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A Scholarly Denition of Articial Intelligence (AI): Advancing AI
as a Conceptual Framework in Communication Research
Homero Gil de Zúñiga
a,b,c
, Manuel Goyanes
d
, and Timilehin Durotoye
b
a
Democracy Research Unit, Political Science, College of Law & Public Administration, University of Salamanca,
Salamanca;
b
Media Effects Research Lab, Film Production & Media Studies Department, Donald P. Bellisario
College of Communications, Pennsylvania State University, State College;
c
Facultad de Comunicación y Letras,
Universidad Diego Portales, Santiago;
d
Department of Communication, Universidad Carlos III
ABSTRACT
Research on Articial Intelligence (AI) in communication research is
gaining broader interest. This interdisciplinary interest has yet to be
supported by a systematic scholarly denition and by a holistic theo-
retical framework in communication research. First, combining prior
theoretical eorts from diverse disciplines in the social sciences, espe-
cially journalism and communication, this study introduces a wide-
ranging working AI scholarly denition in communication research as
the tangible real-world capability of non-human machines or articial
entities to perform, task solve, communicate, interact, and act logically as
it occurs with biological humans. We also propose its theoretical oper-
ationalization based on two dimensions: level of performance and level
of autonomy, advancing an elementary conceptual framework draw-
ing on AI’s levels of potential actions or performance the AI may
accomplish, including 1) performing tasks, 2) taking decisions, and 3)
making predictions; as well as AI’s level of autonomy, or the agency
results contingent on the degrees of human input, interaction, or
supervision involved.
KEYWORDS
Artificial intelligence; AI;
AI performance level;
AI autonomy level;
social sciences;
communication; journalism
Present-day society is saturated by digital infrastructures, supported by artificial technolo-
gies, which have become essential in navigating the social world (de Lima-Santos & Ceron,
2021; Hepp, 2020; Lobera et al., 2020). Rapid advances in Artificial Intelligence (AI) have
significantly disrupted the world economy and other sectors like engineering, agriculture,
politics, and media, developing technological-driven systems in the process of information
and service creation, dissemination, and preservation (Birtchnell, 2018; Kamble & Shah,
2018; Kieslich et al., 2022; Tubaro et al., 2020). The penetration, use, and reliance on AI have
various implications for different disciplines, including communication.
Some studies in communication and journalism have problematized the potential
impacts of AI in shaping users’ attitudes, perceptions, and behaviors (Brewer et al., 2022;
Chen & Wen, 2021). For instance, AI is redefining present-day journalism through the
introduction of AI-infused workflow systems, the use of data in news stories, and the
reorganization of journalistic labor and recruitment (Deuze & Beckett, 2022; Sun et al.,
2022). AI also has implications in communication, particularly in persuasive communica-
tion, in which scholars focus on the interplay between technology and persuasion to
CONTACT Homero Gil de Zúñiga hgz@usal.es Democracy Research Unit, Political Science, College of Law & Public
Administration, University of Salamanca
POLITICAL COMMUNICATION
https://doi.org/10.1080/10584609.2023.2290497
© 2023 Taylor & Francis Group, LLC
investigate how artificial entities like robotics and algorithms (Dehnert & Mongeau, 2022;
Hermann, 2022; Lee & Liang, 2018; Siegel et al., 2009) may influence persuasion across
diverse contexts, including political and public opinion (Cohen, 2021).
In journalism, this technology has reshaped modern investigative reporting, news pro-
duction, and distribution while targeting users based on their news preferences (Stray, 2019)
by employing cutting-edge AI tools (de Lima-Santos & Ceron, 2021). Thus far, researchers
have identified three phases of computerization in newsrooms: automated information
generation, automated content production, and information distribution and audience
relations (Sánchez-García et al., 2023). Each phase influences news organizations’ utiliza-
tion of machine learning to predict story virality, model audience consumption patterns
(Stray, 2019), and monetize website subscriptions (Goyanes et al., 2023). Additionally,
personalization recommenders, bots, and algorithms have been used by major newsrooms
such as The New York Times, Reuters, China Morning Post, and news aggregators like Google
News and Yahoo News (Das et al., 2007; Sánchez-García et al., 2023).
