Bill Marino

Bill Marino, Nicholas D. Lane

The era of AI regulation (AIR) is upon us. But AI systems, we argue, will not be able to comply with these regulations at the necessary speed and scale by continuing to rely on traditional, analogue methods of compliance. Instead, we posit that compliance with these regulations will only realistically be achieved computationally: that is, with algorithms that run across the life cycle of an AI system, automatically steering it toward AIR compliance in the face of dynamic conditions. Yet despite their (we would argue) inevitability, the research community has yet to specify exactly how these algorithms for computational AIR compliance should behave - or how we should benchmark their performance. To fill these gaps, we specify a set of design goals for such algorithms. In addition, we specify a benchmark dataset that can be used to quantitatively measure whether individual algorithms satisfy these design goals. By delivering this blueprint, we hope to give shape to an important but uncrystallized new domain of research - and, in doing so, incite necessary investment in it.

Rui-Jie Yew, Bill Marino, Suresh Venkatasubramanian

The shape of AI regulation is beginning to emerge, most prominently through the EU AI Act (the "AIA"). By 2027, the AIA will be in full effect, and firms are starting to adjust their behavior in light of this new law. In this paper, we present a framework and taxonomy for reasoning about "avoision" -- conduct that walks the line between legal avoidance and evasion -- that firms might engage in so as to minimize the regulatory burden the AIA poses. We organize these avoision strategies around three "tiers" of increasing AIA exposure that regulated entities face depending on: whether their activities are (1) within scope of the AIA, (2) exempted from provisions of the AIA, or are (3) placed in a category with higher regulatory scrutiny. In each of these tiers and for each strategy, we specify the organizational and technological forms through which avoision may manifest. Our goal is to provide an adversarial framework for "red teaming" the AIA and AI regulation on the horizon.