# The social impact of Generative AI: An Analysis on ChatGPT

Maria T. Baldassarre
Department of Computer Science
University of Bari "A. Moro"
Bari, BA 70125
<EMAIL_ADDRESS>
Danilo Caivano
Department of Computer Science
University of Bari "A. Moro"
Bari, BA 70125
<EMAIL_ADDRESS>
Berenice Fernández Nieto
Department of Computer Science
University of Bari "A. Moro"
Bari, BA 70125
<EMAIL_ADDRESS>
Domenico Gigante
Department of Computer Science
University of Bari "A. Moro"
Bari, BA 70125
<EMAIL_ADDRESS>
Azzurra Ragone
Department of Computer Science
University of Bari "A. Moro"
Bari, BA 70125
<EMAIL_ADDRESS>

(September, 2023)

###### Abstract

In recent months, the social impact of Artificial Intelligence (AI) has gained
considerable public interest, driven by the emergence of Generative AI models,
ChatGPT in particular. The rapid development of these models has sparked
heated discussions regarding their benefits, limitations, and associated
risks. Generative models hold immense promise across multiple domains, such as
healthcare, finance, and education, to cite a few, presenting diverse
practical applications. Nevertheless, concerns about potential adverse effects
have elicited divergent perspectives, ranging from privacy risks to escalating
social inequality. This paper adopts a methodology to delve into the societal
implications of Generative AI tools, focusing primarily on the case of
ChatGPT. It evaluates the potential impact on several social sectors and
illustrates the findings of a comprehensive literature review of both positive
and negative effects, emerging trends, and areas of opportunity of Generative
AI models. This analysis aims to facilitate an in-depth discussion by
providing insights that can inspire policy, regulation, and responsible
development practices to foster a human-centered AI.

_Keywords_ AI Social Impact $\cdot$ ChatGPT Social Impact $\cdot$ Human-
centered AI $\cdot$ Perceptions on ChatGPT $\cdot$ AI Social Concern

##  1 Introduction

In recent months, the social impact of Artificial Intelligence (AI) has been
at the forefront of public debate due primarily to the introduction of new
software systems and technologies, specifically ChatGPT. The rapid development
of these technologies has, even more, sparked the debate regarding the
advantages, limitations, and risks of Artificial Intelligence’s expanding
capabilities. From healthcare to cybersecurity, generative models offer a vast
array of practical and prosperous future possibilities. However, concerns
regarding potential adverse effects present an opposing viewpoint, with
arguments spanning from privacy risks to deepening social inequalities. Models
like ChatGPT personalize the digital version of the Delphic oracle, where
people expect to find answers to their current problems by automating tasks,
seeking ChatGPT’s opinion on various issues, and even requesting advice.
Nonetheless, it is essential to question whether we are genuinely resolving
uncertainties or uncovering new ones regarding the scope, boundaries, and
prospects of generative models’ societal impact. Some other analysts have
referred to an "AI arms race" in which companies worldwide strive to showcase
the best technology, innovation prowess, and leadership in the AI market. On
the other side of the debate, discussions refer to the rapid development of
these models and the effectiveness of existing legal frameworks in
safeguarding against unintended adverse outcomes. Amongst all these debates,
unquestionably, Generative AI is currently undergoing a period of accelerated
evolution. This evolution inevitably brings about a social impact akin to the
ones experienced through numerous other technological advancements that
transformed our society in the past. To date, significant effects have been
observed in service provision, education, and scientific analysis. However,
more profound and concerning impacts also unfold in domains like democracy,
inequality, security, and military technology.

Consequently, a comprehensive examination and analysis are required to
understand the positive and negative social consequences, emerging trends, and
areas of improvement of generative models. These studies are needed to address
potential vulnerabilities and ensure the development of these technologies
considers the diverse social contexts and realities in which they are
deployed.

Building upon the preceding insights, this analysis adopts a comprehensive
approach to explore the societal ramifications and future trajectories of
Generative AI, with a specific emphasis on ChatGPT. The analysis is organized
as follows: Section 2 examines the potential impacts of ChatGPT across diverse
social sectors and the evolution of the debate across various spheres; Section
3 provides a brief overview of the state-of-the-art of Generative AI models as
well as a classification of these; Section 4 presents the study design, goals,
and Research Questions, plus the search strategy. Section 5 includes the data
analysis and synthesis, drawing conclusions from the literature review, and
sharing visualizations of our findings. Conclusion and future work close the
paper.

