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Review Paper: Data Science for Social Good
Dhavalkumar Solanki
December 22, 2023
1 Abstract
Technologies
have
always
been
reshaping
our
eco
nomic,
social
and
cultural
environments
but
more
recently
we
have
seen
the
speed
of
change
and
im
pact
of
ICT(Information
and
Communication
Tech
nologies).
These
phenomenal
changes
are
fuelled
by
the
proliferation
of
data
and
the
advances
in
compu
tational
methods.
Though
Artificial
Intelligence
as
a
(sub)discipline
has
been
around
for
over
60
years,
it
is
only
in
the
last
decade
or
so
that
we
have
seen
very
significant
improvements
in
techniques
such
as
machine
learning.The
pervasive
influence
of
data
sci
ence
methodologies
and
technological
advancements
has
transformed
societal
paradigms,
offering
unprece
dented
opportunities
to
address
pressing
global
chal
lenges.This
review
paper
examines
the
burgeoning
field
of
”Data
Science
for
Social
Good,”
wherein
data
driven
initiatives
and
computational
techniques
are
harnessed
to
tackle
multifaceted
societal
issues.
Em
bracing
an
interdisciplinary
lens,
this
review
consoli
dates
and
analyzes
the
multifarious
endeavors
lever
aging
data
science
across
diverse
domains
for
the
bet
terment
of
society.
2 Introduction
Through
a
meticulous
examination
of
impactful
case
studies
and
success
stories,
this
review
underscores
the
tangible
outcomes
and
transformative
potential
of
data-driven
interventions
in
effectuating
positive
change.
However,
it
also
highlights
the
challenges,
ethical
considerations,
and
inherent
biases
that
ac
company
the
implementation
of
data
science
for
so
cial
good,
emphasizing the need for ethical frame
works
and
responsible
data
practices.
Ultimately,
this
review
paper
illuminates
the
instrumental
role
of
data
science
in
empowering
communities,
foster
ing
inclusivity,
and
driving
sustainable
development.
It
concludes
by
outlining
future
research
directions
and
advocating
for
the
continued
integration
of
data
driven
approaches
to
tackle
the
ever-evolving
land
scape
of
societal
challenges,
thereby
forging
a
path
toward
a
more
equitable
and
prosperous
future
for
all.
3 Data Science Initiatives Ad
dressing Social Challenges
3.1 Healthcare Accessibility
The
intersection
of
data
science
and
healthcare
has
led
to
innovative
solutions
aimed
at
enhancing
ac
cessibility,
affordability,
and
quality
of
healthcare
services.
Initiatives
leveraging
data
analytics
and
machine
learning
have
revolutionized
healthcare
by
enabling
predictive
analytics
for
disease
diagnosis,
optimizing
treatment
plans,
and
facilitating
remote
healthcare
monitoring.
For
instance,
projects
uti
lizing
electronic
health
records
(EHR)
data
have
demonstrated
significant
advancements
in
predict
ing
disease
outbreaks,
optimizing
resource
allocation, and improving patient outcomes.
3.2 Poverty Alleviation
Data-driven
initiatives
focused
on
poverty
alleviation
encompass
a
diverse
range
of
interventions,
includ
ing
microfinance,
poverty
mapping,
and
targeted
aid
distribution.
Machine
learning algorithms analyzing
socio-economic
indicators,
geospatial
data,
and
de
mographic
information
have
facilitated
poverty
map
2.
ping
efforts,
aiding
policymakers
in
identifying
vul
nerable
populations
and
tailoring
intervention
strate
gies.
Moreover,
innovative
applications
of
predictive
modeling
in
microfinance
institutions
have
enhanced
credit
risk
assessment
and
expanded
financial inclu sion for marginalized communities.
3.3 Environmental Sustainability
The
amalgamation
of
data
science
techniques
with
environmental
science
has
propelled
efforts
to
ward
environmental
sustainability.
Data-driven
ap
proaches
leveraging
satellite
imagery,
sensor
data,
and
climate
models
have
enabled
effective
monitoring
of
deforestation,
climate
change
impacts,
and
nat
ural
disaster
management.
Additionally,
predictive
models
powered
by
machine
learning
algorithms
have
been
instrumental
in
optimizing
resource
manage
ment,
biodiversity
conservation,
and
renewable
en
ergy
production,
contributing to a more sustainable ecosystem.
4 Methodologies and Tech niques
Data
Collection
and
Preprocessing
Data
collection
methodologies
encompass
diverse
sources,
including
structured
databases,
unstructured
text,
images,
and
sensor
data.
Techniques
for
data
preprocessing
in
volve
data
cleaning,
normalization,
and
feature
en
gineering
to
ensure
the
quality
and
relevance
of
datasets
for
analysis.
Furthermore,
data
fusion
tech
niques
integrating
heterogeneous
data
sources
have
enabled
a
comprehensive
understanding of societal challenges.
Machine
Learning
and
Predictive
Modeling
Ma
chine
learning
techniques
such
as
supervised
learn
ing,
unsupervised
learning,
and
reinforcement
learn
ing
serve
as
powerful
tools
for
predictive
modeling.
Algorithms
like
decision
trees,
neural
networks,
and
ensemble
methods
are
applied
to
analyze
complex
so
cietal
data,
aiding
in
forecasting,
classification,
and
clustering
tasks.
These
methodologies
are
instru
mental
in
predicting
trends,
identifying
patterns,
and
making
data-driven
decisions
in
social
interventions.
Geospatial
Analysis
and
Visualization
Geospatial
analysis
techniques,
coupled
with
visualization
tools,
facilitate
the
exploration
and
understanding
of
spa
tial
data.
Geographic
information
systems
(GIS),
spatial
clustering
algorithms,
and
interactive
maps
offer
insights
into
spatial
patterns,
aiding
in
urban
planning,
disaster
response,
and
resource
allocation.
