Towards an AI policy framework in scholarly publishing
Trends in Cognitive Sciences,
Год журнала:
2024,
Номер
28(2), С. 85 - 88
Опубликована: Янв. 8, 2024
Язык: Английский
Towards an AI policy framework in scholarly publishing
Опубликована: Окт. 1, 2023
Generative
artificial
intelligence
(AI)
tools
are
rapidly
transforming
scholarly
research.
To
harness
their
benefits
while
minimizing
potential
pitfalls,
robust
policies
urgently
needed.
In
analyzing
AI
for
authors
and
reviewers
from
major
publishing
organizations
leading
journal
groups,
we
found
not
only
marked
inconsistencies
in
permitted
usage
reporting
requirements,
but
also
a
general
lack
of
clarity
or
guidance
on
ethical
principles,
transparency,
reproducibility.
Furthermore,
the
restrictions
set
by
these
largely
unenforceable.
establish
norms
best
practices,
propose
pragmatic,
enabling
principle
that
encourages
to
produce
work
with
assistance,
emphasizing
responsible
disclosure
over
unenforceable
restrictions.
Building
this
strengths
existing
policies,
offer
ready-to-use
author
reviewer
policy
templates.
When
systems
used
beyond
typical
editing
services,
should
report
details
tool,
its
usage,
generated
content,
prompts.
Reviewer
articulate
confidentiality
risks.
Our
analysis
templates
roadmap
building
consensus
practices
integration
into
routines,
thereby
accelerating
discovery
without
compromising
academic
integrity.
Язык: Английский
The art of deception: humanizing AI to outsmart detection
Taseef Ayub,
Rayees Ahmad Malla,
Mashood Yousuf Khan
и другие.
Global Knowledge Memory and Communication,
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 14, 2024
Purpose
The
study
aims
to
investigate
the
influence
of
HIX.AI,
an
artificial
intelligence
(AI)
tool
that
humanizes
generated
content,
on
detection
capabilities
AI-generated
text
detectors.
Design/methodology/approach
investigates
reliability
six
content
tools
by
passing
ten
essays,
five
each
using
Chat
Generative
Pre-Trained
Transformer
(ChatGPT)
and
Bard
(Gemini)
before
after
through
which
content.
Findings
found
selected
detectors
identified
with
inconsistencies.
Some
essays
were
falsely
as
human-written
a
few
detectors,
indicating
are
unreliable.
Post-HIX.AI
application
all
passed
except
two,
mixed
two
separate
Practical
implications
findings
present
evolving
field
can
bypass
highlighting
difficulties
in
identifying
presence
humanization
tool.
Passing
has
serious
consequences,
especially
academics.
Hence,
recommends
more
robust
distinguish
accurately.
Originality/value
contributes
existing
literature
AI
highlights
challenges
pose
Язык: Английский