Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits

Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users’ preferences over a high-dimensional gait parameter space. However, existing preference-based learning methods have only explored low-dimensional domains due to computational limitations. To learn user preferences in high dimensions, this work presents LINECOSPAR, a human-in-the-loop preference-based framework that enables optimization over many parameters by iteratively exploring one-dimensional subspaces. Additionally, this work identifies gait attributes that characterize broader preferences across users. In simulations and human trials, we empirically verify that LINECOSPAR is a sample-efficient approach for high-dimensional preference optimization. Our analysis of the experimental data reveals a correspondence between human preferences and objective measures of dynamicity, while also highlighting differences in the utility functions underlying individual users’ gait preferences. This result has implications for exoskeleton gait synthesis, an active field with applications to clinical use and patient rehabilitation

Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models

To recognize and mitigate harms from large language models (LLMs), we need to understand the prevalence and nuances of stereotypes in LLM outputs. Toward this end, we present Marked Personas, a prompt-based method to measure stereotypes in LLMs for intersectional demographic groups without any lexicon or data labeling. Grounded in the sociolinguistic concept of markedness (which characterizes explicitly linguistically marked categories versus unmarked defaults), our proposed method is twofold: 1) prompting an LLM to generate personas, i.e., natural language descriptions, of the target demographic group alongside personas of unmarked, default groups; 2) identifying the words that significantly distinguish personas of the target group from corresponding unmarked ones. We find that the portrayals generated by GPT-3.5 and GPT-4 contain higher rates of racial stereotypes than human-written portrayals using the same prompts. The words distinguishing personas of marked (non-white, non-male) groups reflect patterns of othering and exoticizing these demographics. An intersectional lens further reveals tropes that dominate portrayals of marginalized groups, such as tropicalism and the hypersexualization of minoritized women. These representational harms have concerning implications for downstream applications like story generation.Comment: To appear at ACL 2023, 9 pages, 3 figures, 3 table

CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations

Recent work has aimed to capture nuances of human behavior by using LLMs to simulate responses from particular demographics in settings like social science experiments and public opinion surveys. However, there are currently no established ways to discuss or evaluate the quality of such LLM simulations. Moreover, there is growing concern that these LLM simulations are flattened caricatures of the personas that they aim to simulate, failing to capture the multidimensionality of people and perpetuating stereotypes. To bridge these gaps, we present CoMPosT, a framework to characterize LLM simulations using four dimensions: Context, Model, Persona, and Topic. We use this framework to measure open-ended LLM simulations' susceptibility to caricature, defined via two criteria: individuation and exaggeration. We evaluate the level of caricature in scenarios from existing work on LLM simulations. We find that for GPT-4, simulations of certain demographics (political and marginalized groups) and topics (general, uncontroversial) are highly susceptible to caricature.Comment: To appear at EMNLP 2023 (Main

Dynamics of rapidly spinning blob-filaments: fluid theory with a parallel kinetic extension

Blob-filaments (or simply 'blobs') are coherent structures formed by turbulence and sustained by nonlinear processes in the edge and scrape-off layer (SOL) of tokamaks and other magnetically confined plasmas. The dynamics of these blob-filaments, in particular their radial motion, can influence the scrape-off layer width and plasma interactions with both the divertor target and with the main chamber walls. Motivated by recent results from the XGC1 gyrokinetic simulation code reported on elsewhere [J. Cheng et al. submitted to Nucl. Fusion and available at arXiv:2302.02877v1], a theory of rapidly spinning blob-filaments has been developed. The theory treats blob filaments in the closed flux surface region or the region that is disconnected from sheaths in the SOL. It extends previous work by treating blob spin, arising from partially or fully adiabatic electrons, as the leading order effect and retaining inertial (ion charge polarization) physics in next order. Spin helps to maintain blob coherency and affects the blob's propagation speed. Dipole charge polarization, treated perturbatively, gives rise to blob-filaments with relatively slow radial velocity, comparable to that observed in the simulations. The theory also treats the interaction of rapidly spinning blob filaments with a zonal flow layer. It is shown analytically that the flow layer can act like a transport barrier for these structures. Finally parallel electron kinetic effects are incorporated into the theory. Various asymptotic parameter regimes are discussed and asymptotic expressions for the radial and poloidal motion of the blob-filaments are obtained.Comment: 31 pages, 2 figures, accepted in the journal Physics of Plasmas 30, 072302 (2023

