Many economists argue that a national carbon tax
would be the most effective policy for incentivizing the
development of low-carbon energy technologies. Yet
existing models that measure the effects of a carbon
tax only consider carbon taxes with fixed schedules.
We propose a simple energy system transition model
based on a finite-horizon Markov Decision Process
(MDP) and use it to compare the carbon emissions
reductions achieved by static versus adaptive carbon
taxes. We find that in most cases, adaptive taxes
achieve equivalent if not lower emissions trajectories
while reducing the cost burden imposed by the carbon
tax. However, the MDP optimization in our model
adapted optimal policies to take advantage of the
expected carbon tax adjustment, which sometimes
resulted in the simulation missing its emissions targets.
We propose a novel testing and containment strategy
to limit the spread of SARS-CoV2 while minimising the impact on the social and economic fabric
of countries struggling with the pandemic. Our approach recognises the fact that testing capacities in
many low and middle-income countries (LMICs)
are severely constrained. In this setting, we show
that the best way to utilise a limited number of tests
during a pandemic can be found by solving an allocation problem. Our problem formulation takes
into account the heterogeneity of the population and
uses pooled testing to identify and isolate individuals while prioritising key workers and individuals
with a higher risk of spreading the disease. In order
to demonstrate the efficacy of our strategy, we perform simulations using a network-based SIR model.
Our simulations indicate that applying our mechanism to a population of 10, 000 individuals with only
1 test per day reduces the peak number of infected
individuals by approximately 27%, when compared
to the scenario where no intervention is implemented, and requires at most 2% of the population to
self-isolate at any given point.
Artificial Intelligence has the potential to exacerbate societal bias and set back decades of advances
in equal rights and civil liberty. Data used to train
machine learning algorithms may capture social injustices, inequality or discriminatory attitudes that
may be learned and perpetuated in society. Attempts to address this issue are rapidly emerging
from different perspectives involving technical solutions, social justice and data governance measures. While each of these approaches are essential to the development of a comprehensive solution, often discourse associated with each seems
disparate. This paper reviews ongoing work to ensure data justice, fairness and bias mitigation in AI
systems from different domains exploring the interrelated dynamics of each and examining whether
the inevitability of bias in AI training data may in
fact be used for social good. We highlight the complexity associated with defining policies for dealing
with bias. We also consider technical challenges in
addressing issues of societal bias.
India accounts for 11% of maternal deaths globally
where a woman dies in childbirth every fifteen minutes. Lack of access to preventive care information
is a significant problem contributing to high maternal morbidity and mortality numbers, especially
in low-income households. We work with ARMMAN, a non-profit based in India, to further the
use of call-based information programs by earlyon identifying women who might not engage on
these programs that are proven to affect health parameters positively. We analyzed anonymized callrecords of over 300,000 women registered in an
awareness program created by ARMMAN that uses
cellphone calls to regularly disseminate health related information. We built robust deep learning
based models to predict short term and long term
dropout risk from call logs and beneficiaries’ demographic information. Our model performs 13% better than competitive baselines for short-term forecasting and 7% better for long term forecasting. We
also discuss the applicability of this method in the
real world through a pilot validation that uses our
method to perform targeted interventions.
Much of the computational social science research
focusing on issues faced in developing nations concentrates on web content written in a world language often ignoring a significant chunk of a corpus
written in a poorly resourced yet highly prevalent
first language of the region in concern. Such omissions are common and convenient due to the sheer
mismatch between linguistic resources offered in
a world language and its low-resource counterpart.
However, the path to analyze English content generated in linguistically diverse regions, such as the
Indian subcontinent, is not straight-forward either.
Social science/AI for social good research focusing on Indian sub-continental issues faces two major Natural Language Processing (NLP) challenges:
(1) how to extract a (reasonably clean) monolingual
English corpus? (2) How to extend resources and
analyses to its low-resource counterpart? In this
, we share NLP methods, lessons learnt from
our multiple projects, and outline future focus areas that could be useful in tackling these two challenges. The discussed results are critical to two
important domains: (1) detecting peace-seeking,
hostility-diffusing hope speech in the context of the
2019 India-Pakistan conflict (2) detecting user generated web-content encouraging COVID-19 health
Gerrymandering is the process of drawing electoral
district maps in order to manipulate the outcomes
of elections. Increasingly, computers are involved
in both drawing biased districts and attempts to
measure and regulate this practice. The most highprofile proposals to measure partisan gerrymandering use past voting data to classify a map as gerrymandered (or not). Prior work studies the ability of these metrics to detect gerrymandering, but
does not explore how the metrics could affect voter
behavior or be circumvented via strategic voting.
We show that using past voting data for this classification can affect strategyproofness by introducing a game which models the iterative sequence of
voting and redrawing districts under regulation that
bans outlier maps. In experiments, we show that a
heuristic can find strategies for this game including on real North Carolin maps and voting data.
