Description du poste
In principle, machine learning approaches could produce lie detectors far more reliable than the polygraph. If this happens soon, it will matter a great deal: reliable lie detection could make loyalty purges much more effective, create a useful check on political and AI-lab leaders, make international treaties more robust, and make authoritarian regimes more stable.
I argue the balance between these effects depends largely on cooperation cost: how much willing participation a detector needs from its subject. In particular, lie detection technology with extremely low cooperation cost is better to accelerate than lie detection technology with higher cooperation cost. I remain agnostic here about whether we should, in general, accelerate lie detector technology—this very well might be net-negative. But if we do, we should accelerate low-cooperation-cost lie detection and ensure it is widely available.
In this article, I’ll describe cooperation cost and argue that remote lie detectors strike a better balance of benefits and drawbacks. Then, I’ll explore the most important effects in more depth and see how they differ for remote and in-person lie detectors. Lastly, I’ll consolidate this exploration and touch on some other factors.
Cooperation costLie detector technology could operate on many different input modalities, some of which require more willing participation from the subject. For example, lie detection on written text has very little cooperation cost; it only requires the subject to send writing or have publicly available writing. Conversely, lie detection based on an invasive neural device has extremely high cooperation cost. From lowest to highest cooperation cost, lie detectors could operate on:
• Written text• Audiovisual data• Polygraph-like biometric data• Neural data from a wearable, mobile device• Neural data from a fixed device (e.g., fMRI)• Neural data from an invasive device, requiring surgery• (and many other things and combinations of things)There