##  2 BACKGROUND

Throughout history, the advancement of technology has consistently brought
about significant transformations in social dynamics. Each new technological
breakthrough has sparked debates regarding scientific progress’ advantages and
potential hazards. Currently, this discourse encompasses various automated
tools, data collection and analysis, and the digitization of services, among
other emerging applications, which have become integral parts of modern-day
life.

These novel technological applications pervade numerous societal domains,
ranging from education to diplomacy, exerting a profound influence and
continuously reshaping various social processes. While there is a prevailing
and optimistic belief in the positive impact of technology on human progress
(McKendrick, 2019), it is also becoming increasingly apparent that these
disruptive forces could potentially engender unforeseen and unintended
consequences.

In informatics, sociology, philosophy, and politics, the development of
generative models will continue to ignite in-depth discussions on various
subjects. These discussions include topics such as regulation, risk
mitigation, liability, transparency, and accountability, as well as the
effects on socialization patterns and the trajectory of technological
development itself.

In our case, the significance of examining the social impact of ChatGPT stems
from its potential to cause significant social transformations, despite
ongoing debates regarding the magnitude of these changes. ChatGPT is a
powerful generative model that may impact power dynamics at multiple scales,
from individual interactions to broader social structures (Farrell et al.,
2022). This dynamic occurs in complex social environments marked by
disparities, stereotypes, conflicts, and various political and social
organization forms. These diverse social contexts, which surround scientific
advances, trigger unpredictable and immeasurable consequences that
fundamentally transform how we interact with each other and the world. When a
disruptive force permeates a new environment, it encounters various forms of
resistance. It triggers unintended negative consequences, demands protection
from potential vulnerabilities, causes uncertainty about whether it can be
regulated, and other related factors. This critical-resistant front contrasts
with a skeptical perception that questions the gravity of this new disruption
and the alarmist interpretations surrounding emerging technologies, in this
case. A third front embodies an optimistic outlook, emphasizing the manifold
benefits across multiple sectors, fostering enthusiasm for potential
advancements and enhancements, and envisioning their potential to address
significant challenges. Overall, the interaction between these forms of
resistance shapes perceptions of generative models’ societal impact and raises
critical questions about their implications for the broader social fabric.

These perspectives, as human behavior changes over time, interact and mutually
influence and foster one another. In the case of ChatGPT, we also observe
these attitudes, including optimism, pessimism, and skepticism, but the
panorama is even more complex as we present below in our literature review.

As evidence of the increasing interest in ChatGPT and AI, Figure 1 depicts the
fluctuations in Google search queries for "ChatGPT" globally from May 2020
(the release of ChatGPT) until May 2023 (which includes the time frame of our
research).

Figure 1: Search queries evolution on “ChatGPT” from May 2020 to May 2023 via
Google Trends
(https://trends.google.it/trends/explore?date=2020-04-28%202023-05-12&q=Chat%20GPT3&hl=en).
The "note" in the graph reflects an improvement in Google’s data collection
system implemented on 1 January 22.

Fig.1, furthermore, depicts a consistent low level of interest until December
2022, when a significant shift occurred, coinciding with the months following
the launch of Open AI’s ChatGPT as a prototype service on 30 November 2022
(Gordon, 2022), which attracted global attention. In April and May of 2023,
interest peaked.

As we enter the third wave of AI evolution, examining how socialization
processes change and assessing scientific innovations’ potential positive or
negative effects on rights and freedoms is critical. Following Pinch and
Bijker (Pinch and Bijker, 1984), it is essential to examine the construction
of scientific knowledge across different localities and contexts. As a result,
the primary goal of this analysis is to assess the evolution of ChatGPT in
recent years and to examine its current perceived impact on various social
aspects, in the context of the ongoing wave of AI evolution.

##  3 STATE OF THE ART

Generative pre-training (GP) was a well-known concept in machine learning
applications since 2012 (noa, 2012). Later, in 2017 Google introduced the
transformer architecture (Vaswani et al., 2017). These advancements led to the
birth of large language models like BERT in 2018 (Devlin et al., 2019) and
XLNet in 2019 (Yang et al., 2019): these are pre-trained transformers (PT) but
are not designed to be generative. A language model is a probability
distribution over sequences of words (Jurafsky and Martin, 2008): given any
sequence of words of length m, a language model assigns a probability to the
whole sequence. A Large Language Model (LLM) is a language model based on a
neural network with many parameters (typically billions or more). Prior to
transformer-based architectures, the best-performing neural Natural Language
Processing (NLP) models employed supervised learning from large amounts of
manually-labeled data.