Considering the recent academic interest in AI within communication and journalism
studies and the implications of its development, it is imperative to reconcile the different
research silos that have been evolving with the consolidation of AI in communication-
related phenomena. However, little theoretical research has provided and operationalized
a definition of AI and a conceptual framework for its empirical application in communica-
tion research. We believe this task is timely and relevant as it helps clarify empirical
discourses on AI’s nature, use, and effect and their social, political, cultural, and ethical
implications in society. This study introduces a wide-ranging working AI scholarly defini-
tion in communication research as the tangible real-world capability of non-human
machines or artificial entities to perform, task solve, communicate, interact, and act logically
as it occurs with biological humans. We also propose its theoretical operationalization based
on two dimensions: level of performance and level of autonomy, advancing an elementary
conceptual framework drawing on AI’s levels of potential actions or performance the AI
may accomplish, including 1) performing tasks, 2) taking decisions, and 3) making predic-
tions; as well as AI’s level of autonomy, or the agency results contingent on the degrees of
human input, interaction, or supervision involved.
Articial Intelligence: Impact in Journalism and Communication
The concept of AI dates back to 1955 when Prof. John McCarthy defined the scientific
procedures of machine learning (de Lima-Santos & Ceron, 2021). Over the years, this
technology has revolutionized how intelligent entities are computed to simplify and per-
form human tasks, make decisions, and solve problems (Broussard et al., 2019). For
decades, academic focus on AI mainly flourished in computer science with greater con-
centration on subfields such as “(i) machine learning (Ng & Leung, 2020); (ii) computer
vision- CV (Russell & Norvig, 2022); (iii) speech recognition (Reddy, 1976); (iv) natural
language processing – NLP (Allen, 2003); (v) planning, scheduling, and optimization
(Wölker & Powell, 2021); (vi) expert systems (Benfer et al., 1991); and (vii) robotics” (de
Lima-Santos & Ceron, 2021, p. 3).
The scope of AI, however, has extended globally. Journalists and communication experts,
for instance, have used computational-based aid to improve their reporting (Broussard,
2015), develop more robust investigative journalism (Stray, 2019), and foster old and new
2H. GIL DE ZÚÑIGA ET AL.
journalistic practices, contributing to better and more informed public opinion (Moran &
Shaikh, 2022). AI automation has proven instrumental to journalism and investigative
reporting (de-Lima-Santos & Ceron, 2021; Borchardt, 2022). Conversely, the era of pseudo-
information (Kim & Gil de Zúñiga, 2021), post-factual ideas (Moon et al., 2022) have
thrived in part due to the contribution of AI tools that have sparked the emergence of fake
news generators, video games, and metaverse (Godulla et al., 2021; Mattke, 2018).
In communication, AI is changing social interactions, developing technologies adapted
to online and offline communication (Guzman & Lewis, 2020). AI aims to intelligently
replicate human communication abilities aided by computational resources (Frankish,
2014). Due to the changing dynamics in the connection between AI technologies and
communication, new inquiries are being raised on how AI fits into the communication
paradigms (Gunkel, 2012).
Generally, the two principal dimensions in communication technology research are
human-computer interaction (HCI) and computer-mediated communication (CMC),
which explain how AI might function as a communicator or a channel for human social
interactions (Sundar & Nass, 2000). These dimensions are based on the usability and the
ability to interpret social cues by human interlocutors. Recently, these subfields have been
classified into human-AI interaction (HAII), a derivative from the Theory of Interactive
Media Effects (TIME) model proposed by Sundar (Sundar & Nass, 2000), and AI-mediated
communication [AIMC] (Hancock et al., 2020). Thus, the conceptual advances have
facilitated the contextualization of AI as a rise in machine agency, conversational agents,
and embodied robots (Beattie et al., 2020; Westerman et al., 2020). These versatilities
explain how AI assists and streamlines communication (Reeves, 2016). Nevertheless, the
field still needs a simple communication research framework that facilitates the study of AI
for such interactions.
