Artificial Intelligence (AI) for health has the potential to significantly
change and improve healthcare. However in most African countries, identifying
culturally and contextually attuned approaches for deploying these solutions is
not well understood. To bridge this gap, we conduct a qualitative study to
investigate the best practices, fairness indicators, and potential biases to
mitigate when deploying AI for health in African countries, as well as explore
opportunities where artificial intelligence could make a positive impact in
health. We used a mixed methods approach combining in-depth interviews (IDIs)
and surveys. We conduct 1.5-2 hour long IDIs with 50 experts in health, policy,
and AI across 17 countries, and through an inductive approach we conduct a
qualitative thematic analysis on expert IDI responses. We administer a blinded
30-minute survey with case studies to 672 general population participants
across 5 countries in Africa and analyze responses on quantitative scales,
statistically comparing responses by country, age, gender, and level of
familiarity with AI. We thematically summarize open-ended responses from
surveys. Our results find generally positive attitudes, high levels of trust,
accompanied by moderate levels of concern among general population participants
for AI usage for health in Africa. This contrasts with expert responses, where
major themes revolved around trust/mistrust, ethical concerns, and systemic
barriers to integration, among others. This work presents the first-of-its-kind
qualitative research study of the potential of AI for health in Africa from an
algorithmic fairness angle, with perspectives from both experts and the general
population. We hope that this work guides policymakers and drives home the need
for further research and the inclusion of general population perspectives in
decision-making around AI usage.Abstract
CDR: Customizable Density Ratios of Strong-over-weak LLMs for Preference
Annotation
arXiv:2411.02481v2 »Full PDF »Preference tuning of large language models (LLMs) relies on high-quality
human preference data, which is often expensive and time-consuming to gather.
While existing methods can use trained reward models or proprietary model as
judges for preference annotation, they have notable drawbacks: training reward
models remain dependent on initial human data, and using proprietary model
imposes license restrictions that inhibits commercial usage. In this paper, we
introduce customized density ratio (CDR), a training-free and highly effective
method that leverages off-the-shelf LLMs for preference data annotation. Our
approach uses the log-density ratio between a better-aligned LLM and a less
aligned LLM as a reward signal. We explores 221 different LLMs pairs and
empirically demonstrate that increasing the performance gap between paired LLMs
correlates with better reward generalization. Furthermore, we show that
tailoring the density ratio reward function with specific criteria and
preference exemplars enhances performance across domains and within target
areas.
In our experiment using density ratio from a pair of Mistral-7B models, CDR
achieves a RewardBench score of 82.6, outperforming the best trained reward
functions from same model class and demonstrating competitive performance
against SoTA models in Safety (91.0) and Reasoning (88.0) domains. We use CDR
to annotate an on-policy preference dataset with which we preference tune
Llama-3-8B-Instruct with SimPO. Using reward signals from two relatively weak
models, our approach pushes Llama-3-8B to achieve a 37.4% (+15.1%) win rate on
ArenaHard and a 40.7% (+17.8%) win rate on Length-Controlled AlpacaEval 2.0,
along with a score of 8.0 on MT-Bench.Abstract
A Primer on Word Embeddings: AI Techniques for Text Analysis in Social
Work
Word embeddings represent a transformative technology for analyzing text data
in social work research, offering sophisticated tools for understanding case
notes, policy documents, research literature, and other text-based materials.
This methodological paper introduces word embeddings to social work
researchers, explaining how these mathematical representations capture meaning
and relationships in text data more effectively than traditional keyword-based
approaches. We discuss fundamental concepts, technical foundations, and
practical applications, including semantic search, clustering, and retrieval
augmented generation. The paper demonstrates how embeddings can enhance
research workflows through concrete examples from social work practice, such as
analyzing case notes for housing instability patterns and comparing social work
licensing examinations across languages. While highlighting the potential of
embeddings for advancing social work research, we acknowledge limitations
including information loss, training data constraints, and potential biases. We
conclude that successfully implementing embedding technologies in social work
requires developing domain-specific models, creating accessible tools, and
establishing best practices aligned with social work's ethical principles. This
integration can enhance our ability to analyze complex patterns in text data
while supporting more effective services and interventions.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
SCAR: Sparse Conditioned Autoencoders for Concept Detection and Steering
in LLMs
arXiv:2411.07122v1 »Full PDF »Large Language Models (LLMs) have demonstrated remarkable capabilities in
generating human-like text, but their output may not be aligned with the user
or even produce harmful content. This paper presents a novel approach to detect
and steer concepts such as toxicity before generation. We introduce the Sparse
Conditioned Autoencoder (SCAR), a single trained module that extends the
otherwise untouched LLM. SCAR ensures full steerability, towards and away from
concepts (e.g., toxic content), without compromising the quality of the model's
