arXiv:2105.02117v1 »Full PDF »Machine learning (ML) and artificial intelligence (AI) researchers play an
important role in the ethics and governance of AI, including taking action
against what they perceive to be unethical uses of AI (Belfield, 2020; Van
Noorden, 2020). Nevertheless, this influential group's attitudes are not well
understood, which undermines our ability to discern consensuses or
disagreements between AI/ML researchers. To examine these researchers' views,
we conducted a survey of those who published in the top AI/ML conferences (N =
524). We compare these results with those from a 2016 survey of AI/ML
researchers (Grace, Salvatier, Dafoe, Zhang, & Evans, 2018) and a 2018 survey
of the US public (Zhang & Dafoe, 2020). We find that AI/ML researchers place
high levels of trust in international organizations and scientific
organizations to shape the development and use of AI in the public interest;
moderate trust in most Western tech companies; and low trust in national
militaries, Chinese tech companies, and Facebook. While the respondents were
overwhelmingly opposed to AI/ML researchers working on lethal autonomous
weapons, they are less opposed to researchers working on other military
applications of AI, particularly logistics algorithms. A strong majority of
respondents think that AI safety research should be prioritized and that ML
institutions should conduct pre-publication review to assess potential harms.
Being closer to the technology itself, AI/ML re-searchers are well placed to
highlight new risks and develop technical solutions, so this novel attempt to
measure their attitudes has broad relevance. The findings should help to
improve how researchers, private sector executives, and policymakers think
about regulations, governance frameworks, guiding principles, and national and
international governance strategies for AI.Abstract
Arbitrariness and Social Prediction: The Confounding Role of Variance in
Fair Classification
AAAI '24 (received a Best Paper Honorable Mention designation)
Variance in predictions across different trained models is a significant,
under-explored source of error in fair binary classification. In practice, the
variance on some data examples is so large that decisions can be effectively
arbitrary. To investigate this problem, we take an experimental approach and
make four overarching contributions: We: 1) Define a metric called
self-consistency, derived from variance, which we use as a proxy for measuring
and reducing arbitrariness; 2) Develop an ensembling algorithm that abstains
from classification when a prediction would be arbitrary; 3) Conduct the
largest to-date empirical study of the role of variance (vis-a-vis
self-consistency and arbitrariness) in fair binary classification; and, 4)
Release a toolkit that makes the US Home Mortgage Disclosure Act (HMDA)
datasets easily usable for future research. Altogether, our experiments reveal
shocking insights about the reliability of conclusions on benchmark datasets.
Most fair binary classification benchmarks are close-to-fair when taking into
account the amount of arbitrariness present in predictions -- before we even
try to apply any fairness interventions. This finding calls into question the
practical utility of common algorithmic fairness methods, and in turn suggests
that we should reconsider how we choose to measure fairness in binary
classification.Abstract
Skilled and Mobile: Survey Evidence of AI Researchers' Immigration
Preferences
Accepted for poster presentation at the 2021 AAAI/ACM Conference on
AI, Ethics, and Society
Countries, companies, and universities are increasingly competing over
top-tier artificial intelligence (AI) researchers. Where are these researchers
likely to immigrate and what affects their immigration decisions? We conducted
a survey (n=524) of the immigration preferences and motivations of
researchers that had papers accepted at one of two prestigious AI conferences:
the Conference on Neural Information Processing Systems (NeurIPS) and the
International Conference on Machine Learning (ICML). We find that the U.S. is
the most popular destination for AI researchers, followed by the U.K., Canada,
Switzerland, and France. A country's professional opportunities stood out as
the most common factor that influences immigration decisions of AI researchers,
followed by lifestyle and culture, the political climate, and personal
relations. The destination country's immigration policies were important to
just under half of the researchers surveyed, while around a quarter noted
current immigration difficulties to be a deciding factor. Visa and immigration
difficulties were perceived to be a particular impediment to conducting AI
research in the U.S., the U.K., and Canada. Implications of the findings for
the future of AI talent policies and governance are discussed.Abstract
U.S. Public Opinion on the Governance of Artificial Intelligence
22 pages; 7 figures; 4 tables; accepted for oral presentation at the
2020 AAAI/ACM Conference on A...
Artificial intelligence (AI) has widespread societal implications, yet social
scientists are only beginning to study public attitudes toward the technology.
