Large-scale diffusion neural networks represent a substantial milestone in text-to-image generation, but they remain poorly understood, lacking interpretability analyses. In this paper, we perform a text-image attribution analysis on Stable Diffusion, a recently open-sourced model. To produce pixel-level attribution maps, we upscale and aggregate cross-attention word-pixel scores in the denoising subnetwork, naming our method DAAM. We evaluate its correctness by testing its semantic segmentation ability on nouns, as well as its generalized attribution quality on all parts of speech, rated by humans. We then apply DAAM to study the role of syntax in the pixel space, characterizing head--dependent heat map interaction patterns for ten common dependency relations. Finally, we study several semantic phenomena using DAAM, with a focus on feature entanglement, where we find that cohyponyms worsen generation quality and descriptive adjectives attend too broadly. To our knowledge, we are the first to interpret large diffusion models from a visuolinguistic perspective, which enables future lines of research. Our code is at https://github.com/castorini/daam.

End-to-end automatic speech recognition systems represent the state of the art, but they rely on thousands of hours of manually annotated speech for training, as well as heavyweight computation for inference. Of course, this impedes commercialization since most companies lack vast human and computational resources. In this paper, we explore training and deploying an ASR system in the label-scarce, compute-limited setting. To reduce human labor, we use a third-party ASR system as a weak supervision source, supplemented with labeling functions derived from implicit user feedback. To accelerate inference, we propose to route production-time queries across a pool of CUDA graphs of varying input lengths, the distribution of which best matches the traffic's. Compared to our third-party ASR, we achieve a relative improvement in word-error rate of 8% and a speedup of 600%. Our system, called SpeechNet, currently serves 12 million queries per day on our voice-enabled smart television. To our knowledge, this is the first time a large-scale, Wav2vec-based deployment has been described in the academic literature.

Algorithmic bias is the systematic preferential or discriminatory treatment of a group of people by an artificial intelligence system. In this work we develop a random-effects based metric for the analysis of social bias in supervised machine learning prediction models where model outputs depend on U.S. locations. We define a methodology for using U.S. Census data to measure social bias on user attributes legally protected against discrimination, such as ethnicity, sex, and religion, also known as protected attributes. We evaluate our method on the Strategic Subject List (SSL) gun-violence prediction dataset, where we have access to both U.S. Census data as well as ground truth protected attributes for 224,235 individuals in Chicago being assessed for participation in future gun-violence incidents. Our results indicate that quantifying social bias using U.S. Census data provides a valid approach to auditing a supervised algorithmic decision-making system. Using our methodology, we then quantify the potential social biases of 100 million ridehailing samples in the city of Chicago. This work is the first large-scale fairness analysis of the dynamic pricing algorithms used by ridehailing applications. An analysis of Chicago ridehailing samples in conjunction with American Community Survey data indicates possible disparate impact due to social bias based on age, house pricing, education, and ethnicity in the dynamic fare pricing models used by ridehailing applications, with effect-sizes of 0.74, 0.70, 0.34, and -0.31 (using Cohen's d) for each demographic respectively. Further, our methodology provides a principled approach to quantifying algorithmic bias on datasets where protected attributes are unavailable, given that U.S. geolocations and algorithmic decisions are provided.