The main drawbacks of using supervised learning are the impossibility to use
it on not well-annotated datasets, and also the prohibitive cost and time
required to train extremely large language models (Radford and Narasimhan,
2018). Usually, LLMs trained on a large quantity of data can perform
discretely a good number of tasks; anyway, they can be fine-tuned (i.e.,
further trained on specific data) to execute a specific task with better
performance.

Later, in 2018 OpenAI (OpenAI, 2018) published its famous article "Improving
Language Understanding by Generative Pre-Training", in which the first
Generative Pre-trained Transformer (GPT) system was introduced (Radford and
Narasimhan, 2018). GPT is a type of large language model (LLM) used mainly for
Generative AI, which is a type of AI capable of generating various kinds of
content, such as text and images, in response to instructions (also known as
prompts). Generative AI models learn the patterns and structure of their input
training data and then generate new data that has similar characteristics,
according to what has been asked as a prompt.

###  3.1 Foundational models

A foundational model is an AI model trained on broad data at scale such that
it can be adapted to a wide range of downstream tasks. The most famous and
performant GPT foundation models are the ones released by OpenAI. The most
recent is GPT-4 (OpenAI, a), for which OpenAI refused to publish the size and
training details due to business reasons (OpenAI, b).

Other such models include Google’s PaLM (Google, a) and Together’s GPT-JT
(Together, ), which has been reported as the closest-performing open-source
alternative to GPT-3. Meta as well has released a generative foundational
language model, named LLaMA (Meta, ). Foundational GPTs can also handle media
(and not only text), both for input and/or output. For example, GPT-4 is
capable of processing text and images as input but only produces text as
output.

###  3.2 Task-Specific Models

A foundational GPT model is usually further trained to better perform specific
tasks and/or handle subject-matter domains. One of the most used methods for
such adaptation is fine-tuning (beyond that done for the foundation model).
Fine-tuning is an approach in which the weights of a pre-trained (language)
model are trained on new data. One example of this is fine-tuning LLM to
comprehend and follow instructions: in January 2022, OpenAI introduced
InstructGPT (OpenAI, c) –a series of models which were fine-tuned to follow
instructions. The gained advantages included higher accuracy, less
negative/toxic sentiment, and generally better alignment with user needs.
Other examples of task-specific models are chatbots, AI systems that engage in
human-like conversation; ChatGPT (OpenAI, d) is currently the most famous
chatbot. Anyway, other major chatbots currently exist, such as Microsoft’s
Bing Chat (Bing, ) – which uses OpenAI’s GPT-4 (OpenAI, a) (as part of a
broader close collaboration between OpenAI and Microsoft) –, Google’s
competing chatbot Bard (Google, b) and LaMDA (Google, c), Jasper Chat (Jasper,
), Claude (Anthropic, a).

Finally, the text-to-model task is becoming quite popular. To date, some
famous models are Dall-E 2 (dalle2_ref, 2), Stable Diffusion (Diffusion, ),
PhotoSonic Art Generator (Writesonic, ) whose task is the production of images
based on user-provided textual prompts. Following the text-to-image models,
also the text-to-video task has been addressed with a lot of models, such as:
Runway (Runway, ), Meta’s Make-A-Video (Make-A-Video, ), Google’s Imagen Video
(Imagen, ) and Phenaki (Villegas et al., ). All these models can generate
video from text and/or text/image prompts.

###  3.3 Domain-specific models

GPT systems can be re-trained to address particular fields or domains.
Examples of such models (and apps) are BloombergGPT (Bloomberg, ) for the
financial domain, which should provide help with financial news and
information, SlackGPT (Slack, ) to support the Slack instant-messaging service
by providing help and guidance with navigating and summarizing discussions
(based on OpenAI’s API), BioGPT (Microsoft, ) for the biomedical domain, to
provide help with biomedical literature text generation and mining, CoPilot
(GitHub, ) for the IT source code development domain, to provide auto-
completion capabilities for developers.