Existing studies find that the public's trust in institutions can play a major
role in shaping the regulation of emerging technologies. Using a large-scale
survey (N=2000), we examined Americans' perceptions of 13 AI governance
challenges as well as their trust in governmental, corporate, and
multistakeholder institutions to responsibly develop and manage AI. While
Americans perceive all of the AI governance issues to be important for tech
companies and governments to manage, they have only low to moderate trust in
these institutions to manage AI applications.Abstract
Accurate real time crime prediction is a fundamental issue for public safety,
but remains a challenging problem for the scientific community. Crime
occurrences depend on many complex factors. Compared to many predictable
events, crime is sparse. At different spatio-temporal scales, crime
distributions display dramatically different patterns. These distributions are
of very low regularity in both space and time. In this work, we adapt the
state-of-the-art deep learning spatio-temporal predictor, ST-ResNet [Zhang et
al, AAAI, 2017], to collectively predict crime distribution over the Los
Angeles area. Our models are two staged. First, we preprocess the raw crime
data. This includes regularization in both space and time to enhance
predictable signals. Second, we adapt hierarchical structures of residual
convolutional units to train multi-factor crime prediction models. Experiments
over a half year period in Los Angeles reveal highly accurate predictive power
of our models.Abstract
Hire Me or Not? Examining Language Model's Behavior with Occupation
Attributes
With the impressive performance in various downstream tasks, large language
models (LLMs) have been widely integrated into production pipelines, like
recruitment and recommendation systems. A known issue of models trained on
natural language data is the presence of human biases, which can impact the
fairness of the system. This paper investigates LLMs' behavior with respect to
gender stereotypes, in the context of occupation decision making. Our framework
is designed to investigate and quantify the presence of gender stereotypes in
LLMs' behavior via multi-round question answering. Inspired by prior works, we
construct a dataset by leveraging a standard occupation classification
knowledge base released by authoritative agencies. We tested three LLMs
(RoBERTa-large, GPT-3.5-turbo, and Llama2-70b-chat) and found that all models
exhibit gender stereotypes analogous to human biases, but with different
preferences. The distinct preferences of GPT-3.5-turbo and Llama2-70b-chat may
imply the current alignment methods are insufficient for debiasing and could
introduce new biases contradicting the traditional gender stereotypes.Abstract
Online Mirror Descent for Tchebycheff Scalarization in Multi-Objective
Optimization
The goal of multi-objective optimization (MOO) is to learn under multiple,
potentially conflicting, objectives. One widely used technique to tackle MOO is
through linear scalarization, where one fixed preference vector is used to
combine the objectives into a single scalar value for optimization. However,
recent work (Hu et al., 2024) has shown linear scalarization often fails to
capture the non-convex regions of the Pareto Front, failing to recover the
complete set of Pareto optimal solutions. In light of the above limitations,
this paper focuses on Tchebycheff scalarization that optimizes for the
worst-case objective. In particular, we propose an online mirror descent
algorithm for Tchebycheff scalarization, which we call OMD-TCH. We show that
OMD-TCH enjoys a convergence rate of O(√logm/T) where m is the
number of objectives and T is the number of iteration rounds. We also propose
a novel adaptive online-to-batch conversion scheme that significantly improves
the practical performance of OMD-TCH while maintaining the same convergence
guarantees. We demonstrate the effectiveness of OMD-TCH and the adaptive
conversion scheme on both synthetic problems and federated learning tasks under
fairness constraints, showing state-of-the-art performance.Abstract
SIESEF-FusionNet: Spatial Inter-correlation Enhancement and
Spatially-Embedded Feature Fusion Network for LiDAR Point Cloud Semantic
Segmentation
The ambiguity at the boundaries of different semantic classes in point cloud
semantic segmentation often leads to incorrect decisions in intelligent
