I feel that this view, about the existential threat that robots are going to take over humanity, takes away our agency as humans. At the end of the day, we’re designing these systems, and we get to say how they are deployed, we can turn the switch off.
CEO & CO-FOUNDER OF AFFECTIVA
Rana el Kaliouby is the co-founder and CEO of Affectiva, a startup company that specializes in AI systems that sense and understand human emotions. Affectiva is developing cutting-edge AI technologies that apply machine learning, deep learning, and data science to bring new levels of emotional intelligence to AI. Rana is an active participant in international forums that focus on ethical issues and the regulation of AI to help ensure the technology has a positive impact on society. She was selected as a Young Global Leader by the World Economic Forum in 2017.
MARTIN FORD: I want to begin by exploring your background; I’m especially interested in how you became involved with AI and how your trajectory went from an academic background to where you are today with your company, Affectiva.
RANA EL KALIOUBY: I grew up around the Middle East, being born in Cairo, Egypt and spending much of my childhood in Kuwait. During this time, I found myself experimenting with early computers, as a result of both my parents being in technology, and my dad would bring home the old Atari machines where we would pick them apart. Fast-forward and that grew into my undergraduate course where I majored in Computer Science at the American University in Cairo. I guess you could say this is where the thinking behind Affectiva first came into play. During this time, I became fascinated by how technology changes how humans connect with one another. Nowadays a lot of our communication is mediated via technology, and so the special way that we connect with technology, but also with one another, fascinates me.
The next step was to do a PhD. I received a scholarship to work with the Computer Science department at Cambridge University, which, on a side note, was something that was quite unusual for a young Egyptian and Muslim woman to do. This was in the year 2000, so it was before we all had smartphones, but at the time I was quite interested in this idea of human-computer interaction and how our interface is going to evolve over the next few years.
Through my own experience, I realized that I was spending a lot of time in front of my machine, where I was coding and writing all these research papers, which opened me to two realizations. The first realization was that the laptop I was using (remember no smartphones yet) was supposedly quite intimate with me. I mean, I was spending a lot of hours with it, and while it knew a lot of things about me—like if I was writing a Word document or coding—it had no idea how I was feeling. It knew my location, it knew my identity, but it was just completely oblivious to my emotional and cognitive state.
In that sense my laptop reminded me of Microsoft Clippy, where you would be writing a paper, and then this paper-clip would show up, do a little twirl, and it would say, “Oh, it looks like you’re writing a letter! Do you need any help?” Clippy would often show up at the weirdest times, for example when I was super-stressed and my deadline was in 15 minutes... and the paperclip would do its funny little cheesy thing. Clippy helped me realize that we have an opportunity here, because there’s an emotional intelligence gap with our technology.
The other thing that was kind of very clear is that this machine mediated a lot of my communication with my family back home. During my PhD, there were times when I was that homesick, and I would be chatting with my family in tears, and yet they’d have no idea because I was hiding behind my screen. It made me feel very lonely and I realized how all of the rich non-verbal communications that we have when we’re face to face, in a phone conversation or a video conference, are all lost in cyberspace when we are interacting digitally.
MARTIN FORD: So, your own life experiences led you to become interested in the idea of technology that could understand human emotions. Did your PhD focus much on exploring this idea?
RANA EL KALIOUBY: Yes, I became intrigued by the idea that we’re building a lot of smartness into our technologies but not a lot of emotional intelligence, and this was an idea that I started to explore during my PhD. It all began during one of my very early presentations at Cambridge, where I was talking to an audience about how curious I was about how we might build computers that could read emotions. I explained during the presentation how I am, myself, a very expressive person—that I’m very attuned to people’s facial expressions, and how intriguing I found it to think about how we could get a computer to do the same. A fellow PhD student popped up and said, “Have you looked into autism because people on the autism spectrum also find it very challenging to read facial expressions and non-verbal behaviors?” As a result of that question, I ended up collaborating very closely with the Cambridge Autism Research Center during my PhD. They had an amazing dataset that they’d compiled to help kids on the autism spectrum to learn about different facial expressions.
Machine learning needs a lot of data, and so I borrowed their dataset to train the algorithms I was creating, on how to read different emotions, something that showed some really promising results. This data opened up an opportunity to focus not just on the happy/sad emotions, but also on the many nuanced emotions that we see in everyday life, such as confusion, interest, anxiety or boredom.