With the growing adoption of AI in communication, its functionality has extended from
one – to-one encounter to one – to–many, thereby uncovering new assumptions about the
human communication process, including deeper socio-emotional interactions (Lee &
Sundar, 2009), human-AI socialization and “friendship” (Brandtzaeg et al., 2022), and
autonomy – as visible in algorithmic assistants, work bots, AI co-authorship, and AI
curatorship (Banas et al., 2022; Duan et al., 2022; Hepp, 2020). These facets also highlight
AI’s role as an intelligent-agent communicator in contrast to a simple mediator role and the
degree of agency it affords humans versus machines in journalism, persuasion, and com-
munication (Dehnert & Mongeau, 2022; Sundar & Lee, 2022).
AI is also utilized to resolve issues of hate speech and mis/disinformation online and on
social media (Banas et al., 2022); conduct sentiment analysis, information pattern extraction,
visual analysis, and speech recognition (Ryan, 2020; Vergeer, 2020). Additionally, commu-
nicative bots as artificial companions are becoming more popular among users (Hepp, 2020)
and E-commerce enterprises for sales conversions and learning about customers’ purchasing
behaviors via recommender systems (Sun & Zhang, 2018). However, there are projected
benefits to the future scenarios of AI technology and drawbacks to its malicious uses at
individual and organizational levels (Nah et al., 2020; Araujo et al., 2020; Canavilhas, 2022;
Ciancaglini et al., 2020; Helberger & Diakopoulos, 2022; Natale, 2021).
Despite the advantages of AI, there are ethical, privacy, and authenticity concerns about
its deployment (Hermann, 2022; Kieslich et al., 2022). For instance, there are issues of lack
of transparency and inaccuracy of AI-embedded systems in learning about users’
POLITICAL COMMUNICATION 3
preferences (Jokinen, 2015), or the “cold start problem,” where inferences about users may
not be accurately drawn (Gope & Jain, 2017; Lee, 2020). Some examples are machine
learning algorithms used in recommender systems in social media platforms like Tik Tok,
Instagram, YouTube, or media streaming services like Netflix, Hulu, Apple TV, etc. There
are also uncertainties about increased machine autonomy as AI gains consciousness and
becomes self-aware to accelerate attention processing and emulate human-level intelligence
like ChatGPT (i.e., GPT-4) and DALL-E (Chella & Manzotti, 2013; Ng & Leung, 2020).
Several of these adverse effects of AI inspired the launch of the European AI Act in 2021 by
the European Commission to provide binding ethical guidelines for private and public
integration of AI (Helberger & Diakopoulos, 2022).
Relatedly, Volovici et al. (2022) mention how the abuse of AI in some healthcare systems
can propagate prejudices like racism and sexism. Generative AI, a deep learning machine
model that creates high-dimensional data from simple prompts, like texts, images, or work-
able prototypes in response to users’ commands (i.e., ChatGPT, and Dall-E), has been
identified as a potential threat to journalists (Jamil, 2021; Ciancaglini et al., 2020) and
marketers due to the ability of AI to perform tasks accurately and quickly (Pavlik, 2023).
Although AI tools are expected to reduce the costs of investigative journalism, current meta-
journalistic conversations begin to suggest otherwise due to the uptake of algorithmic
processes that undermine human involvement in news production (Borchardt et al., 2019;
Guzman, 2018; Moran & Shaikh, 2022). Likewise, there are predictions of the death of
journalism values as journalists grapple with over-reliance on robots in the newsrooms,
raising ethical debates on how AI is appropriated in the news (Moran & Shaikh, 2022).
Scholarly Denition of Articial Intelligence in Communication Research
The study of AI in communication has relied on broad descriptions from computer science
and engineering, sociology, and legal studies as “learning algorithm used to approximate
some form of intelligence operating within computing machines” (Ninness & Ninness,
2020, p. 100). In some cases, AI has been subsumed as automation in media use and
production (Hepp, 2020) and categorized as general AI, which explains intelligent systems
designed to conduct limited tasks as opposed to possessing similar or higher human
intelligence (Ryan, 2020). The role of AI in communication and newly proposed conceptual
parameters – human-AI interaction (HAII) and AI-mediated communication, are dis-
cussed by scholars based on factors like source orientation and social exchanges optimized
by machine learning (Guzman & Lewis, 2020; Sundar & Lee, 2022).