Finally, we address questions from a recent US
Supreme Court case that relate to our model. This
is a summary of “Meddling Metrics: the Effects
of Measuring and Constraining Partisan Gerrymandering on Voter Incentives” appearing in EC2020
Rapid damage assessment after natural disasters is crucial for effective planning of relief efforts. Satellites with Very High Resolution (VHR) sensors can provide a detailed aerial image of the affected area, but current damage detection systems are fully- or semi-manual which can delay the delivery of emergency care. In this paper, we apply recent advancements in segmentation and change detection to detect damage given pre- and post-disaster VHR images of an affected area. Moreover, we demonstrate that segmentation models trained for this task rely on shadows by showing that (i) shadows influence false positive detections by the model, and (ii) removing shadows leads to poorer performance. Through this analysis, we aim to inspire future work to improve damage detection.
Social media has become an increasingly important political domain in recent years, especially for
campaign advertising. In this work, we develop
a linear model of advertising influence maximization in two-candidate elections from the viewpoint
of a fully-informed social network platform, using several variations on classical DeGroot dynamics to model different features of electoral opinion formation. We consider two types of candidate
objectives—margin of victory (maximizing total
votes earned) and probability of victory (maximizing probability of earning the majority)—and show
key theoretical differences in the corresponding
games, including advertising strategies for arbitrarily large networks and the existence of pure Nash
equilibria. Finally, we contribute efficient algorithms for computing mixed equilibria in the margin of victory case as well as influence-maximizing
best-response algorithms in both cases and show
that in practice, as implemented on the Adolescent Health Dataset, they contribute to campaign
equality by minimizing the advantage of the higherspending candidate.
Since the start of the pandemic, the proliferation
of fake news and misinformation has been a constant battle for health officials and policy makers as
they work to curb the spread of COVID-19. In areas within the Global South, it can be difficult for
officials to keep track of the growth of such false information and even harder to address the real concerns their communities have. In this paper, we
present some techniques the AI community can offer to help address this issue. While the topics presented within this paper are not a complete solution,
we believe they could complement the work government officials, healthcare workers, and NGOs
are currently doing on the ground in Sub-Saharan
As the COVID-19 pandemic continues, formulating targeted policy interventions supported by differential SARS-CoV2 transmission dynamics will
be of vital importance to national and regional governments. We develop an individual-level model
for SARS-CoV2 transmission that accounts for
location-dependent distributions of age, household
structure, and comorbidities. We use these distributions together with age-stratified contact matrices to instantiate specific models for Hubei, China;
Lombardy, Italy; and New York, United States.
We then develop a Bayesian inference framework
which leverages data on reported deaths to obtain a
posterior distribution over unknown parameters and
infer differences in the progression of the epidemic
in the three locations. These findings highlight the
role of between-population variation in formulating
Applications of artificial intelligence for wildlife
protection have focused on learning models of
poacher behavior based on historical patterns.
However, poachers’ behaviors are described not
only by their historical preferences, but also their
reaction to ranger patrols. Past work applying machine learning and game theory to combat poaching
have hypothesized that ranger patrols deter poachers, but have been unable to find evidence to identify how or even if deterrence occurs. Here for the
first time, we demonstrate a measurable deterrence
effect on real-world poaching data. We show that
increased patrols in one region deter poaching in
the next timestep, but poachers then move to neighboring regions. Our findings offer guidance on how
adversaries should be modeled in realistic gametheoretic settings.
An ongoing challenge in machine learning is to improve the transparency of learning models, helping end users to build trust and defend fairness and equality while protecting individual privacy and information assets. Transparency is a timely topic given the increasing application of machine learning techniques in the real world, and yet much more progress is needed in addressing the transparency issues. We propose critical research questions on transparency-aware machine learning on two fronts: know how and know that. Know-how is concerned with searching for a set of decision objects (e.g. functions, rules, lists, and graphs) that are cognitively fluent for humans to apply and consistent with the original complex model, while know-that is concerned with gaining more in-depth understanding of the internal justification of the decisions through external constraints on accuracy, consistency, privacy, reliability, and fairness.
During the COVID-19 pandemic, committees have
been appointed to make ethically difficult triage decisions, which are complicated by the diversity of
stakeholder interests involved. We propose a disciplined, automated approach to support such difficult collective decision-making. Our system aims
to recommend a policy to the group that strikes a
compromise between potentially conflicting individual preferences. To identify a policy that best
aggregates individual preferences, our system first
elicits individual stakeholder value judgements by
asking a moderate number of strategically selected
queries, each taking the form of a pairwise comparison posed to a specific stakeholder. We propose a novel formulation of this problem that selects which queries to ask which individuals to best
inform the downstream recommendation problem.
Modeling this as a multi-stage robust optimization
problem, we show that we can equivalently reformulate this as a mixed-integer linear program
which can be solved with off-the-shelf solvers. We
evaluate the performance of our approach on the
problem of recommending policies for allocating
critical care beds to patients with COVID-19. We
show that asking questions intelligently allows the
system to recommend a policy with a much lower
regret than asking questions randomly. The lower
regret suggests that the system is suited to help a
committee reach a better decision by suggesting
a policy that aligns with stakeholder value judgments.