I could soon see that we had this tool that we could package up and provide as a training tool for individuals on the autism spectrum. This is where I realized that my work wasn’t just about improving human-computer machine interfaces, but also about improving human communication and human connection.
When I completed my PhD at Cambridge, I met with the MIT professor, Rosalind Picard, who authored the book Affective Computing, and would later co-found Affectiva with me. But back in 1998, Rosalind posited that technology needs to be able to identify human emotions and respond to those emotions.
Long story short, we ended up chatting, and Rosalind invited me to join her lab at the MIT Media Lab. The project that brought me over to the US was a National Science Foundation project that would take my technology of reading emotions and, by integrating it with a camera, we could apply it for kids on the autism spectrum.
MARTIN FORD: In one of the articles I read about you, I think you described an “emotional hearing aid” for autistic kids. Is this what you are referring to? Did that invention stay at the conceptual level or did it become a practical product?
RANA EL KALIOUBY: I joined MIT in 2006, and between then and 2009 we partnered with a school in Providence, Rhode Island, and they were focused on kids on the autism spectrum. We deployed our technology there, and we would take prototypes to the kids and have them try it, and they would say “this doesn’t feel quite right,” so we iterated the system until it began to succeed. Eventually, we were able to demonstrate that the kids who were using the technology were having a lot more eye contact, and they were doing a lot more than just looking at people’s faces.
Imagine how these kids, somewhere on the spectrum of autism, would wear these pairs of glasses with a camera facing outwards. When we first started doing this research, a lot of the camera data we got was just of the floor or the ceiling: the kids weren’t even looking at the face. But the input that we got, from working with these kids, allowed us to build real-time feedback that helped encourage them to make face contact. Once those kids started to do that, we gave them feedback on what kind of emotions people are displaying. It all looked very promising.
You’ve got to remember that Media Lab is a unique academic department at MIT, in the sense that it has very strong ties to industry, to the point where about 80% of the lab’s funding comes from Fortune 500 companies. So twice a year, we would host these companies for what we called Sponsor Week, where it was very demo-or-die because you had to actually show what you were working on. A PowerPoint wouldn’t cut it!
So, twice a year between 2006 and 2008 we’d invite all these folks over to MIT, and we would demo the autism prototype. During these kinds of events, companies like Pepsi would ask if we’d thought about applying this work to test whether advertising was effective. And Procter & Gamble wanted to use it to test its latest shower gels, because it wanted to know if people liked the smells or not. Toyota wanted to use it for driver state monitoring, and The Bank of America wanted to optimize the banking experience. We explored getting some more research assistants to help develop the ideas that our funders wanted, but we soon realized that this was not research anymore, that it was in fact a commercial opportunity.
I was apprehensive about leaving academia, but I was starting to get a little frustrated that in academia you do all these prototypes, but they never get deployed at scale. With a company, I felt we had an opportunity to scale and bring products to market, and to change how people communicate and do things on a day-to-day basis.
MARTIN FORD: It sounds like Affectiva has been very customer-driven. Many startups try to create a product in anticipation of a market being there; but in your case, the customers told you exactly what they wanted, and you responded directly to that.
RANA EL KALIOUBY: You’re absolutely right, and it quickly became apparent that we were sitting on a potentially huge commercial opportunity. Collectively, Rosalind and I felt that between us we had started this field, we were thought leaders, and that we wanted to do it in a very ethical way as well—which was core to us.
MARTIN FORD: What are you working on at Affectiva now, and what’s your overall vision for where it’s going to go in the future?
RANA EL KALIOUBY: Our overall vision is that we’re on a mission to humanize technology. We’re starting to see technology permeate every aspect of our life. We’re also starting to see how interfaces are becoming conversational, and that our devices are becoming more perceptual—and a lot more potentially relational. We’re forming these tight relationships with our cars, our phones, and our smart-enabled devices like Amazon’s Alexa or Apple’s Siri.
If you think about a lot of people who are building these devices, right now, they’re focused on the cognitive intelligence aspect of these devices, and they’re not paying much attention to the emotional intelligence. But if you look at humans, it’s not just your IQ that matters in how successful you are in your professional and personal life; it’s often really about your emotional and social intelligence. Are you able to understand the mental states of people around you? Are you able to adapt your behavior to take that into consideration and then motivate them to change their behavior, or persuade them to take action?
