AI and the Future of Lie Detection

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We live in a world now where we know how to lie. With advances in AI, it is very likely that we will soon live in a world where we know how to detect truth. The potential scope of this technology is vast — the question is how should we use it?

Some people are naturally good liars, and others are naturally good lie detectors. For example, individuals who fit the latter description can often sense lies intuitively, observing fluctuations in pupil dilation, blushing, and a variety of micro-expressions and body movements that reveal what’s going on in someone else’s head. This is because, for the vast majority of us who are not trained deceivers, when we lie, or lie by omission, our bodies tend to give us away.

For most of us, however, second guessing often overtakes intuition about whether someone is lying. Even if we are aware of the factors that may indicate a lie, we are unable to simultaneously observe and process them in real time — leaving us, ultimately, to guess whether we are hearing the truth.

Now suppose we did not have to be good lie detectors, because we would have data readily available to know if someone was lying or not. Suppose that, with this data, we could determine with near-certainty the veracity of someone’s claims. We live in a world now where we know how to lie. With advances in AI, it is very likely that we will soon live in a world where we know how to detect truth. The potential scope of this technology is vast — the question is how should we use it?

The Future of AI Lie Detection  

Imagine anyone could collect more than just wearable data showing someone’s (or their own) heartbeat, but continuous data on facial expressions from video footage, too. Imagine you could use that data, with a bit of training, to analyze conversations and interactions from your daily life — replaying ones you found suspicious with a more watchful gaze. Furthermore, those around you could do the same: imagine a friend, or company, could use your past data to reliably differentiate between your truths and untruths, matters of import and things about which you could not care less.

This means a whole new toolkit for investigators, for advertisers, for the cautious, for the paranoid, for vigilantes, for anyone with internet access. Each of us will have to know and understand how to manage and navigate this new data-driven public record of our responses.

The issue for the next years is not whether lying will be erased — of course it will not —but rather, how these new tools should be wielded in the pursuit of finding the truth. Moreover, with a variety of potential ways of mis-reading and misusing these technologies, in what contexts should they be made available, or promoted?

The Truth About Knowing the Truth

Movies often quip about the desire to have a window into someone else’s brain, to feel assured that what they say describes what they feel, that what they feel describes what they will do, and what they will do demonstrates what everything means for them. Of course, we all know the world is not so neat, and one might fall prey to searching for advice online. What happens when such advice is further entrenched in a wave of newly available, but poorly understood, data?

What will happen, for example, when this new data is used in the hiring process, with candidates weeded out by software dedicated to assessing whether and about what they’ve lied during an interview? What will happen when the same process is used for school selection, jury selection, and other varieties of interviews, or when the results are passed along to potential employers. As the number of such potential scenarios grows, the question we have to ask is when is our heartbeat private information?

Is knowledge of our internal reactions itself private, simply because until now only a small segment of perceptive people could tell what was happening? Communities often organize around the paths of least resistance, creating a new divide between those who understand and can navigate this new digital record, and those who cannot.

Imagine therapists actively recording cognitive dissonance, news shows identifying in real time whether or not a guest believes what they are saying, companies reframing interviews with active facial analysis, quick border security questioning. The expanding scope of sensors is pushing us away from post-truth to an age of post-lying, or rather, an end to our comfort with the ways in which we currently lie. As with everything, the benefits will not be felt equally.

We might even be able to imagine the evolution of lie detection moving towards brain-computer interfaces — where one’s right to privacy must then be discussed in light of when we can consider our thoughts private.

In court rooms, if we can reliably tell the difference between reactions during a lie and during the truth, do witnesses have a right to keep that information private? Should all testimonials be given in absolute anonymity? Researchers at the University of Maryland developed DARE, the Deception Analysis and Reasoning Engine, which they expect to be only a few years away from near perfect deception identification.

How then should we think about the 5th amendment of the US constitution, how should we approach the right to not incriminate oneself? With the advent of these technologies, perhaps the very nature of the courtroom should change. Witnesses are not given a polygraph on the stand for good reason: it’s unreliable — but there may be little stopping someone with a portable analytics system to tell their vitals or analyze a video feed from a distance, and publish the results for the court of public opinion. How should our past behavior be recorded and understood?