text generation on standard evaluation benchmarks. We demonstrate the effective
application of our approach through a variety of concepts, including toxicity,
safety, and writing style alignment. As such, this work establishes a robust
framework for controlling LLM generations, ensuring their ethical and safe
deployment in real-world applications.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
MAN TruckScenes: A multimodal dataset for autonomous trucking in diverse
conditions
Accepted to NeurIPS 2024 Datasets and Benchmarks Track
Autonomous trucking is a promising technology that can greatly impact modern
logistics and the environment. Ensuring its safety on public roads is one of
the main duties that requires an accurate perception of the environment. To
achieve this, machine learning methods rely on large datasets, but to this day,
no such datasets are available for autonomous trucks. In this work, we present
MAN TruckScenes, the first multimodal dataset for autonomous trucking. MAN
TruckScenes allows the research community to come into contact with
truck-specific challenges, such as trailer occlusions, novel sensor
perspectives, and terminal environments for the first time. It comprises more
than 740 scenes of 20s each within a multitude of different environmental
conditions. The sensor set includes 4 cameras, 6 lidar, 6 radar sensors, 2
IMUs, and a high-precision GNSS. The dataset's 3D bounding boxes were manually
annotated and carefully reviewed to achieve a high quality standard. Bounding
boxes are available for 27 object classes, 15 attributes, and a range of more
than 230m. The scenes are tagged according to 34 distinct scene tags, and all
objects are tracked throughout the scene to promote a wide range of
applications. Additionally, MAN TruckScenes is the first dataset to provide 4D
radar data with 360{\deg} coverage and is thereby the largest radar dataset
with annotated 3D bounding boxes. Finally, we provide extensive dataset
analysis and baseline results. The dataset, development kit, and more are
available online.Abstract
25 pages, 12 figures. arXiv admin note: text overlap with
arXiv:2405.06433
When using machine learning for automated prediction, it is important to
account for fairness in the prediction. Fairness in machine learning aims to
ensure that biases in the data and model inaccuracies do not lead to
discriminatory decisions. E.g., predictions from fair machine learning models
should not discriminate against sensitive variables such as sexual orientation
and ethnicity. The training data often in obtained from social surveys. In
social surveys, oftentimes the data collection process is a strata sampling,
e.g. due to cost restrictions. In strata samples, the assumption of
independence between the observation is not fulfilled. Hence, if the machine
learning models do not account for the strata correlations, the results may be
biased. Especially high is the bias in cases where the strata assignment is
correlated to the variable of interest. We present in this paper an algorithm
that can handle both problems simultaneously, and we demonstrate the impact of
stratified sampling on the quality of fair machine learning predictions in a
reproducible simulation study.Abstract
BehaviorGPT: Smart Agent Simulation for Autonomous Driving with
Next-Patch Prediction
Simulating realistic behaviors of traffic agents is pivotal for efficiently
validating the safety of autonomous driving systems. Existing data-driven
simulators primarily use an encoder-decoder architecture to encode the
historical trajectories before decoding the future. However, the heterogeneity
between encoders and decoders complicates the models, and the manual separation
of historical and future trajectories leads to low data utilization. Given
these limitations, we propose BehaviorGPT, a homogeneous and fully
autoregressive Transformer designed to simulate the sequential behavior of
multiple agents. Crucially, our approach discards the traditional separation
between "history" and "future" by modeling each time step as the "current" one
for motion generation, leading to a simpler, more parameter- and data-efficient
agent simulator. We further introduce the Next-Patch Prediction Paradigm (NP3)
to mitigate the negative effects of autoregressive modeling, in which models
are trained to reason at the patch level of trajectories and capture long-range
spatial-temporal interactions. Despite having merely 3M model parameters,
BehaviorGPT won first place in the 2024 Waymo Open Sim Agents Challenge with a
realism score of 0.7473 and a minADE score of 1.4147, demonstrating its
exceptional performance in traffic agent simulation.Abstract
A neural-network based anomaly detection system and a safety protocol to
protect vehicular network
This thesis addresses the use of Cooperative Intelligent Transport Systems
(CITS) to improve road safety and efficiency by enabling vehicle-to-vehicle
communication, highlighting the importance of secure and accurate data
exchange. To ensure safety, the thesis proposes a Machine Learning-based
Misbehavior Detection System (MDS) using Long Short-Term Memory (LSTM) networks
to detect and mitigate incorrect or misleading messages within vehicular
networks. Trained offline on the VeReMi dataset, the detection model is tested
in real-time within a platooning scenario, demonstrating that it can prevent
nearly all accidents caused by misbehavior by triggering a defense protocol
that dissolves the platoon if anomalies are detected. The results show that
while the system can accurately detect general misbehavior, it struggles to
label specific types due to varying traffic conditions, implying the difficulty
of creating a universally adaptive protocol. However, the thesis suggests that
with more data and further refinement, this MDS could be implemented in
real-world CITS, enhancing driving safety by mitigating risks from misbehavior
in cooperative driving networks.Abstract