All of these situations, where we are asking people to take action, we all need to be emotionally intelligent to get to that point. I think that this is equally true for technology that is going to be interfacing with you on a day-to-day basis and potentially asking you to do things.
Whether that is helping you sleep better, eat better, exercise more, work more productively, or be more social, whatever that technology is, it needs to consider your mental state when it tries to persuade you to take part in them.
My thesis is that this kind of interface between humans and machines is going to become ubiquitous, that it will just be ingrained in the future human-machine interfaces, whether it’s our car, our phone or smart devices at our home or in the office. We will just be coexisting and collaborating with these new devices, and new kinds of interfaces.
MARTIN FORD: Could you sketch out some of the specific things you’re working on? I know you’re doing something with monitoring drivers in cars to make sure they are attentive.
RANA EL KALIOUBY: Yes, the issue today around monitoring drivers in cars is that there are so many situations to cater for, that Affectiva as a company has focused specifically on situations that are ethical, and where there’s a good product-market fit. And of course, for where the markets are ready.
When Affectiva started in 2009, the first kind of low-hanging market opportunities were in advertising testing, as I mentioned, and today Affectiva works with a quarter of the Fortune Global 500 companies to help them understand the emotional connection their advertising creates with their consumers.
Often, companies will spend millions of dollars to create an advertisement that’s funny or one that tugs at your heart. But they have no idea if they struck the right emotional chord with you. The only way that they could find that sort of thing out, before our technology existed, was to ask people. So, if you, Martin Ford, were the person watching the ad, then you’d get a survey, and it would say, “Hey, did you like this ad? Did you think it was funny? Are you going to buy the product?” And the problem with that is that it’s very unreliable and very biased data.
So now, with our technology, as you’re watching the ad, with your consent it will analyze on a moment-by-moment basis all your facial expressions and aggregate that over the thousands of people who watched that same ad. The result is an unbiased, objective set of data around how people respond emotionally to the advertising. We can then correlate that data with things like customer purchase intent, or even actual sales data and virality.
Today we have all these KPIs that can be tracked, and we’re able to tie the emotional response to actual consumer behavior. That’s a product of ours that’s in 87 countries, from the US and China to India, but also smaller countries like Iraq and Vietnam. It’s a pretty robust product at this point, and it’s been amazing because it allows us to collect data from all over the world, and it’s all very spontaneous data. It’s data that, I would argue, even Facebook and Google don’t have because it’s not just your profile picture, it’s you sitting in your bedroom one night, watching a shampoo ad. That’s the data we have, and that’s what drives our algorithm.
MARTIN FORD: What are you analyzing? Is it mostly based on facial expressions or also on other things like voice?
RANA EL KALIOUBY: Well, when we first started, we worked with just the face, but about eighteen months ago we went back to the drawing board and asked: how do we as humans monitor the responses of other humans?
People are pretty good at monitoring the mental states of the people around them, and we know that about 55% of the signals we use are in facial expression and your gestures, while about 38% of the signal we respond to is from tone of voice. So how fast someone is speaking, the pitch, and how much energy is in the voice. Only 7% of the signal is in the text and the actual choice of words that someone uses!
Now when you think of the entire industry of sentiment analysis, the multi-billion-dollar industry of people listening to tweets and analyzing text messages and all that, it only accounts for 7% of how humans communicate. What I like to think about what we’re doing here, is trying to capture the other 93% of non-verbal communication.
So, back to your questions: about eighteen months ago I started a speech team that looks at these prosodic paralinguistic features. They would look at the tone of voice and the occurrence of speech events, such as how many times you say “um” or how many times you laughed. All of these speech events are independent of the actual words that we’re saying. Affectiva technology now combines these things and takes what we call a multimodal approach, where different modalities are combined, to truly understand a person’s cognitive, social or emotional state.
MARTIN FORD: Are the emotional indicators you look for consistent across languages and cultures, or are there significant differences between populations?
RANA EL KALIOUBY: If you take facial expressions or even the tone of a person’s voice, the underlying expressions are universal. A smile is a smile everywhere in the world. However, we are seeing this additional layer of cultural display norms, or rules, that depict when people portray their emotions, or how often, or how intensely they show their emotion. We see examples of people amplifying their emotions, dampening their emotions, or even masking their emotions altogether. We particularly see signs of masking in Asian markets, where Asian populations are less likely to show negative emotions, for instance. So, in Asia we see an increased incidence of what we call a social smile, or a politeness smile. Those are not expressions of joy, but are more themed around saying, “I acknowledge you,” and in that sense they are a very social signal.