How we design nudges, how we design public spaces, how we navigate social situations, job offers, personal relationships, all depend on a balance of social convention by which we allow ourselves — and others — to hide information. Yet, what should we do with a technology that promises to expose this hidden information? Is a world based on full-truths preferable to the one we have now? Will we have a chance to decide?

Advances in AI and the democratization of data science are making the hypothetical problem of what kind of world we prefer an all too real discussion, one we need to have soon. Otherwise we’ll have no say in determining what lies ahead.

Josh Entsminger

Josh Entsminger is an applied researcher at Nexus Frontier Tech. He additionally serves as a senior fellow at Ecole Des Ponts Business School’s Center for Policy and Competitiveness, a research associate at IE business school’s social innovation initiative, and a research contributor to the world economic forum’s future of production initiative.

Mark Esposito

Mark Esposito is a member of the Teaching Faculty at the Harvard University's Division of Continuing, a Professor of business and economics, with an appointment at Hult International Business School. He is an appointed Research Fellow in the Circular Economy Center, at the University of Cambridge's Judge Busines School. He is also a Fellow for the Mohammed Bin Rashid School of Government in Dubai. At Harvard, Mark teaches Systems Thinking and Complexity, Economic Strategy and Business, Government & Society for the Extension and Summer Schools and serves as Institutes Council Co-Leader, at the Microeconomics of Competitiveness program (MOC) developed at the Institute of Strategy and Competitiveness, at Harvard Business School. He is Founder & Director of the Lab-Center for Competitiveness, a think tank affiliated with the MOC network of Prof. Michael Porter at Harvard Business School and Head of the Political Economy and Sustainable Competitiveness Initiative. He researches the "Circular Economy" inside out and his work on the topic has appeared on top outlets such as The Guardian, World Economic Forum, Harvard Business Review, California Management Review, among others. He is the co-founder of the concepts of "Fast Expanding Markets" and "DRIVE", which represent new lenses of growth detection at the macro, meso and micro levels of the economy. He is also an active entrepreneur and co-founded Nexus FrontierTech, an Artificial Intelligence Studio, aimed at providing AI solutions to a large portfolio of clients. ​He was named one of the emerging tomorrow's thought leaders most likely to reinvent capitalism by Thinkers50, the world’s premier ranking of management thinkers and inducted into the "Radar" of the 30 most influential thinkers, on the rise.

Terence Tse

Terence is a co-founder & managing director of Nexus Frontier Tech: An AI Studio. He is also an Associate Professor of Finance at the London campus of ESCP Europe Business School. Terence is the co-author of the bestseller Understanding How the Future Unfolds: Using DRIVE to Harness the Power of Today’s Megatrends. He also wrote Corporate Finance: The Basics. In addition to providing consulting to the EU and UN, Terence regularly provides commentaries on the latest current affairs and market developments in the Financial Times, the Guardian and the Economist, as well as through CNBC and the World Economic Forum. He has also appeared on radio and television shows and delivered speeches at the UN, International Monetary Fund and International Trade Centre. Invited by the Government of Latvia, he was a keynote speaker at a Heads of Government Meeting, alongside the Premier of China and Prime Minister of Latvia. Terence has also been a keynote speaker at corporate events in India, Norway, Qatar, Russia and the UK. Previously, Terence worked in mergers and acquisitions at Schroders, Citibank and Lazard Brothers in Montréal and New York. He also worked in London as a consultant at EY, focusing on UK financial services. He obtained his PhD from the Judge Business School at the University of Cambridge.

Danny Goh

Danny is a serial entrepreneur and an early stage investor. He is the partner and Commercial Director of Nexus Frontier Tech, an AI advisory business with presence in London, Geneva, Boston and Tokyo to assist CEO and board members of different organisations to build innovative businesses taking full advantage of artificial intelligence technology.
 Danny has also co-founded Innovatube, a technology group that operates a R&D lab in software and AI developments, invests in early stage start-ups with 20+ portfolios, and acts as an incubator to foster the local start-up community in South East Asia. Innovatube labs have a team of researches and engineers to develop cutting edge technology to help start-ups and enterprises bolster their operation capabilities. Danny currently serves as an Entrepreneurship Expert with the Entrepreneurship centre at the Said Business School, University of Oxford and he is an advisor and judge to several technology start-ups and accelerators including Startupbootcamp IoT London. Danny has lived in four different continents in the last 20 years in Sydney, Kuala Lumpur, Boston and London, and constantly finds himself travelling.