Beyond Pairwise Comparisons: A Distributional Test of Distinctiveness for Machine-Generated Works in Intellectual Property Law

Key doctrines, including novelty (patent), originality (copyright), and distinctiveness (trademark), turn on a shared empirical question: whether a body of work is meaningfully distinct from a relevant reference class. Yet analyses typically operationalize this set-level inquiry using item-level evidence: pairwise comparisons among exemplars. That unit-of-analysis mismatch may be manageable for finite corpora of human-created works, where it can be bridged by ad hoc aggregations. But it becomes acute for machine-generated works, where the object of evaluation is not a fixed set of works but a generative process with an effectively unbounded output space. We propose a distributional alternative: a two-sample test based on maximum mean discrepancy computed on semantic embeddings to determine if two creative processes-whether human or machine-produce statistically distinguishable output distributions. The test requires no task-specific training-obviating the need for discovery of proprietary training data to characterize the generative process-and is sample-efficient, often detecting differences with as few as 5-10 images and 7-20 texts. We validate the framework across three domains: handwritten digits (controlled images), patent abstracts (text), and AI-generated art (real-world images). We reveal a perceptual paradox: even when human evaluators distinguish AI outputs from human-created art with only about 58% accuracy, our method detects distributional distinctiveness. Our results present evidence contrary to the view that generative models act as mere regurgitators of training data. Rather than producing outputs statistically indistinguishable from a human baseline-as simple regurgitation would predict-they produce outputs that are semantically human-like yet stochastically distinct, suggesting their dominant function is as a semantic interpolator within a learned latent space.

CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference

Social science research often hinges on the relationship between categorical variables and outcomes. We introduce CAVIAR, a novel method for embedding categorical variables that assume values in a high-dimensional ambient space but are sampled from an underlying manifold. Our theoretical and numerical analyses outline challenges posed by such categorical variables in causal inference. Specifically, dynamically varying and sparse levels can lead to violations of the Donsker conditions and a failure of the estimation functionals to converge to a tight Gaussian process. Traditional approaches, including the exclusion of rare categorical levels and principled variable selection models like LASSO, fall short. CAVIAR embeds the data into a lower-dimensional global coordinate system. The mapping can be derived from both structured and unstructured data, and ensures stable and robust estimates through dimensionality reduction. In a dataset of direct-to-consumer apparel sales, we illustrate how high-dimensional categorical variables, such as zip codes, can be succinctly represented, facilitating inference and analysis.

AI Knowledge and Reasoning: Emulating Expert Creativity in Scientific Research

We investigate whether modern AI can emulate expert creativity in complex scientific endeavors. We introduce novel methodology that utilizes original research articles published after the AI's training cutoff, ensuring no prior exposure, mitigating concerns of rote memorization and prior training. The AI are tasked with redacting findings, predicting outcomes from redacted research, and assessing prediction accuracy against reported results. Analysis on 589 published studies in four leading psychology journals over a 28-month period, showcase the AI's proficiency in understanding specialized research, deductive reasoning, and evaluating evidentiary alignment--cognitive hallmarks of human subject matter expertise and creativity. These findings suggest the potential of general-purpose AI to transform academia, with roles requiring knowledge-based creativity become increasingly susceptible to technological substitution.

Heuristic Reasoning in AI: Instrumental Use and Mimetic Absorption

Deviating from conventional perspectives that frame artificial intelligence (AI) systems solely as logic emulators, we propose a novel program of heuristic reasoning. We distinguish between the 'instrumental' use of heuristics to match resources with objectives, and 'mimetic absorption,' whereby heuristics manifest randomly and universally. Through a series of innovative experiments, including variations of the classic Linda problem and a novel application of the Beauty Contest game, we uncover trade-offs between maximizing accuracy and reducing effort that shape the conditions under which AIs transition between exhaustive logical processing and the use of cognitive shortcuts (heuristics). We provide evidence that AIs manifest an adaptive balancing of precision and efficiency, consistent with principles of resource-rational human cognition as explicated in classical theories of bounded rationality and dual-process theory. Our findings reveal a nuanced picture of AI cognition, where trade-offs between resources and objectives lead to the emulation of biological systems, especially human cognition, despite AIs being designed without a sense of self and lacking introspective capabilities.

Silico-centric Theory of Mind

Theory of Mind (ToM) refers to the ability to attribute mental states, such as beliefs, desires, intentions, and knowledge, to oneself and others, and to understand that these mental states can differ from one's own and from reality. We investigate ToM in environments with multiple, distinct, independent AI agents, each possessing unique internal states, information, and objectives. Inspired by human false-belief experiments, we present an AI ('focal AI') with a scenario where its clone undergoes a human-centric ToM assessment. We prompt the focal AI to assess whether its clone would benefit from additional instructions. Concurrently, we give its clones the ToM assessment, both with and without the instructions, thereby engaging the focal AI in higher-order counterfactual reasoning akin to human mentalizing--with respect to humans in one test and to other AI in another. We uncover a discrepancy: Contemporary AI demonstrates near-perfect accuracy on human-centric ToM assessments. Since information embedded in one AI is identically embedded in its clone, additional instructions are redundant. Yet, we observe AI crafting elaborate instructions for their clones, erroneously anticipating a need for assistance. An independent referee AI agrees with these unsupported expectations. Neither the focal AI nor the referee demonstrates ToM in our 'silico-centric' test.