By and large, everything is universal. There are cultural nuances, of course, and because we have all this data, we’ve been able to build region-specific and sometimes even country-specific norms. We have so much data in China, for instance, that China is its own norm. Instead of comparing a Chinese individual’s response to say, a chocolate ad, we compare a Chinese individual to the subpopulation that’s most like them. And this particular approach has been critical to our success in monitoring emotional states in different cultures around the world.
MARTIN FORD: I guess then that other applications you’re working on are oriented toward safety, for example monitoring drivers or the operators of dangerous equipment to make sure they stay attentive?
RANA EL KALIOUBY: Absolutely. In fact in the last year we’ve started to get a ton of inbound interest from the automotive industry. It’s really exciting because it’s a major market opportunity for Affectiva and we’re solving two interesting problems for the car industry.
In the cars of today, where there is an active driver, safety is a huge issue. And safety will continue to be an issue, even when we have semi-autonomous vehicles like Tesla that can drive themselves for a while but do still need a co-pilot to be paying attention.
Using Affectiva software, we’re able to monitor the driver or the co-pilot for things like drowsiness, distraction, fatigue and even intoxication. In the case of intoxication, we would alert the driver or also even potentially have the car intervene. Intervention could be anything from changing the music to blasting a little bit of cold air, or tightening the seat belt, all the way to potentially saying, “You know what? I’m the car, and I feel I could be a safer driver than you are right now. I’m taking control over.” There’s a lot of actions the car can take once it understands the level of attention and how impaired a driver is. So, that’s one class of use cases.
The other problem we’re solving for the automotive industry is around the occupant experience. Let’s look into the future where we have fully autonomous vehicles and robot-taxis, where there’s no driver in the car at all. In those situations, the car needs to understand the state of the occupants such as, how many people are in the car, what’s their relationship, are they in a conversation, or even do we have a baby in the car that’s potentially getting left behind? Once you understand the mood of the occupants in the car, you can personalize the experience.
The robot-taxi could make product recommendations or route recommendations. This would also introduce new business models for auto companies, especially premium brands like a BMW or a Porsche, because right now they’re all about the driving experience. But in the future, it’s not going to be about driving anymore: it’s going to be about transforming and redefining that transport, that mobility experience. Modern transport is a very exciting market, and we’re spending a lot of our mindshare building products for that industry, and also for those partnered with Tier 1 companies.
MARTIN FORD: Do you see potential applications in healthcare? Given that we do have a mental health crisis, I wonder if you think the kind of technology you’re building at Affectiva might help in areas like counseling?
RANA EL KALIOUBY: Healthcare is probably what I’m most excited about, because we know that there are facial and vocal biomarkers of depression, and we know that there are signs that could be predictive of suicidal intent in a person. Think about how often we are in front of our devices and our phones, that’s an opportunity to collect very objective data.
Right now, you can only ask a person, on a scale from 1 to 10, how depressed they are, or how suicidal they are. It’s just not accurate. But we now have the opportunity to collect data at scale and build a baseline model of who someone is and what their baseline mental state or mental health state is. Once we have that data, if someone starts to deviate from their normal baseline, then a system can signal that to the person themselves, to their family members or even maybe a healthcare professional.
Then imagine how we could use these same metrics to analyze the efficacy of different treatments. The person could try cognitive behavioral therapy or certain drugs, and we would be able to quantify, very accurately and very objectively over time, if those treatments were effective or not. I feel that there’s a real potential here to understand anxiety, stress, and depression, and be able to quantify it.
MARTIN FORD: I want to move into a discussion about the ethics of AI. It’s easy to think of things that people might find disturbing about this kind of technology. For example, during a negotiation, if your system was secretly watching someone and giving the other side information about their responses, that would create an unfair advantage. Or it could be used for some form of wider workplace surveillance. Monitoring someone when they’re driving to make sure they’re attentive would probably be okay with most people, but they might feel very different about the idea of your system watching an office worker sitting in front of a computer. How do you address those concerns?