Furthermore, there is a lack of operationalization of different constructs to assess the
impact of AI on democracy, news media, social and political attitudes, and perceptions such
as public trust (Chen & Wen, 2021).
We define AI as the tangible real-world capability of non-human machines or artificial
entities to perform, task solve, communicate, interact, and act logically as it occurs with
biological humans. AI may not solely depend on human intelligence to define its existence,
and we argue that its operationalization is based on two parameters: level of performance
and level of autonomy. Accordingly, this study advances an elementary conceptual frame-
work drawing on AI’s levels of performance, or the actions AI may accomplish, which
include 1) performing tasks, 2) making decisions, and 3) making predictions, as well as AI’s
level of autonomy, as the agency results contingent on the degrees of human input,
4H. GIL DE ZÚÑIGA ET AL.
interaction, or supervision involved. Like humans, AI will disclose different levels of
intelligence based on its performance and autonomy capabilities.
Table 1 shows that AI is mostly defined as computational technique capable of actualiz-
ing specific tasks, mimicking human behaviors, cognitive heuristics, and intelligence with-
out considering the relationship between human-human and human-machine
communication patterns. Although none of definitions listed in the table below include
all the different attributes proposed by the current definition offered in this article, we
advance a holistic AI theoretical framework here that can largely be applied regardless of
which academic definition is finally employed.
AI as a Conceptual Framework in Communication Research
Drawing on current communication studies and building on our definition of AI, we argue
that AI embraces a notion that may be categorized into two levels: 1) the depth of analysis
that AI can perform specific tasks, make decisions, communicate, interact, and forecast
results, resulting in unique output, and 2) the degree to which AI requires or does not
require human supervision or interaction. Based on these two features, our framework for
explicating AI in communication research stands as follows:
Level of performance
This level of analysis, which is consistently but dispersedly discussed in the literature as an
inherent feature of AI (Gunkel, 2012; Lee et al., 2023; Park et al., 2021), is implicitly
appraised by extant research but lacks the consistent operationalization we seek to present
next. It entails the potential actions of AI, including 1) performing tasks, 2) making
decisions, and 3) making predictions. These three categories should be understood as
complex actions with different levels of interaction and mutual feedback, as well as an
initial approach to understand and advance a historically primitive framework of AI in
communication (see Figure 1).
Figure 1 illustrates that AI may achieve different performance levels individually (single
level), in a dual interaction (second level), in a triple interactive mode (third level), or as
a series of events occurring one after the other (i.e., perform tasks – make decisions, or make
decisions – make predictions, or perform tasks – make predictions – make decisions).
Performing a task, for instance, can be theoretically assumed to be a simple action for AI,
but as we proceed to deciding or predicting, the level of complexity increases. The level of
complexity rises as the potential connections between these activities are also triggered: for
example, decisions based on predictions (i.e., Gmail and Outlook text predictions,
Grammarly, Google editor) or predictions based on decisions (i.e., Canva and Google
search engine). What we argue here is that the output of AI depends on the intricacies of
an AI interaction between its performative levels, 1) performing tasks, 2) making decisions,
and 3) making predictions to produce results, and the quality of inputs an AI initially
receives or its level of autonomy.
AI’s level of performance can be understood through automated decision-making, which
describes actions conducted using advanced technologies and without human involvement
to make data-driven decisions (Dodge & Kitchin, 2007; European Commission, 2018). In
other words, algorithms are utilized to gather, process, and generate data for making
POLITICAL COMMUNICATION 5
Table 1. Current definitions of AI in communication research.
Author (s) Article Journal/Publisher Definition of AI
Castro and New (2016, p. 2) The Promise of Artificial Intelligence Center for Data Innovation The process of “creating computing machines and systems
that perform operations analogous to human learning
and decision-making.”
