Tag Archives: AI

Organizational behavior and reputation

Public red-teaming and trust

DEF CON is one of the most important hacker conferences worldwide, held yearly in Las Vegas. This coming August, it will host a large simulation, in which thousands of security experts from the private sector and academia will be invited to compete against each other to uncover flaws and bias in the generative large language models (LLMs) produced by leading firms such as OpenAI, Google, Anthropic, Hugging Face, and Stability. While in traditional red-team events the targets are bugs in the code, hardware, or human infrastructure, participants at DEF CON have additionally been instructed to seek exploits through adversarial prompt engineering, so as to induce the LLMs to return troubling, dangerous, or unlawful content.

This initiative definitely goes in the right direction in terms of building trust through verification, and bespeaks significant confidence on the part of the companies, as it can safely be expected that the media outlets in attendance will be primed to amplify any failure or embarassing shortcoming in the models’ output. There are limits, however, to how beneficial such an exercise can be. For one thing, the target constituency is limited to the extremely digitally literate (and by extension to the government agencies and private businesses the firms aspire to add to their customer list): the simulation’s outcome cannot be expected to move the needle on the broad, non-specialist perception of AI models and their risks in the public at large. Also, the stress test will be performed on customized versions of the LLMs, made available by the companies specifically for this event. The Volkswagen emissions scandal is only the most visible instance of how one may exploit such a benchmarking system. What is properly needed is the possibility of an unannounced audit of LLMs on the ground in their actual real-world applications, on the model of the Michelin Guide’s evaluation process for chefs and restaurants.

In spite of these limitations, the organization of the DEF CON simulation if nothing else proves that the leading AI developers have understood that wide-scale adoption of their technology will require a protracted engagement with public opinion in order to address doubts and respond to deeply entrenched misgivings.

Trustworthiness

Societal trust and the pace of AI research

An open letter from the Future of Life Institute exhorts the leading AI labs to enact a six-month moratorium on further experiments with artificial intelligence. The caliber of some of the early signatories guarantees that significant public conversation will ensue. Beyond the predictable hype, it is worth considering this intervention in the AI ethics and politics debate both on its merits and for what it portends more broadly for the field.

First off, the technicalities. The text locates the key chokepoint in AI development to be exploited in the interests of the moratorium in the scarcity of compute power. Truly, we are at the antipodes of the decentralized mode of innovation that drove, for instance, the original development of the commercial and personal web in the 1990s. However, it remains to be seen whether the compute power barrier has winnowed down the field into enough of an oligopoly for the proposed moratorium to have any chance of application. A closely related point is verifiability: even if there were few enough players to enable a coordination regime to emerge and there was virtually universal buy-in, it would still be necessary to enact some form of verification in order to police the system and ensure nobody is cheating. By comparison, the nuclear non-proliferation regime enjoys vast buy-in and plentiful dedicated enforcement resources (both at the nation-state and at the international organization level) and yet is far from perfect or fool-proof.

Moving to broader strategic issues, it bears considering whether the proposed moratorium, which would necessarily have to be global in scope, is in any way feasible in the current geopolitical climate. After all, one of the classic formulations of technological determinism relies on Great Power competition in military and dual-use applications. It would not be outlandish to suggest that we already are in a phase of strategic confrontation, between the United States and China among others, where the speed of tech change has become a dependent variable.

Perhaps, however, it is best to consider the second-order effects of the letter as the crux of the matter. The moratorium is extremely unlikely to come about, and would be highly unwieldy to manage if it did (the tell, perhaps, is the mismatch between the apocalyptic tone in which generative AI is described and the very short time requested to prepare for its onslaught). Nonetheless, such a proposal shifts the debate. It centers AI as the future technology to be grappled with socially, presents it as largely inevitable, and lays the responsibility for dealing with its ills at the foot of society as a whole.

Most strikingly, though, this intervention in public discourse relies on very tenuous legitimacy grounds for the various actors concerned, beginning with the drafters and signatories of the letter. Is the public supposed to endorse their analysis and support their prescriptions on the basis of their technical expertise? Or their impartiality? Or their track record of civic-mindedness? Or their expressing of preferences held by large numbers of people? All these justifications are problematic in their own way. In a low-trust environment, the authoritativeness of a public statement conducted in this fashion is bound to become itself a target of controversy.

Political economy and visibility

Tropes of the Techlash

A review by Paul Dicken published online a week ago in The New Atlantis is representative of a certain kind of argument in contemporary social critiques of high tech. The piece discusses a book by Ben Schneiderman entitled Human-Centered AI, which came out earlier this year for Oxford UP, and mainly reads as an exposé of a benighted scientism that at best is hopelessly naïve about its potential to effect meaningful emancipatory social change and at worst is disingenuous about the extractive and exploitative agendas that underwrite its deployment.

One would not wish to deny that Schneiderman makes for a good target: computer scientists as a sociological class are hardly more self-reflexive or engagé than any other similarly-defined professional group, and divulgative AI-and-management texts seldom present incisive and counterintuitive social commentary. Nonetheless, it is hard to miss a certain symmetry between the attacks on the political self-awareness of the author in question (how could he have missed the damning social implications??) and the peans to progress through techno-solutionism which characterized public debate on Web2.0 before the techlash.

The fact itself that Dicken refers back to Charles Babbage as a precursor of contemporary AI research and its dark side should suggest that the entwinement of technological advancement with political economy might be a long-run phenomenon. What is different is that in the present conjuncture would-be social critics seem to harbor absolutely no faith that the political and social ills upstream from technological development can be righted, and no plan to do so. New technology changes affordances, and this shift makes certain social dynamics more visible. But in the absence of specifically political work, such visibility is ephemeral, irrelevant. Hence, the exposé of political cluelessness risks becoming the master trope of the techlash, essentially a declaration of social impotence.