RANA EL KALIOUBY: There’s a little history lesson here about when Rosalind, myself, and our first employee met around Rosalind’s kitchen table and we were thinking: Affectiva is going to get tested, so what are our boundaries and what’s non-negotiable? In the end, we landed on this core value of respecting that people’s emotions are a very personal type of data. From then on, we agreed that we would only take on situations where people are explicitly consenting and opting in to share that data. And, ideally, where they’re also getting some value in return for sharing that data.
These are things that Affectiva has been tested on. In 2011, we were running low on funds, but we had the opportunity for funding from a security agency that had a venture arm, and it was very interested in using the technology for surveillance and security. Even though most people know that when they go to an airport, they’re being watched, we just felt that this was not in line with our core value of consent and opt-in, so we declined the offer even though the money was there. At Affectiva, we’ve stayed away from applications where we feel that people aren’t necessarily opting in and the value equation is not balanced.
When you think about the applications around the workplace, this question does become very interesting because the same tool could be used in ways that might be very empowering—or of course, very like Big Brother. I do think it would be super-interesting if people wanted to opt-in, anonymously, and employers were able to then get a sentiment score, or just an overall view, of whether people are stressed in the office—or whether people are engaged and happy.
Another great example would be where a CEO is giving a presentation, to people dialed in from around the world, and the machine indicates whether or not the message is resonating as they CEO intends. Are the goals exciting? Are people motivated? These are core questions that if we’re all co-located, it would be easy to collect; but now, with everybody distributed, it’s just really hard to get a sense of these things. However, if you turn it around and use the same technology to say, “OK. I’m going to pick on a certain member of staff because they seemed really disengaged,” then that’s a total abuse of the data.
Another example would be where we have a version of the technology that tracks how meetings go, and at the end of every meeting, it can give people feedback. It would give you feedback like, “you rambled for 30 minutes, and you were pretty hostile towards so-and-so, you should be a little bit more thoughtful or more empathetic.” You can easily imagine how this technology could be used as a coach to help staff negotiate better or be a more thoughtful team member; but at the same time, you could use it to hurt people’s careers.
I would like to think of us as advocating for situations where people can get the data back, and they can learn something about it, and it could help them advance their social and emotional intelligence skills.
MARTIN FORD: Let’s delve into the technology you’re using. I know that you use deep learning quite heavily. How do you feel about that as a technology? There has been some recent pushback, with some people suggesting that progress in deep learning is going to slow or even hit a wall, and that another approach will be needed. How do you feel about the use of neural networks and how they’re going to evolve in the future?
RANA EL KALIOUBY: Back when I did my PhD, I used dynamic Bayesian networks to quantify and build these classifiers. Then a couple of years ago we moved all our science infrastructure to be deep learning-based, and we have absolutely reaped the benefits of that.
I would say that we haven’t even maxed out yet on deep learning. With more data combined with these deep neural nets, we see increases in the accuracy and robustness of our analysis across so many different situations.
Deep learning being awesome, I don’t think that it’s the be-all, end-all to all of our needs. It’s still pretty much supervised, so you still need to have some labeled data to track these classifiers. I think of it as an awesome tool within this bigger bucket of machine learning, but deep learning is not going to be the only tool that we use.
MARTIN FORD: Thinking more generally now, let’s talk about the march towards artificial general intelligence. What are the hurdles involved? Is AGI something that is feasible, realistic or even something you expect to see in your lifetime?
RANA EL KALIOUBY: We’re many, many, many, many, many years away from an AGI and the reason I say that is because when you look at all the examples of AI that we have today, all of them are pretty narrow. Today’s AI does one thing well, but they all had to be bootstrapped in one way or another, even if they learned how to play a game from scratch.
I think there are sub-assumptions, or some level of sub-curation, that happens with the dataset, which has allowed that algorithm to learn whatever it learns, and I don’t think that we’ve yet figured out how to give it human-level intelligence.
Even if you look at the best natural language processing system that we have today, and you give it something like a third-grade test, it doesn’t pass.
MARTIN FORD: What are your thoughts about the intersection between AGI and emotion? A lot of your work is primarily focused on getting machines to understand emotion, but flipping the coin, what about having a machine that exhibits emotion? Do you think that’s an important part of what AGI would be, or do you imagine a zombie-like machine that has no emotional sense at all?