Social Norm Bias: Residual Harms of Fairness-Aware Algorithms

Many modern machine learning algorithms mitigate bias by enforcing fairness constraints across coarsely-defined groups related to a sensitive attribute like gender or race. However, these algorithms seldom account for within-group heterogeneity and biases that may disproportionately affect some members of a group. In this work, we characterize Social Norm Bias (SNoB), a subtle but consequential type of algorithmic discrimination that may be exhibited by machine learning models, even when these systems achieve group fairness objectives. We study this issue through the lens of gender bias in occupation classification. We quantify SNoB by measuring how an algorithm's predictions are associated with conformity to inferred gender norms. When predicting if an individual belongs to a male-dominated occupation, this framework reveals that "fair" classifiers still favor biographies written in ways that align with inferred masculine norms. We compare SNoB across algorithmic fairness methods and show that it is frequently a residual bias, and post-processing approaches do not mitigate this type of bias at all.Comment: Spotlighted at the 2021 ICML Machine Learning for Data Workshop and presented at the 2021 ICML Socially Responsible Machine Learning Worksho

Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits

Understanding users' gait preferences of a lower-body exoskeleton requires optimizing over the high-dimensional gait parameter space. However, existing preference-based learning methods have only explored low-dimensional domains due to computational limitations. To learn user preferences in high dimensions, this work presents LineCoSpar, a human-in-the-loop preference-based framework that enables optimization over many parameters by iteratively exploring one-dimensional subspaces. Additionally, this work identifies gait attributes that characterize broader preferences across users. In simulations and human trials, we empirically verify that LineCoSpar is a sample-efficient approach for high-dimensional preference optimization. Our analysis of the experimental data reveals a correspondence between human preferences and objective measures of dynamic stability, while also highlighting inconsistencies in the utility functions underlying different users' gait preferences. This has implications for exoskeleton gait synthesis, an active field with applications to clinical use and patient rehabilitation

The Surveillance AI Pipeline

A rapidly growing number of voices have argued that AI research, and computer vision in particular, is closely tied to mass surveillance. Yet the direct path from computer vision research to surveillance has remained obscured and difficult to assess. This study reveals the Surveillance AI pipeline. We obtain three decades of computer vision research papers and downstream patents (more than 20,000 documents) and present a rich qualitative and quantitative analysis. This analysis exposes the nature and extent of the Surveillance AI pipeline, its institutional roots and evolution, and ongoing patterns of obfuscation. We first perform an in-depth content analysis of computer vision papers and downstream patents, identifying and quantifying key features and the many, often subtly expressed, forms of surveillance that appear. On the basis of this analysis, we present a topology of Surveillance AI that characterizes the prevalent targeting of human data, practices of data transferal, and institutional data use. We find stark evidence of close ties between computer vision and surveillance. The majority (68%) of annotated computer vision papers and patents self-report their technology enables data extraction about human bodies and body parts and even more (90%) enable data extraction about humans in general

Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale

Machine learning models are now able to convert user-written text descriptions into naturalistic images. These models are available to anyone online and are being used to generate millions of images a day. We investigate these models and find that they amplify dangerous and complex stereotypes. Moreover, we find that the amplified stereotypes are difficult to predict and not easily mitigated by users or model owners. The extent to which these image-generation models perpetuate and amplify stereotypes and their mass deployment is cause for serious concern

Anthropogenic sound exposure-induced stress in captive dolphins and implications for cetacean health

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Yang, W.-C., Chen, C.-F., Chuah, Y.-C., Zhuang, C.-R., Chen, I.-H., Mooney, T. A., Stott, J., Blanchard, M., Jen, I.-F., & Chou, L.-S. Anthropogenic sound exposure-induced stress in captive dolphins and implications for cetacean health. Frontiers in Marine Science, 8,(2021): 606736, https://doi.org/10.3389/fmars.2021.606736.Many cetaceans are exposed to increasing pressure caused by anthropogenic activities in their marine environment. Anthropogenic sound has been recognized as a possible stressor for cetaceans that may have impacts on health. However, the relationship between stress, hormones, and cytokines secretion in cetaceans is complex and not fully understood. Moreover, the effects of stress are often inconsistent because the character, intensity, and duration of the stressors are variable. For a better understanding of how anthropogenic sounds affect the psychophysiology of cetaceans, the present study compared the changes of cortisol concentration and cytokine gene transcriptions in blood samples and behaviors of captive bottlenose dolphins (Tursiops truncatus) after sound exposures. The sound stimuli were 800 Hz pure-tone multiple impulsive sound for 30 min at three different sound levels (estimated mean received SPL: 0, 120, and 140 dB re 1 μPa) that likely cause no permanent and temporary hearing threshold shift in dolphins. Six cytokine genes (IL-2Rα, IL-4, IL-10, IL-12, TNF-α, and IFN-γ) were selected for analysis. Cortisol levels and IL-10 gene transcription increased and IFNγ/IL-10 ratio was lower after a 30-min high-level sound exposure, indicating the sound stimuli used in this study could be a stressor for cetaceans, although only minor behavior changes were observed. This study may shed light on the potential impact of pile driving-like sounds on the endocrine and immune systems in cetaceans and provide imperative information regarding sound exposure for free-ranging cetaceans.This work was supported by the Ministry of Science and Technology in Taiwan (MOST 108-2313-B-002-021 and MOST 109-2628-B-002-028)