Sometimes domain-specificity is realized via software components, specifically
named plug-ins or add-ons. For example, Google Workspace has available add-ons
such as GPT for Sheets and Docs – which is reported to aid the use of
spreadsheet functionality in Google Sheets.

##  4 STUDY DESIGN

To perform this review, we followed the protocol proposed in (Cartaxo et al.,
2018), and we completed the review process with the strategies presented in
(Kitchenham and Charters, 2007) for performing systematic literature reviews.
The following subsections describe in detail the study design and its
execution. The literature review presented in this work was carried out
through the following steps:

  1. 1.

Goal and Research questions: the goal and the correlated research questions
were identified to guide the literature review;

  2. 2.

Search strategy: defining the strategy to collect previous works published in
the literature, including research databases and query strings;

  3. 3.

Eligibility criteria definition: the criteria used to filter the collected
studies have been defined;

  4. 4.

Data extraction: defining how relevant data were extracted to help answer the
research questions;

  5. 5.

Data analysis and synthesis: defining how to organize extracted relevant data
to answer the research questions.

Fig. 2 summarizes the review protocol.

Figure 2: Research protocol used in the literature review

###  4.1 Goal and Research Question Definition

We formulated the following research questions to analyze the diverse
dimensions of ChatGPT’s impact.

Based on this goal, we defined the following research questions:

  * •

RQ1: What are the perceived positive and negative impacts of ChatGPT in
contemporary society?

  * •

RQ2: What are the emerging trends perceived in ChatGPT development?

  * •

RQ3: Which areas of improvement can be identified in the development of such
technologies?

###  4.2 Search strategy

The research has been split into two distinct parts: part one, focused on grey
literature, and part two, focused on white literature. Including both grey
literature and academic contributions allowed us to conduct a deeper
exploration of the impact of ChatGPT in various settings and from various
perspectives.

In part one – started on 29th November 2023 – we focused on grey-literature
sources, like blog posts and news articles from multiple domains – such as
business, education, technology, and society – emphasizing ChatGPT. Here our
goal was to feel the sentiment of the media and tech sphere and capture
feelings on ChatGPT that cannot emerge from white-literature sources.
Furthermore, some pilot searches on white-literature sources produced very few
results, which did not allow us to derive reliable and consistent conclusions.

The string used for part one was:

(“ChatGPT” AND “social concerns”) (“ChatGPT” AND “social impact”) (“ChatGPT”
AND “Human rights”) (“ChatGPT” AND “society*”) (“ChatGPT” AND “education*”)
(“ChatGPT” AND “ethics”)

This string was used to perform a keyword-based search on Google search
engine111https://www.google.it/; the search was performed in private-browsing
mode, after logging out from personal accounts and erasing all web cookies and
history (Piasecki et al., 2017).

In the end, this search resulted in 1230 literature sources.

While executing part one of the research, we continued executing periodic
pilot searches for white-literature sources.

In late February 2023, we noticed a notable change: the number of scientific
articles addressing ChatGPT increased significantly, encompassing diverse
approaches and perspectives. This may be due to the fact that academic papers
need more time to be reviewed by peers and published (w.r.t. blog posts and
news articles).

So, for part two of the research – started on 22nd February 2023 – we decided
to use Google Scholar222https://scholar.google.com/ as white-literature search
engine; here we searched for scientific articles of various fields – such as
business, education, technology, society, healthcare. The string used for part
two of the research was the following:

("Large Language Model" AND "Social impact") ("Large Language Model" AND Human
Rights") ("Large Language Model" OR "ChatGPT" AND "Ethics")("Large Language
Model" AND "ChatGPT" AND "Ethics") ("Large Language Model" OR "ChatGPT" AND
"Social concerns") ("Large Language Model” AND "ChatGPT" AND "society*")
("Large Language Model" AND "ChatGPT" AND "education*") ("Chat GPT" AND
"social concerns") ("ChatGPT" AND "social impact") ("ChatGPT" AND "Human
rights") ("ChatGPT" AND "society*") ("ChatGPT" AND "education*") ("ChatGPT"
AND "ethics")

In the end, this search resulted in 86 new literature sources.