Ng and Leung (2020, p. 1) Strong Artificial Intelligence and Consciousness Journal of Artificial Intelligence
and Consciousness
“Artificial Intelligence (AI) is about emulating the human
intelligence process by machines.”
de Lima-Santos and Ceron (2021) Artificial Intelligence in News Media: Current Perceptions
and Future Outlook
Journalism and Media It is a step-by-step process for performing repetitive actions,
designing models, and solving technical problems
without pre-existing concrete solutions.
Nah et al. (2020) Communicating Artificial Intelligence (AI): Theory,
Research, and Practice
Communication Studies It is portrayed as the interactions between virtual and
human agents across social, cultural, political, and ethical
domains to enable machine imitations of human
behavior and thoughts.
European Commission,
Directorate-General for
Communications Networks,
Content, and Technology
(2021)
Proposal For a Regulation Of The European Parliament and
of The Council Laying Down Harmonized Rules on
Artificial Intelligence (Artificial Intelligence Act) and
Amending Certain Union Legislative Acts
European Union AI Act AI systems are defined in an extensive framework, with risk-
based and social-good approaches, which comprises
automation in the form of structured or unstructured
data acquisition, content creation, moderation, and
production, predictive and recommendation systems
employed for decision-making, and targeting users.
Guzman and Lewis (2020) Artificial intelligence and communication: A Human-
Machine Communication research agenda
New Media & Society The concept of AI is focused on understanding human
intelligence and developing technologies to perform
tasks associated with human cognitive and emotional
abilities.
Hancock et al. (2020, p. 90) AI-Mediated Communication: Definition, Research
Agenda, and Ethical Considerations
Journal of Computer-Mediated
Communication
AI “refers broadly to computational systems that involve
algorithms, machine learning methods, natural language
processing, and other techniques that operate on behalf
of an individual to improve communication outcome.”
Russell (2010, p. 4) Artificial Intelligence: A Modern Approach Pearson Education “A computational rational agent that acts given inputs
(percepts) to achieve the best-expected outcome.”
Kurzweil (1990, p. 117) The Age of Intelligent Machines MIT press Cambridge “The art of creating machines that
perform tasks that require intelligence when people carry
them out.”
Jamil (2021) Artificial Intelligence and Journalistic Practice: The
Crossroads on Obstacles and Opportunities for Pakistani
Journalists
Journalism Practice In journalism, AI extends beyond machine storytelling. It is
described as the processing of natural language and the
incorporation of transformed data, algorithms, and
automated modes of news production and dissemination
aided by news bots.
(Continued)
6H. GIL DE ZÚÑIGA ET AL.
Table 1. (Continued).
Author (s) Article Journal/Publisher Definition of AI
Kaplan and Haenlein (2019, p. 17) Siri, Siri, in my hand: Who’s the fairest in the land? On
the interpretations, illustrations, and implications of
artificial intelligence
Business Horizons “a system’s ability to interpret external data correctly, to
learn from such data, and to use those learnings to
achieve specific goals and tasks through flexible
adaptation.”
Sundar (2020) Rise of Machine Agency: A Framework For Studying the
Psychology of Human–AI Interaction (HAII)
Journal of Computer-Mediated
Communication
It entails AI-driven mass personalization of communication
content.
Ryan (2020, p. 3) In AI We Trust: Ethics, Artificial Intelligence, and Reliability Science and Engineering Ethics “It is a field of computer science that focuses on computer
processes that can often function and react in human-like
ways, such as image recognition (vision), speech
recognition (hearing), and natural language generation
(speaking).”
Carlson (2015) The Robotic Reporter: Automated Journalism and the
Redefinition of Labor, Compositional Forms, and
Journalistic Authority
Digital Journalism It is defined as a set of algorithmic processes designed to
generate and distribute media output like texts and
images for general use, with limited or no human
intervention.
Russell and Norvig (2022) Artificial intelligence: a modern approach (4th ed.). London: Pearson Education “Study of agents that receive precepts of the environment
and perform actions.”