Visual
representations
help
communicate
complex
spatial
information
to
stakeholders and policymakers effectively.
Network
Analysis
and
Social
Graphs
Network
anal
ysis
techniques
analyze
relational
data
and
social
graphs
to
uncover
hidden
patterns
and
relation
ships.
Graph-based
algorithms
and
centrality
mea
sures
help
identify
influential
nodes
in
social
net
works,
enabling
targeted
interventions
for
commu
nity
engagement,
opinion
spreading,
and
social
wel
fare
programs.
These
methodologies
are
pivotal
in
understanding
social
structures
and
fostering inclu sive communities.
5 Impact Assessment and Case
Studies
5.1 healthcare accessibility
Drug
Discovery
drug
revelation
in
data
science
in
medical
care
The
revelation
of
medications
is
an
intri
cate
cycle
and
includes
an
expense
of
around
USD
2.6
billion.
It
requires
an
investment
term
of
something
like
12
years
to
take
a
medication
from
the research facility to the market.
The
enterprises
are
utilizing
Data
Science
to
im
prove
and
abbreviate
the
number
of
cycles
and
the
testing
engaged
with
the
Drug
Discovery.
In
this
way,
analysts
are
utilizing
different
Machine
Learning
cal
culations
and
numerical
models.
To
anticipate
what
these
medications
will
mean
for
the
human
body
for
finding
more
successful
medications.
5.2 Poverty alleviation
3.
As
for
biometric
data,
India
is
said
to
be
well-known
for
using
biometric
data
to
ensure
that
aid
is
given
to
the
right
people
and
corruption
is
prevented
through
its
national
unique
identity
card
program,
Aadhaar.
By
using
biometric
data
to
create
unique
identity
cards
for
its
citizens,
the
poor
communities
can
effec
tively
access
aid
and
subsidies
with
instant
identity verifications.
If
mobile
phone,
satellite
and
biometric
data
can
be
leveraged
to
identify
who
needs
help
and
send
help
to
them,
such
data
can
be
used
to
increase
financial
in
clusion
for
people
without
formal
credit
using mobile banking.
5.3 Enviornment Sustainability
Human
society
has
taken
many
measures
to
ad
dress
environmental
issues.
For
example,
deploying
wastewater
treatment
plants
(WWTPs)
to
alleviate
water
pollution
and
the
shortage
of
usable
water;
us
ing
waste-to-energy
(WtE)
plants
to
recover
energy
from
the
waste
and
reduce
its
environmental
impact.
However,
managing
these
facilities
is
taxing
because
the
processes
and
operations
are
always
complex
and
dynamic.
These
characteristics
hinder
the
compre
hensive
and
precise
understanding
of
the
processes
through
the
conventional
mechanistic
models.
On
the
other
hand,
with
the
development
of
the
Fourth
In
dustrial
Revolution,
large-volume
and
high-resolution
data
from
automatic
online
monitoring
have
become
increasingly
obtainable.
These
data
usually
reflect
abundant
detailed
information
of
process
activities
that
can
be
utilized
for optimizing process control
6 Challenges and Ethical Con
siderations
6.1 Data Quality and Bias
The
quality
of
data
used
in
social
good
initiatives
poses
a
significant
challenge,
often
affected
by
in
completeness,
inaccuracy,
or
biases.
Biases
within
datasets,
including
demographic
biases
or
sampling
biases, can propagate through algorithms, leading
to unfair outcomes and exacerbating societal
disparities.
6.2 Privacy and Confidentiality
Ethical
considerations
regarding
data
privacy
and
confidentiality
are
paramount
in
data-driven
initia
tives.
Balancing
the
benefits
of
data
analysis
with
individual
privacy
rights
remains
a
challenge.
Ensur
ing
anonymization,
secure
data
storage,
and
obtain
ing
informed
consent
are
critical
for
upholding
ethical standards.
6.3 Interpretable and Transparent Models
Complex
machine
learning
models
often
lack
inter
pretability,
making
it
challenging
to
understand
their
decision-making
processes.
Ensuring
transparency
in
algorithms
and
models
is
crucial,
especially
in
sensi
tive
domains
such
as
healthcare
or
criminal
justice,
to
mitigate
biases
and
ensure
accountability.
6.4 Fairness and Algorithmic Bias
Ensuring
fairness
in
data-driven
interventions
in
volves
addressing
algorithmic
biases
and
discrimina
tory
outcomes.
Fairness
metrics
and
techniques,
in
cluding
fairness-aware
algorithms
and
bias
detection
methods,
are
essential
to
mitigate
biases
and
promote
equitable
outcomes
across diverse populations.
6.5 Stakeholder Engagement and Trust
Building
trust
and
fostering
stakeholder
engagement
are
essential
for
the
success
of
social
good
initiatives.
Ensuring
transparent
communication,
involving
di
verse
stakeholders,
and
addressing
concerns
regard
ing
data
use
and
decision-making
processes
are
criti
cal
to
gaining
societal acceptance and support.
6.6 Resource Constraints and Access
Limited resources, including funding, technological
4.
infrastructure, and data access, pose challenges in
implementing
data-driven
initiatives
in
underserved
communities.
Bridging
the
digital
divide
and
ensur
ing
equitable
access
to
data
and
technology
are
es
sential considerations.
References
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Zegura, Ellen, Carl DiSalvo, and Amanda
Meng. "Care and the practice of data
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the 1st ACM SIGCAS Conference on
Computing and Sustainable Societies.
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2.
Tomašev, Nenad, et al. "AI for social good:
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impact." Nature Communications 11.1
(2020): 2468.
3.
Coulton, C.J., Goerge, R.,
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Cleveland: American Academy of Social
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