RANA EL KALIOUBY: I would say that we are already there, right now, in terms of machines exhibiting emotions. Affectiva has developed an emotion-sensing platform, and a lot of our partners use this sensing platform to actuate machine behavior. Whether that technology is a car, or a social robot, an emotion-sensing platform can take our human metrics as input, and that data can be used to decide how a robot is going to respond. Those responses could be the things that a robot says from our stimuli, just like Amazon Alexa responds today.
Of course, if you’re asking Amazon Alexa to order something and it keeps getting it wrong, then you’re now getting annoyed. But instead of Alexa just being completely oblivious to all of that, your Alexa device could say, “OK, I’m sorry. I realize I’m getting this wrong. Let me try again.” Alexa could acknowledge our level of frustration and it could then incorporate that into its response, and into what it actually does next. A robot could move its head, it could move around, it could write, and it could exhibit actions that we would translate into, “Oh! It looks like it’s sorry.”
I would argue that machine systems are already incorporating emotional cues in their actions, and that they can portray emotions, in any way that someone designs them to do so. That is quite different, of course, from the device actually having emotions, but we don’t need to go there.
MARTIN FORD: I want to talk about the potential impact on jobs. How do you feel about that? Do you think that there is the potential for a big economic and job-market disruption from AI and robotics, or do you think that’s perhaps been overhyped, and we shouldn’t worry quite so much about it?
RANA EL KALIOUBY: I’d like to think of this as more of a human-technology partnership. I acknowledge that some jobs are going to cease to exist, but that’s nothing new in the history of humanity. We’ve seen that shift of jobs over and over again, and so I think there’s going to be a whole new class of jobs and job opportunities. While we can envision some of those new jobs now, we can’t envision all of them.
I don’t subscribe to the vision of a world where robots are going to take over and be in control, whilst humanity will just sit around and chill by the beach. I grew up in the Middle East during the time of the first Gulf War, so I’ve realized that there are so many problems in the world that need to be solved. I don’t think we’re anywhere close to a machine that’s just going to wake up someday and be able to solve all these problems. So, to answer your question, I’m not concerned.
MARTIN FORD: If you think about a relatively routine job, for example a customer service job in a call center, it does sound like the technology you’re creating might enable machines to do that more human element of the work as well. When I’m asked about this, which is often, I say the jobs that are most likely to be safe are the more human-oriented jobs, the ones that involve emotional intelligence. But it sounds like you’re pushing the technology into this area as well, so it does seem that there’s a very broad range of occupations that could be eventually be impacted, including some areas currently perceived as quite safe from automation.
RANA EL KALIOUBY: I think you’re right about this, and let me give an example with nurses. At Affectiva, we are collaborating with companies that are building nurse avatars for our phones, and even installing social robots in our homes, which are designed to be a companion to terminally-ill patients. I don’t think this is going to take the place of real nurses, but I do think it’s going to change how nurses do their jobs.
You can easily imagine how a human nurse could be assigned to twenty patients, and each of these patients has access to a nurse avatar or a nurse robot. The human nurse only gets brought into the loop if there is a problem that the nurse robot can’t deal with. The technology allows the nurse robot to manage so many more patients, and manage them longitudinally, in a way that’s not possible today.
There’s a similar example with teachers. I don’t think intelligent learning systems are going to replace teachers, but they are going to augment them in places where there isn’t access to enough teachers. It’s like we’re delegating these jobs to those mini-robots that could do parts of the job on our behalf.
I think this is even true for truck drivers. Nobody will be driving a truck in the next ten years, but someone is sitting at home and tele-operating 100 fleets out there and making sure that they’re all on track. There may instead be a job where someone needs to intervene, every so often, and take human control of one of them.
MARTIN FORD: What is your response to some of the fears expressed about AI or AGI, in particular by Elon Musk, who has been very vocal about existential risks?
RANA EL KALIOUBY: There’s a documentary on the internet called Do You Trust This Computer? which was partially funded by Elon Musk, and I was featured in it being interviewed.
MARTIN FORD: Yes, in fact, a couple of the other people I’ve interviewed in this book were also featured in that documentary.
RANA EL KALIOUBY: Having grown up in the Middle East, I feel that humanity has bigger problems than AI, so I’m not concerned.
I feel that this view, about the existential threat that robots are going to take over humanity, takes away our agency as humans. At the end of the day, we’re designing these systems, and we get to say how they are deployed, we can turn the switch off. So, I don’t subscribe to those fears. I do think that we have more imminent concerns with AI, and these have to do with the AI systems themselves and whether we are, through them, just perpetuating bias?