The United Nations Information
Economy Report (UNCTAD,
2017, p. 5)
Information Economy Report: Digitalization, Trade, and
Development
United Nations Publication “AI refers to the capability of machines to imitate intelligent
human behavior. This may involve performing various
cognitive tasks, such as sensing, processing oral
language, reasoning, learning, making decisions, and
demonstrating an ability to manipulate objects
accordingly”
POLITICAL COMMUNICATION 7
decisions in some aspects, including political, cultural, economic, and ethical (Elish & Boyd,
2018; Kitchin, 2017). Gunkel (2012) and Natale (2021) emphasized that AI should be
viewed from a technical and material functioning of computing technologies or machine
intelligence, which is pivotal in modeling the relationship between humans and machines.
In digital journalism, social bots are employed to create a scenario where AI tech-
nologies are active producers of communication, including news content (Natale, 2021).
Tong and Walther (2011) mention the use of algorithms and search engines for data
mining news leads and crafting prose to refine news stories. This indicates a practice of
computational journalism where algorithmic tools are employed for tracking topics on
public affairs (Appelgren & Nygren, 2014), analyzing big data sets (Andersen, 2018),
fact-checking falsified information, and restructuring journalistic processes (Lewis et al.,
2020). Some examples include Open Refine (openrefine.org) and Dedupe.io (DataMade,
2016).
Furthermore, AI may perform tasks and make predictions in aggregated forms.
Vergeer (2020) reveals that the gatekeeping function of journalists is disrupted with
the use of machine learning algorithms to build paywalls and recommender systems
that help journalists learn about the audience’s interests (Shaw et al., 2021;
Gruszczynski & Wagner, 2017). It makes decisions using proprietary content man-
agement systems and statistical programs. Automated news is becoming a trend in
digital journalism (Broussard, 2015), with several machine-written news pieces being
created across multiple languages within minutes (Dörr, 2016; Hansen et al., 2017).
Nevertheless, the importance of AI is related to automated news articles, as well as
Figure 1. Venn Diagram Showing Examples of Artificial Intelligence Tools based on their levels of
performance.
8H. GIL DE ZÚÑIGA ET AL.
newsroom planning, post-production, and optimization via chatbots and other AIs
such as Quakebot (Salaverría & de Lima-Santos, 2020), ChatGPT, and Dall-E
(Moran & Shaik, 2022).
Drawing on our AI framework, we argue machine-human interaction and online
communication is also changing. Institutions are increasingly adopting machine agents
or chatbots to carry out a variety of communicative tasks previously designated to
humans, such as customer support (Adam et al., 2021), scheduling appointments
(Srinivas & Ravindran, 2018), and personal banking (Umamaheswari et al., 2023). AI
has also permeated everyday human conversations that can manage people’s mental
health (Adam et al., 2021), convey nonverbal expressions with emojis (Prada et al.,
2018), introduce identity shifts (Gonzales & Hancock, 2008), and foster relationships
(Tong & Walther, 2011). AI models like ChatGPT and DALL-E (Zhang et.al., 2023),
developed by research lab OpenAI, have made headlines for their ability to function as
meaningful AI. Only levels of performance and the level of autonomy will dictate how
this AI will affect the world in the future.
Following the analyses of AI’s functioning, a theoretical evaluation of these AI
activities denotes that they should be understood as multilayered actions that require
the interaction of single, dual, or triple activities at different levels. Moreover, the
level of intricacy increases as mutual feedback and potential connections between
these AI actions are systematically triggered. Digital streaming service Netflix, for
instance, provides customized recommendations based on users’ viewing history and
likes (Sun & Zhang, 2018).
Major digital companies like Google, Alibaba, Amazon, and Apple have intro-
duced conversational AI to revolutionize information systems. These bots are inte-
grated as AI assistants such as Alexa (Amazon), Siri (Apple), Google Assistant, and
Bard (Google’s chat GPT-like AI), which can process instructions, engage in chit-
chat, and allow users to access information (Sun & Zhang, 2018). Figure 1 shows the
majority of present-day AI activities, portraying the three integral actions and their
relationships.
Deuze and Beckett (2022) reference how AI-infused systems create news stories and
gather public issues that resonate most among audiences through various websites or
applications. There are cases of Twitter bot indicators - Botometer and Tweetbotornot -
performing tasks like detecting automated accounts that collate tweets used to pro-
mote political discourses (Martini et al., 2021). With the rapid advancement in AI
technology, sophisticated predictions are made using, for instance, voice recognition
applications to detect health-related issues (Schulz, 2017), or algorithms to discover
hidden trends from big data that might provide insights into relevant news topics
(Stray, 2019).