The Google Trends data of some keywords have
strong correlations with COVID-19 hospitalizations. We attempt to use these correlations and
show an experimental procedure using a simple
LSTM model to nowcast hospitalization peaks using Google Trends data. Experiments are done on
French regions and on Belgium. This is a preliminary work, that would need to be tested during a
(hopefully non-existing) second peak.
Social media has quickly grown into an essential
tool for people to communicate and express their
needs during crisis events. Prior work in analyzing social media data for crisis management has
focused primarily on automatically identifying actionable (or, informative) crisis-related messages.
In this work, we show that recent advances in Deep
Learning and Natural Language Processing outperform prior approaches for the task of classifying
informativeness and encourage the field to adopt
them for their research or even deployment. We
also extend these methods to two sub-tasks of informativeness and find that the Deep Learning methods are effective here as well.
In health care organizations, a patient’s privacy is
threatened by the misuse of their electronic health
record (EHR). To monitor privacy intrusions, logging
systems are often deployed to trigger alerts whenever a suspicious access is detected. However, such
mechanisms are insufficient in the face of small budgets, strategic attackers, and large false positive rates.
In an attempt to resolve these problems, EHR systems are increasingly incorporating signaling, so that
whenever a suspicious access request occurs, the system can, in real time, warn the user that the access
may be audited. This gives rise to an online problem
in which one needs to determine 1) whether a warning should be triggered and 2) the likelihood that the
data request will be audited later. In this paper, we
formalize this auditing problem as a Signaling Audit
Game (SAG). A series of experiments with 10 million real access events (containing over 26K alerts)
from Vanderbilt University Medical Center (VUMC)
demonstrate that a strategic presentation of warnings
adds value in that SAGs realize significantly higher
utility for the auditor than systems without signaling.
Discharge summaries are essential for the transition of patients’ care but often lack sufficient information. We present an attention-based model
to generate discharge summaries to support communication during the transition of care from intensive care units (ICU) to community care. We
trained and evaluated our approach over 500, 000
clinical progress notes. The summaries automatically generated by our model achieve a ROUGE-L
of 0.83 when comparing with discharge summaries
written by health professionals. We attribute the
high performance to our three-step pipeline that incorporates disease and specialist contexts to enrich
the summaries with relevant information based on
the context of the hospital stay. Additionally, we
present a novel visualization of ICU flow of care using MIMIC-III. Our promising results have the potential to improve the pipeline of hospital discharge
and continuous health care.
Research shows that providing an appliance-wise energy breakdown can help users save up to 15% of their energy bills. Non-intrusive load monitoring (NILM) or energy disaggregation is the task of estimating the household energy measured at the aggregate level for each constituent appliances in the household. The problem was first was introduced in the 1980s by Hart. Over the past three decades, NILM has been an extensively researched topic by researchers. NILMTK was introduced in 2014 to the NILM community in order to motivate reproducible research. Even after the introduction of the NILMTK toolkit to the community, there has been a little contribution of recent state-of-the-art algorithms back to the toolkit. In this paper, we propose a new disaggregation API, which further simplifies the process for the rapid comparison of different state-of-the-art algorithms across a wide range of datasets and algorithms. We also propose a new rewrite for writing the new disaggregation algorithms for NILMTK, which is similar to Scikitlearn. We demonstrate the power of the new API by conducting various complex experiments using the API.
COVID-19 Prevention, which combines the soft approaches and best practices for public health safety,
is the only recommended solution from the health
science and management society side considering
the pandemic era. This process must be promoted
via facilitation support to collective urban awareness programs through public dialogue and collective intelligence. Moreover, support must be provided throughout the process to perform complex
public deliberation to find issues and ideas within
existing approaches that can result in better approaches towards prevention. In an attempt to evaluate the validity of such claims in a conflict and
COVID-19-affected country like Afghanistan, we
conducted a large-scale digital social experiment using conversational AI and social platforms from an
info-epidemiology and an info-veillance perspective.
This served as a means to uncover an underling truth,
give large-scale facilitation support, extend the soft
impact of discussion to multiple sites, collect, diverge, converge and evaluate a large amount of
opinions and concerns from health experts, patients
and local people, deliberate on the data collected and
explore collective prevention approaches of
COVID-19. Finally, this paper shows that deciding
a prevention measure that maximizes the probability
of finding the ground truth is intrinsically difficult
without utilizing the support of an AI-enabled discussion systems.
We propose a multi-armed bandit setting where each arm corresponds to a subpopulation, and pulling an arm is equivalent to granting an opportunity to this subpopulation. In this setting the decision-maker’s fairness policy governs the number of opportunities each subpopulation should receive, which typically depends on the (unknown) reward from granting an opportunity to this subpopulation. The decision-maker can decide whether to provide these opportunities or pay a predefined monetary value for every withheld opportunity. The decision-maker’s objective is to maximize her utility, which is the sum of rewards minus the cost of withheld opportunities. We provide a no-regret algorithm that maximizes the decisionmaker’s utility and complement our analysis with an almost-tight lower bound. Full version of the paper is available at https://tinyurl.com/y7s9avud.