MARTIN FORD: So, you would say that bias is one of the more pressing issues that we’re currently facing?
RANA EL KALIOUBY: Yes. Because the technology is moving so fast, while we train these algorithms, we don’t necessarily know exactly what the algorithm or the neural network is learning. I fear that we are just rebuilding all the biases that exist in society by implementing them in these algorithms.
MARTIN FORD: Because the data is coming from people, so inevitably it incorporates their biases. You’re saying that it isn’t the algorithms that are biased, it’s the data.
RANA EL KALIOUBY: Exactly, it’s the data. It’s how we’re applying this data. So Affectiva, as a company, is very transparent about the fact that we need to make sure that the training data is representative of all the different ethnic groups, and that it has gender balance and age balance.
We need to be very thoughtful about how we train and validate these algorithms. This an ongoing concern, it’s always a work in progress. There is always more that we can do to guard against these kinds of biases.
MARTIN FORD: But the positive side would be that while fixing bias in people is very hard, fixing bias in an algorithm, once you understand it, might be a lot easier. You could easily make an argument that relying on algorithms more in the future might lead to a world with much less bias or discrimination.
RANA EL KALIOUBY: Exactly. One great example is in hiring. Affectiva has partnered a company called HireVue, who use our technology in the hiring process. Instead of sending a Word resume, candidates send a video interview, and by using a combination of our algorithms and natural language processing classifiers, the system ranks and sorts those candidates based on their non-verbal communication, in addition to how they answered the questions. This algorithm is gender-blind, and it’s ethnically blind. So, the first filters for these interviews do not consider gender and ethnicity.
HireVue has published a case study, with Unilever, where it shows that not only did it reduce its time to hire by 90%, but the process resulted in a 16% increase in the diversity of its incoming hiring population. I found that to be pretty cool.
MARTIN FORD: Do you think AI will need to be regulated? You’ve talked about how you’ve got very high ethical standards at Affectiva, but looking into the future, there’s a real chance that your competitors are going to develop similar technologies but perhaps not adhere to the same standards. They might accept the contract from an authoritarian state, or the corporation that wants to secretly spy on its employees or customers, even if you would not. Given this, do you think there’s going to be a need to regulate this type of technology?
RANA EL KALIOUBY: I’m a big advocate of regulation. Affectiva is part of the Partnership on AI consortium, and a member of the FATE working group, which is the Fair, Accountable, Transparent and Equitable AI.
Through working with these groups, our mandate is to develop guidelines that advocate for the equivalent of an FDA (Food and Drug Administration) process for AI. Alongside this work, Affectiva publishes best practices and guidelines for the industry. Since we are thought leaders, it is our responsibility to be an advocate for regulation, and to move the ball forward, as opposed to just saying, “Oh, yeah. We’re just going to wait until legislation comes about.” I don’t think that that’s the right solution.
I’m also a part of the World Economic Forum, on which there’s an international forum council on robotics and AI. Through working with this forum, I’ve become fascinated by the cultural differences in how different countries think about AI. A great example can be seen in China, which is part of this council. We know that the Chinese government doesn’t really care about ethics, and so it begs the question, how do you navigate that? Different nations think about AI regulation differently, which makes this difficult to answer the question.
MARTIN FORD: To end on an upbeat note, I assume you’re an optimist? That you believe these technologies are, on balance, going to be beneficial for humanity?
RANA EL KALIOUBY: Yes, I would say that I’m an optimist because I believe that technology is neutral. What matters is how we decide to use it, and I think there’s a potential for good, and we should, as an industry, follow the footsteps of my team, where we’ve decided to focus our mindshare on the positive applications of AI.
RANA EL KALIOUBY is the CEO and co-founder of Affectiva, a company focused on emotion AI. She received her undergraduate and master’s degrees from American University in Cairo, Egypt and her PhD from the Computer Lab at the University of Cambridge. She worked as a research scientist at the MIT Media Lab, where she developed technology to assist autistic children. That work led directly to the launch of Affectiva.
Rana has received a number of awards and distinctions, including selection as a Young Global Leader in 2017 by the World Economic Forum. She was also featured on Fortune Magazine’s 40 under 40 and TechCrunch’s 40 Female founders who crushed it in 2016 lists.