Hansen et al. (2017) and Marconi and Siegman (2017) mentioned the impact of machine
intelligence in monitoring global feeds, drawing patterns from big data sets and collating the
results in writing a news story that can be shared with the audience. Today, the prevalence
of AI technologies has made such theorizing a reality with several examples of machine
learning used in interpersonal and computer-mediated contexts (Beattie et al., 2020;
Westerman et al., 2020) and by news organizations (Marconi & Siegman, 2017; Schmitt,
2019; Wölker & Powell, 2021). However, these AI’s performance levels are not exclusive to
journalism or communication disciplines.
POLITICAL COMMUNICATION 9
Level of autonomy
This level of analysis provides a nuance on perceptions of the potential of AI-Human
interaction and has been a debate in the literature (Hancock et al., 2020; Milano et al., 2020).
Theoretically assumed to be of high or low levels of autonomy, AI’s autonomy discerns the
agency result of the potential degree of AI or human input, interaction, or supervision
involved. Fortunati (2018) references how AI communicative bots are designed as auton-
omously operating systems to facilitate quasi-communication between humans, and AI
possesses different interfaces of communicative autonomy. The function of communicative
bots depends on datafication and continuous feedback, which determines if a system is
partially or fully automated, as well as the variation of oversight it affords individuals
(Araujo et al., 2020; Esposito, 2017). So, the larger the amount of data processing capabilities
of any given AI, the higher the automation and extent of mediatization (Andersen, 2018)
and, ultimately, autonomy.
Some AI tools are developed to be “functionally automatic, to act when triggered without
any regular human intervention or oversight” (Gillespie et al., 2014, p. 170) This indicates
high visualization of information from databases or the internet. AI companions such as
Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, Google’s Assistant, and Shazam are
autonomous systems that meet human socio-communication needs (Diakopoulos, 2016;
Hepp, 2020; Selwyn & Gallo Cordoba, 2022). Nevertheless, these AI tools provide various
degrees of control for users. Shazam, for instance, gives users extended control over their
music consumption because it is designed to identify songs via a smartphone’s microphone
and propose music using search results saved in users’ databases (Hepp, 2020). This
conceptualization of AI-enabled interaction, thus, deals with humans’ control over AI
and not the opposite. Results of a fully supervised AI technology may imply different
societal, economic, or human risks, assuming that if humans fully control AI technologies,
this may influence fewer risking outputs, as opposed to a fully autonomous AI technology
close to the cutting-edge technological singularity.
Discussion
Many news agencies employ AI tools to enhance their users’ experiences (Lee et al., 2023)
and improve automated news production and distribution (Deuze & Beckett, 2022; Moran
& Shaikh, 2022), redefining our society (Barredo-Ibáñez et al., 2021; Dehnert & Mongeau,
2022; Siegel et al., 2009). AI represents a fundamental role in many strands of society, from
industries to scholarship, including communication as a field. Thus, it is crucial to define its
core processes and provide a framework aiming at capturing the broadness of its application
in communication research. Furthermore, as AI grows rapidly, predominant theories on
communication research only provide a partial perspective of AI’s framework. Hence, our
proposed definition of AI broadly conceives the complexity of AI based on its level of
performance and autonomy and the procedures involved. This study provides a holistic
scholarly definition of AI as the tangible real-world capability of non-human machines to
perform, task solve, communicate, and interact as it occurs with humans.
AI’s levels of performance and autonomy situate theoretical perspectives of
human-machine interaction and the variants of the practices conducted by AI.
These classifications will help scholars and users reimagine the different uses of AI
10 H. GIL DE ZÚÑIGA ET AL.
systems and their outcomes for various issues. Scholars, for instance, could investi-
gate the potential and effects of computational journalism to deepen conversations
on AI’s formative role in the newsroom and examine audiences’ reaction to AI-
generated news vs. journalist-written news. Future studies may explore the psycho-
logical effects of AI literacy on individuals’ expectations of how an algorithm’s
performance might help them distinguish between fake and real news, thereby
increasing fake news literacy among audiences who consume news from social
media or alternative media, which are known as hotspots for the spread of mis-
information (Lee et al., 2023; Mason et al., 2018).