perception systems, such as autonomous driving. Hence, accurate delineation of
the boundaries is crucial for improving safety in autonomous driving. A novel
spatial inter-correlation enhancement and spatially-embedded feature fusion
network (SIESEF-FusionNet) is proposed in this paper, enhancing spatial
inter-correlation by combining inverse distance weighting and angular
compensation to extract more beneficial spatial information without causing
redundancy. Meanwhile, a new spatial adaptive pooling module is also designed,
embedding enhanced spatial information into semantic features for strengthening
the context-awareness of semantic features. Experimental results demonstrate
that 83.7% mIoU and 97.8% OA are achieved by SIESEF-FusionNet on the Toronto3D
dataset, with performance superior to other baseline methods. A value of 61.1%
mIoU is reached on the semanticKITTI dataset, where a marked improvement in
segmentation performance is observed. In addition, the effectiveness and
plug-and-play capability of the proposed modules are further verified through
ablation studies.Abstract
Revisiting, Benchmarking and Understanding Unsupervised Graph Domain
Adaptation
Unsupervised Graph Domain Adaptation (UGDA) involves the transfer of
knowledge from a label-rich source graph to an unlabeled target graph under
domain discrepancies. Despite the proliferation of methods designed for this
emerging task, the lack of standard experimental settings and fair performance
comparisons makes it challenging to understand which and when models perform
well across different scenarios. To fill this gap, we present the first
comprehensive benchmark for unsupervised graph domain adaptation named
GDABench, which encompasses 16 algorithms across 5 datasets with 74 adaptation
tasks. Through extensive experiments, we observe that the performance of
current UGDA models varies significantly across different datasets and
adaptation scenarios. Specifically, we recognize that when the source and
target graphs face significant distribution shifts, it is imperative to
formulate strategies to effectively address and mitigate graph structural
shifts. We also find that with appropriate neighbourhood aggregation
mechanisms, simple GNN variants can even surpass state-of-the-art UGDA
baselines. To facilitate reproducibility, we have developed an easy-to-use
library PyGDA for training and evaluating existing UGDA methods, providing a
standardized platform in this community. Our source codes and datasets can be
found at: https://github.com/pygda-team/pygda.Abstract
LongSafetyBench: Long-Context LLMs Struggle with Safety Issues
arXiv:2411.06899v1 »Full PDF »With the development of large language models (LLMs), the sequence length of
these models continues to increase, drawing significant attention to
long-context language models. However, the evaluation of these models has been
primarily limited to their capabilities, with a lack of research focusing on
their safety. Existing work, such as ManyShotJailbreak, has to some extent
demonstrated that long-context language models can exhibit safety concerns.
However, the methods used are limited and lack comprehensiveness. In response,
we introduce \textbf{LongSafetyBench}, the first benchmark designed to
objectively and comprehensively evaluate the safety of long-context models.
LongSafetyBench consists of 10 task categories, with an average length of
41,889 words. After testing eight long-context language models on
LongSafetyBench, we found that existing models generally exhibit insufficient
safety capabilities. The proportion of safe responses from most mainstream
long-context LLMs is below 50\%. Moreover, models' safety performance in
long-context scenarios does not always align with that in short-context
scenarios. Further investigation revealed that long-context models tend to
overlook harmful content within lengthy texts. We also proposed a simple yet
effective solution, allowing open-source models to achieve performance
comparable to that of top-tier closed-source models. We believe that
LongSafetyBench can serve as a valuable benchmark for evaluating the safety
capabilities of long-context language models. We hope that our work will
encourage the broader community to pay attention to the safety of long-context
models and contribute to the development of solutions to improve the safety of
long-context LLMs.Abstract
16737 latest Fairness/Ethics + ML/AI papers