All the documents obtained with this search strategy (both in part one and
part two) were surveyed using a 3-stages information classification process.
In the first stage, only the title and keywords of the collected articles were
read. In the second stage, we analyzed the abstract of each article while in
the third stage we read the complete article. All these stages were conducted
separately and in blind-view way by two of the authors. In case of a
disagreement, a third author manually verified and took the final decision.

All found publications were subjected to the selection criteria outlined in
Sec. 4.3 to determine their relevance for inclusion in the analysis.

###  4.3 Eligibility Criteria Definition and Data Extraction

The selection procedure used for filtering the identified pool of 1316 papers
was based on the following criteria:

  * •

for every text the content should be mainly related to ChatGPT and LLM,

  * •

the content should be written in English.

Then, the following criteria helped us to ensure we only included publications
providing substantial information to our analysis, especially on ChatGPT,
while we excluded papers with only brief mentions or tangential references:

  * •

For blog posts, we required the author’s name to be consistently provided, and
the blogs should be specialized in the relevant subject matter.

  * •

Regarding news articles, we preferred those that offered an extensive
analysis. We adopted this criterion because initial news coverage of ChatGPT
tended to be repetitive, often focusing on its capabilities and limitations
and just providing a brief history of the model.

  * •

In the case of academic articles, we sought diverse approaches to ensure a
comprehensive perspective rather than lean solely on a single field, such as,
for instance, the impact of ChatGPT in education.

After applying these selection criteria, we selected a total of 71 papers from
our initial pool of 1316 articles.

In the Data Extraction step, we extracted all relevant data that could help
answer any of the research questions. The extraction process was performed by
two of the authors and conflicts were solved by a third author in a blind-view
way. We used Atlas.ti333https://atlasti.com/ to tabulate and organize data.
More detailed information regarding the data and how it was indexed can be
found in the online appendix (Baldassarre et al., 2023).

##  5 DATA ANALYSIS AND SYNTHESIS

Part one of the search has been conducted from 29th November 2022 to 22nd
February 2023. Part two has been conducted from 22nd February 2023 to 19th May
2023. Table 1 details the results obtained in both parts, as well as the
documents selected once our selection criteria were applied.

Research phase |  Resources retrieved |  Resources analyzed |  Resources selected
---|---|---|---
First part, until Feb. 22 on Google |  1230 |  300 |  25
Second part, until May 19 on Google scholar |  86 |  63 |  46
Table 1: Amount of documents collected grouped by research phase.

In order to answer RQ1, RQ2 and RQ3 we performed an analysis using Atlas.ti.
Atlas.ti is a qualitative research tool that enables the systematic
organization of documentary resources by using codes and creating documentary
categories. Atlas.ti allowed us to visualize and intuitively present content
trends within the analyzed documents. We employed this tool to analyze the
selected papers, which were organized in Atlas.ti "document groups" following
the categories in Table 2.

Codes for text analysis in Atlas.ti
---
Positive Impact |  Negative Impact |  Emerging trends |  Areas for improvement
Benefits to education |  Disinformation risk |  Impact on the tech/AI market |  Need for appropriate regulation
Benefits to customer service |  Negative impact on freedom of expression |  Copyright uncertainty |  GDPR compliance concerns
Benefits of responses in real-time |  Bias concerns |  Uncertainty over liability for production failures |  Uncertainty of classification under the AI Act
… |  … |  … |

Table 2: Codes for text analysis in Atlas.ti

The categories and codes presented in Table 2 are derived from an in-depth
analysis of the selected papers. The codes were primarily developed "in vivo,"
meaning they emerged as potential units of analysis during the reading and
analysis process. For example, when repetitive references were made to the
potential positive impacts of ChatGPT in education, we created the code
"Benefits for education". After reviewing and analyzing the documents, the
codes were organized into four categories, which helped to address RQ1, RQ2,
and RQ3. The criteria for categorization emerged from a thoughtful reflection
on the codes and their contextual relevance. In the category of positive
impacts, codes such as "24/7 Availability" and "Personalized feedback" are
included since they are frequently mentioned as strengths of ChatGPT. On the
other hand, the category of negative impacts encompasses codes such as "Bias
concerns", which emerged as a recurring argument when discussing the
potentially detrimental effects of this model. Other codes like "Privacy
concern" and "Water footprint" were identified, further emphasizing the
importance of addressing these issues in the context of ChatGPT’s
implementation.

In the category of emerging trends, we captured the unforeseen consequences,
which, although potentially negative, arise unexpectedly from the evolution of
the model itself. Examples include "Copyright uncertainty" and the "Need to
clarify private sector liability", which pose challenges that trigger
transformations in particular domains such as the "Impact on the tech/AI
market". This category also encompasses unexpected challenges to the AI Act
(European Commission, 2021) and its coverage of generative models. Another
aspect within this category is "Unintentional misinformation", referring to
instances where the chat model provides unintentionally inaccurate
information, a matter currently under scrutiny by various experts.
Additionally, the category encompasses codes like "Skepticism about its actual
impact". These emerging trends shed light on the complex issues ChatGPT
presents.

The final category encompasses areas of improvement, focusing on aspects that
require further development to address all the adverse and unexpected effects
of the model. For instance, the tag "Negative outcomes mitigation" highlights
the need for more efforts to minimize adverse consequences. The categorization
of codes as "Uncertainty in data governance" also responds to the criteria as
an area of improvement rather than a negative impact. The above-mentioned
decision was made considering that current legal frameworks do not adequately
account for models like GPT, thus highlighting the need for specific measures
to address these cases, which will likely be developed in the coming years.
This category also encompasses improvement areas, such as "Limited up-to-date
information" and "Limited Medical terminology", emphasizing the potential for
enhancements.

After establishing the codes and categories, we visually represented their
distribution across the 71 documents. The resulting tree map in Atlas.ti,
depicted in Figure 3, displays the frequency of the most repeated codes.

Figure 3: Treemap- Atlas.ti with codes distribution

Table 3 also shows the frequency of codes, highlighting the least and most
recurrent codes within the Atlas.ti analysis.

Most recurrent | # | Least recurrent | #
---|---|---|---
Bias concerns | 39 | Unpredictability risk | 1
Disinformation risk | 25 | Prone to injection attacks | 1
Benefits for education | 22 | Over-regulation risk | 1
Privacy concern | 21 | Opportunity to increase renewable energy use | 1
Need for appropriate regulation | 20 | Need for using Renewable Energy Sources | 1
Discrimination risk | 18 | Need for human rights safeguards | 1
Unfairness in the data use in the model | 18 | Need for explainability and traceability | 1
Benefits for customer service | 17 | Need for Accessibility and Affordability | 1
Inaccurate answers | 17 | Limited Medical terminology | 1
Benefits for content creation | 15 | Lead people into extremist positions risk | 1

Table 3: Most and least recurrent codes; each category is associated with its
number of occurrences.

In addition, Fig.4 displays a Sankey diagram that graphically depicts the
distribution of code categories across the analyzed documents (in document
groups-unit). The diagram shows that scientific papers, blog posts, conference
symposiums, and other types of publications encompass all coding groups
(negative and positive impacts, areas of improvement, and emerging trends).
See Table A2 in online appendix (Baldassarre et al., 2023) for a description
of document categories. The articles category includes just emerging trends,
areas for improvement, and negative perceptions. Most document categories,
including scientific papers, columns, analyses, editorials, and articles,
exhibit a negative tendency. Notably, positive perceptions are more prevalent
in blog-posts, conference and symposium papers, and to a lesser degree in news
articles. "Bias concern" and "Disinformation risk" are two of the most common
codes contributing to negative perceptions. In contrast, "Benefits for
customer service" and "Multidisciplinary benefits" are the most prevalent
codes in the documents with a positive trend. Table A3 in online appendix
(Baldassarre et al., 2023) details tendencies and code frequency across all
document categories.

Figure 4: Sankey diagram illustrating the distribution of code groups across
document groups

##  6 Discussion

RQ1. What are the perceived positive and negative impacts of ChatGPT in
contemporary society?

Throughout our literature review, we identified several positive and negative
impacts attributed to ChatGPT. Noteworthy benefits include: the potential for
enhancing customer service; multiple papers emphasize the positive impact of
ChatGPT in this domain (International Journal of Human Rights Law Review,
2023; Ray, 2023; Paul et al., 2023; Hillemann and Zimprich, 2023; Davis, 2022;
Lock, 2022; Deo, 2023; Rivas and Zhao, 2023; Kumordzie, 2023; Levente, 2023;
Marr, 2023; Abdullah et al., 2022; Khowaja et al., 2023; Gupta et al., 2023;
Bruff, 2023; Iskender, 2023). The model is highlighted as an enabler of cross-
cultural dialogue, facilitating communication between individuals from
different cultural backgrounds (International Journal of Human Rights Law
Review, 2023). Moreover, ChatGPT offers the advantage of automating repetitive
tasks, freeing time for more complex and value-added activities (Davis, 2022;
Levente, 2023; Khan and Umer, 2023; Gupta et al., 2023). These benefits extend
to various sectors, including business and healthcare (Deo, 2023). Another key
advantage is its availability around the clock. This 24/7 accessibility proves
valuable in commercial, healthcare, and educational contexts (Paul et al.,
2023; Levente, 2023; Lee, 2023; Sun and Hoelscher, 2023; Cardoso, 2023). The
model’s continuous availability ensures timely assistance and support,
covering users’ diverse needs.

ChatGPT also demonstrates significant potential in education, offering various
advantages (Marr, 2023; Shidiq, 2023; Lee and Yilmaz Soylu, 2023; Sun and
Hoelscher, 2023; Mhlanga, 2023; Rivas and Zhao, 2023; Abdullah et al., 2022;
Bahrini et al., 2023; Ray, 2023; Ausat et al., 2023; Solaiman et al., 2019;
Lee and Yilmaz Soylu, 2023; Tiunova and Muñoz, 2023; Iskender, 2023; Bruff,
2023; Geertsema et al., 2023). Despite concerns about plagiarism and academic
integrity, the model’s integration can enhance teaching practices in several
ways. It can, for example, automates curriculum creation, enabling educators
to save time and streamline the process (Marr, 2023; Bahrini et al., 2023).
Moreover, it facilitates the development of innovative educational content,
fostering an engaging learning environment (Lee, 2023; Shidiq, 2023).
Additionally, the model serves as a personalized study support assistant,
providing tailored guidance and assistance to individual learners (Ray, 2023;
Rivas and Zhao, 2023; Sun and Hoelscher, 2023; Mhlanga, 2023; Abdullah et al.,
2022; Ausat et al., 2023). In this perspective, a vision emphasizes the need
for controlled integration and adherence to academic guidelines to ensure
responsible and ethical use of generative AI models in education (Sun and
Hoelscher, 2023). By establishing appropriate regulations and ethical
frameworks, the educational benefits of ChatGPT can be maximized while
addressing concerns related to plagiarism and promoting an enriching learning
experience.

In the medical field, the model has shown promise in research, data analysis,
and telemedicine applications, contributing to advancements in healthcare
(Bahrini et al., 2023). Furthermore, diverse papers recognize its potential
contribution to addressing environmental challenges. For instance, it may help
find innovative solutions to reduce water consumption in the AI industry,
highlighting its role in promoting sustainability (George et al., 2023).
Similarly, ChatGPT is also appreciated for its potential as an informative and
accountability tool within institutions (Ray, 2023; Cardoso, 2023; Biswas,
2023a). Lastly, it positively impacts journalism, where it can help with
content creation, fact-checking, and generating engaging narratives (Davis,
2022; Marr, 2023; Bahrini et al., 2023).

Conversely, the most recurrent social concern is bias. Several papers
(International Journal of Human Rights Law Review, 2023; Ray, 2023; Paul et
al., 2023; Lee, 2023; Shidiq, 2023; Biddle, 2023; C and J, 2023; Equality Now,
2023; Robson, 2023; Perrigo, 2023; Li, 2023; Biswas, 2023a; Vidhya et al.,
2023; Khan and Umer, 2023; Ausat et al., 2023; Paul et al., 2023; Solaiman et
al., 2019; Tamkin et al., 2021; Treude and Hata, 2023; Farrell et al., 2022;
Tiunova and Muñoz, 2023; Thirunavukarasu et al., 2023; Stepanechko and Kozub,
2023; Geertsema et al., 2023) discuss the risk of deepening existing biases
and how ChatGPT can include sexist and racist views due to the characteristics
of the data used during its training. In this sense, there is a special
concern about using these tools in various sectors, including finance (Khan
and Umer, 2023) and other social activities, which can replicate and deepen
structural and historical inequalities. Our analysis also reveals another
perceived negative impact, which is its potential to generate false
information and facilitate the spread of disinformation (Ray, 2023; Paul et
al., 2023; Hillemann and Zimprich, 2023; Davis, 2022; Lock, 2022; