, I’ll give you Singapore, as an example, the three Singapore universities. It’s actually academically harder to get into the local universities, than it is to go overseas. So you only go overseas because you couldn’t get into the local ones. But then internationally, no one recognises that. So that’s also quite interesting. And the third level in terms of diversity is that I’ve heard my HR friends often comment that if you’re an expat doing the same job, you’ll get 10/20% higher salary because you expect it. And the multinational companies that bring you in, expect to pay you more versus, I know living in the UK, if you are a foreigner, you’re an immigrant, versus here, oh, you’re in ex-pat. So that perception is quite marked now living here in Asia.

Julia: It’d be really interesting to see how that shifts over time. Country by country, region by region. Because what we’re seeing at the moment is these incredible opportunities that will be opened up as we come out of COVID. The countries that are emerging out of COVID stronger are some of the emerging markets. Some of them are the frontier markets, some of the opportunities of business. So actually the attitudes towards where opportunity comes to be found or naturally shift as well. This is something I’m paying a lot of attention to at the moment. And so therefore, where the skills and talents go, how the skills and talents are rewarded and also how they are deemed to be educated and worthy to be in an organisation, could quite possibly shift as well.

Anna: What’s quite interesting in Singapore, so Singapore for me is a model country of how to deal with COVID. Primarily, and I suppose a lot of people would say is because we’re small, we’re a city state and the population is broadly around seven million, yet at this point in time, when most of the world they’re in the second or third wave, we’re coming out of it. And so I can actually go outside and all the restaurants are open and the theatres are open and you have to wear a mask, but everyone’s very compliant here because everyone’s really aware of their social responsibility and the social care and acknowledging that the government has spent billions trying to get the economy to move again. So people will more or less toe the line and wear the mask and do the social distancing and so forth.

Plus here, though, they do name and shame if they catch you. And then they do slap you with a huge fine, so it’s carrot-and-stick at the same time. But because of that, we have a lot more freedom and the economy is actually starting to inch up words, just to your point. Something else I wanted just to mention in terms of gender is that in Asia, you have a culture where professional women are encouraged to work because a lot of families will have helpers. And so the way that they’ve structured the family support is quite different from my experience in the UK, where it was quite difficult and expensive to have childcare, whereas here it becomes a lot more affordable and expected. One thing that Singapore also does is that it’s raised the working age, now primarily because there is no national welfare system per se.

But what they do do is that you save into your own pension buckets rather than into a national bucket. And so in the UK, you would have your national insurance here, you have your own personal national insurance that is taken out of your salary. And what they do is that they also give incentives to employers to employ older people beyond certain ages. And you also get training supplements, through COVID, where a lot of people lost their jobs.

The government here, they’re spending lots of money in the new industries of cybersecurity, AI, robotics, and so forth. And they’re pumping in a lot of money to retrain people with special allowances, if you’re over 40 or 45. If you’re in a midlife and you have to change your career, they’ll give you a grant and they’ll fund you, and they’re working with a lot of the big companies to have the transition into the new skill sets. And even when you’re beyond 55, 60, employees get a grant to keep you in employment, because they would rather, you have employment than to not have the income or the social network or the purpose in life is the perspective here.

Julia: I’d love to ask you Anna, and it’s a question we’re asking everybody who comes on to the show is, we are navigating potentially really tough times. And we’re going through an economic downturn. It’s wonderful to hear your comments before the break about how it seems that Singapore is actually going into a bit of an uptick as well. I’d love to hear your thoughts about why diversity inclusion must remain high on corporate agendas.

Anna: I’m sure many people have previously talked about the economic case. I think when you’re looking at what’s happening globally, especially with geopolitics, I think even more now than ever now because of COVID, we have remote working and that’s now become the norm. That the ability to have global teams working together, is marvellous. That the ability to break down all of these barriers because the technology allows us to do that, is wonderful. And so always we’re at crossroads of which side do we choose? Do we put the barriers back up or do we allow each other to find a new world order of working together to collaborate, to appreciate our differences rather than to focus on what’s going wrong rather than what could go right.

Julia: Anna Tan, it’s been wonderful to have you on the show. Thank you very much for all your thoughts.

Anna: Absolute pleasure. Thank you very much, Julia.

Julia: And it’s been a fantastic conversation about bias. Whether you think about it from a recruitment lens, you think about it from a corporate lens, from a data and artificial intelligence as well. Thanks to Anna and to Rana for joining me today. I hope you’ve enjoyed the discussion as much as I have. Thank you for listening as always to DiverCity Podcast I’ve been Julia Streets.

Kieron: This episode of DiverCity Podcast was produced by me, Kieron Yates, on behalf of Julia Streets Productions. 

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