Moreso, there is an uncanniness in how AI can accentuate social and digital
disparities. With the growing popularity of generative AI models like Chat GPT,
GPT-3, and GPT-4, there is an opportunity to leverage AI’s performance and
autonomy in designing empirical studies on the personalization and human-like
conversational metrics of AI agents to explore the emotional intelligence in human-
AI interactions and its impact on users’ perceptions. Following the distinctions of
AI, it is clear that media and communication studies will pose different research
questions and attempt to address debates on the communicative relationship
between humans and AI, including ethical contentions.
Numerous AI challenges need to be faced by academics and policymakers. Diverse
AI-like communicative robots, for instance, are based on the premise of the accumula-
tion of individual private data, so there is a need for a clearer understanding of what
data is public or private when AI is embedded in users’ social spaces (Lutz & Tamó-
Larrieux, 2020) as developers make specific trade-off decisions that may threaten the
ethical design of AI tools. Additionally, it is critical to bring about the notion of AI
accountability, that is, who bears the responsibility for AI intrusion in users’ private
spaces (Gunkel, 2018). AI systems may run the risk of undermining transparency for
users concerning the machine learning process (Liao et al., 2020) and disclosure of the
methods of data collection and the extent of individuating information collected (Sundar
et al., 2013); tracking their data, and monitoring their digital footprints such as interests
and media use patterns.
Over time, different institutions like the European Union continually address such issues
through policymaking (Canavilhas, 2022) and the U.S. Congress is also considering AI’s
implications to society and the health of US democracy. The most recent case is the Tik Tok
Congressional Hearing on privacy invasion, data security, political propaganda, and child
safety (Shepardson & Ayyub, 2023; Tolentino, 2023). Despite the risks and vulnerabilities
caused by the advancement of AI, this Forum article aims to present a standard working
definition of AI in communication and provide a conceptual framework for understanding
how AI models are structured, extending research on the examination of their future
influence in society.
Disclosure statement
No potential conflict of interest was reported by the author(s).
POLITICAL COMMUNICATION 11
Funding
The work was supported by the Spanish National Research Agency’s Program for the Generation of
Knowledge and the Scientific and Technological Strengthening Research + Development [PID2020-
115562GB-I00].
Notes on contributors
Homero Gil de Zúñiga: Ph.D. in Politics at Universidad Europea de Madrid and Ph.D. in Mass
Communication at University of Wisconsin – Madison, serves as Distinguished Research Professor at
University of Salamanca, as Distinguished Professor of Media Effects & AI at The Pennsylvania State
University, and as Senior Research Fellow at Universidad Diego Portales. His work aims to shed an
empirical social scientific light over how social media, algorithms, AI, and other technologies affect
society. Relying on survey, experimental, and computational methods his work seeks to clarify the
way we understand some of today’s most pressing challenges for democracies.
Manuel Goyanes: Ph.D. in Journalism at University of Santiago de Compostela, serves as an Assistant
Professor at Carlos III University in Madrid and as a Visiting Fellow at the Democracy Research Unit
– DRU (University of Salamanca). His research addresses the influence of journalism and new
technologies over citizens’ daily lives, as well as the effects of news consumption on citizens’ political
knowledge and participation. He is also interested in global inequalities in academic participation, the
systematic biases towards global South scholars, and publication trends in Communication.
Timilehim Durotoye: Ph.D. student in the Donald P. Bellisario College of Communications at The
Pennsylvania State University. She earned her BA and MA degrees from the Department of
Communication and Language Arts, University of Ibadan, Nigeria. Using quantitative methods,
her research centers on the effects of new media, algorithms, and artificial intelligence (AI) on
human-social interaction, and long-debated/emergent political behavior capable of influencing
democratic processes.
ORCID
Homero Gil de Zúñiga http://orcid.org/0000-0002-4187-3604
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