River Publishers eBooks,
Год журнала:
2024,
Номер
unknown, С. 197 - 227
Опубликована: Фев. 7, 2024
The
increased
complexity
of
artificial
intelligence
(AI),
machine
learning
(ML)
and
deep
(DL)
methods,
models,
training
data
to
satisfy
industrial
application
needs
has
emphasised
the
need
for
AI
model
providing
explainability
interpretability.Model
Explainability
aims
commu
nicate
reasoning
AI/ML/DL
technology
end
users,
while
interpretability
focuses
on
in-powering
transparency
so
that
users
will
understand
precisely
why
how
a
generates
its
results.Edge
AI,
which
combines
Internet
Things
(IoT)
edge
com
puting
enable
real-time
collection,
processing,
analytics,
decisionmaking,
introduces
new
challenges
acheiving
explainable
interpretable
methods.This
is
due
compromises
among
performance,
constrained
resources,
complexity,
power
consumption,
lack
bench
marking
standardisation
in
environments.This
chapter
presents
state
play
inter
pretability
methods
techniques,
discussing
different
benchmarking
approaches
highlighting
state-of-the-art
development
directions.
Advances in environmental engineering and green technologies book series,
Год журнала:
2024,
Номер
unknown, С. 333 - 362
Опубликована: Окт. 16, 2024
Explainable
AI
(XAI)
is
important
in
situations
where
decisions
have
significant
effects
on
the
results
to
make
systems
more
reliable,
transparent,
and
people
understand
how
work.
In
this
chapter,
an
overview
of
AI,
its
evolution
are
discussed,
emphasizing
need
for
robust
policy
regulatory
frameworks
responsible
deployment.
Then
key
concept
use
XAI
models
been
discussed.
This
work
highlights
XAI's
significance
sectors
like
healthcare,
finance,
transportation,
retail,
supply
chain
management,
robotics,
manufacturing,
legal
criminal
justice,
etc.
profound
human
societal
impacts.
Then,
with
integrated
IoT
renewable
energy
management
scope
smart
cities
addressed.
The
study
particularly
focuses
implementations
solutions,
specifically
solar
power
integration,
addressing
challenges
ensuring
transparency,
accountability,
fairness
AI-driven
decisions.
SSRN Electronic Journal,
Год журнала:
2023,
Номер
unknown
Опубликована: Янв. 1, 2023
The
convergence
of
Blockchain
technology
and
Artificial
Intelligence
(AI)
is
exerting
a
transformative
influence,
ushering
in
new
epoch
security
transparency
within
the
financial
sector.
This
amalgamation
effectively
addresses
pivotal
challenges
faced
by
conventional
systems,
presenting
inventive
solutions
to
heighten
efficiency,
diminish
fraud,
amplify
transparency.
Blockchain,
functioning
as
decentralized
tamper-resistant
ledger,
introduces
paradigm
shift
transactions.
Its
capacity
establish
an
unalterable
record
transactions
ensures
that
once
data
recorded,
it
remains
impervious
modification,
thereby
furnishing
unparalleled
level
security.
inherent
attribute
positions
optimal
choice
for
reinforcing
systems
against
cyber
threats
fraudulent
activities.
On
other
hand,
AI
contributes
predictive
analytics,
machine
learning,
automation
forefront
operations.
integration
finance
enables
real-time
analysis,
risk
assessment,
decision-making,
optimizing
processes
elevating
overall
efficiency.
When
amalgamated
with
augments
precision
dependability
data,
cultivating
more
secure
transparent
ecosystem.
A
aspect
this
streamlining
Know
Your
Customer
(KYC)
Anti-Money
Laundering
(AML)
processes.
nature
facilitates
storage
customer
mitigating
identity
theft,
while
algorithms
adeptly
analyze
extensive
datasets
pinpoint
flag
suspicious
not
only
but
also
adherence
regulatory
requirements.
Smart
contracts,
distinctive
feature
automate
enforce
contractual
agreements,
diminishing
reliance
on
intermediaries
minimizing
probability
human
error.
can
be
seamlessly
integrated
into
these
contracts
enhance
their
adaptability
responsiveness
evolving
market
conditions,
further
refining
ushered
all
stakeholders
have
access
singular
version
truth,
fostering
trust
Furthermore,
incorporation
fraud
detection
management
heightens
proactive
identification
potential
threats,
safeguarding
institutions
clientele.
As
increasingly
embrace
integration,
industry
stands
brink
revolution
safeguards
existing
paves
way
innovative
efficient
ecosystems.
SSRN Electronic Journal,
Год журнала:
2023,
Номер
unknown
Опубликована: Янв. 1, 2023
Recently,
there
has
been
a
growing
trend
in
incorporating
Artificial
Intelligence
(AI)
into
financial
decision-making,
prompting
concerns
about
the
transparency
and
accountability
of
these
intricate
systems.
This
study
investigates
impact
Explainable
(XAI)
approaches
alleviating
improving
decision-making
processes.
The
paper
commences
by
outlining
current
landscape
AI
applications
finance,
underscoring
complex
opaque
nature
advanced
machine
learning
models.
lack
interpretability
models
presents
significant
challenge,
as
stakeholders,
regulators,
end-users
often
struggle
to
comprehend
reasoning
behind
AI-driven
decisions.
opacity
raises
questions
regarding
trust,
particularly
critical
scenarios.
primary
focus
research
centers
on
analysis
implementation
XAI
techniques
introduce
Various
methods,
including
rule-based
systems,
model-agnostic
approaches,
interpretable
models,
are
scrutinized
for
their
effectiveness
producing
understandable
explanations
explores
how
can
be
tailored
meet
distinct
requirements
domain,
where
is
essential
regulatory
compliance
stakeholder
confidence.
Moreover,
delves
potential
mechanisms
within
institutions.
By
offering
model
outputs,
not
only
enhances
but
also
empowers
professionals
identify
rectify
biases,
errors,
or
unethical
behaviour
algorithms.
promoting
accountability,
addresses
ethical
facilitates
responsible
trustworthy
deployment
sector.
This,
turn,
contributes
advancement
fair,
reliable,
secure
Digital Economics Review.,
Год журнала:
2024,
Номер
1(1)
Опубликована: Март 26, 2024
This
study
aims
to
see
the
development
of
research
on
topic
"AI
Finance"
and
plans
that
can
be
carried
out
based
journals
published
theme.
uses
qualitative
method
with
bibliometric
analysis
approach.
The
data
used
is
secondary
theme
which
comes
from
Dimension
database
a
total
127
journal
articles.
Then,
processed
analyzed
using
VosViewer
application
aim
knowing
map
in
world.
results
found
author
mapping
authors
who
most
were
Bhattacharjee
A;
Al-Gasaymeh
A.S;
Arakpogun
E.O;
Wang
X;
Sharma
S;
Arner
D.W;
Yang
J;
Krishna
S.H;
Khan
Singh
R;
Bansal
Raffinetti
E;
Marwala
T.
Furthermore,
keyword
mapping,
there
are
5
clusters
words
development;
challenge,
accounting,
opportunity,
economy,
blockchain,
use.
path
topics
related
AI
Finance
Islamic
Finance,
Enterprise
Development
Behavioral
Green
AI,
Access
Accounting.
