Vice reports on a Tokyo-based company, DeepScore, pitching software for the automatic recognition of ‘trustworthiness’, e.g. in loan applicants. Although their claimed false-negative rate of 30% may not sound particularly impressive, it must of course be compared to well-known human biases in lending decisions. Perhaps more interesting is the instrumentalization cycle, which is all but assured to take place if DeepScore’s algorithm gains wide acceptance. On the one hand, the algorithm’s goal is to create a precise definition for a broad and vague human characteristic like trustworthiness—that is to say, to operationalize it. Then, if the algorithm is successful on its training sample and becomes adopted by real-world decision-makers, the social power of the adopters reifies the research hypothesis: trustworthiness becomes what the algorithm says it is (because money talks). Thus, the behavioral redefinition of a folk psychology concept comes to fruition. On the other hand, however, instrumentalization immediately kicks in, as users attempt to game the operationalized definition, by managing to present the algorithmically-approved symptoms without the underlying condition (sincerity). Hence, the signal loses strength, and the cycle completes. The fact that DeepScore’s trustworthiness algorithm is intended for credit markets in South-East Asia, where there exist populations without access to traditional credit-scoring channels, merely clarifies the ‘predatory inclusion’ logic of such practices (v. supra).
Josephine Wolff (Slate) reports on the recent hack of the water processing plant in Oldsmar, FL. Unknown intruders remotely accessed the plant’s controls and attempted to increase the lye content of the town’s water supply to potentially lethal levels. The case is notable in that the human fail-safe (the plant operator on duty) successfully counterbalanced the machine vulnerability, catching the hack as it was taking place and overriding the automatic controls, so no real-world adverse effects ultimately occurred.
What moral can be drawn? It is reasonable to argue, as Wolff does, against full automation: human supervision still has a critical role to play in the resiliency of critical control systems through human-machine redundancy. However, what Wolff does not mention is that this modus operandi may itself be interpreted as a signature of sorts (although no attribution has appeared in the press so far): it speaks of amateurism or of a proof-of-concept stunt; in any case, of an actor not planning to do any serious damage. Otherwise, it is highly improbable that there would have been no parallel attempt at social engineering of (or other types of attacks against) on-site technicians. After all, as the old security engineering nostrum states, rookies target technology, pros target people.
I am following (just like everyone else) the developing GameStop story. Beyond the financial technicalities, what is interesting for present purposes is that the dynamics of internet virality seem to be finding a close parallel in stock valuation. The term “meme stock” is telling. In other words, at present the online coordination mechanisms, the capital, and the nihilistic boredom are all available to craft an alternative description of reality, which in turn is self-reinforcing (until it isn’t).
I just read an interesting piece in the Harvard Business Review by three researchers at UC Berkeley’s Center for Long-Term Cybersecurity on how to communicate about risk. It is helpful as a pragmatic, concrete proposal on how to handle institutional communication about fundamentally uncertain outcomes in such a way as to bolster public trust and increase mass literacy about risk.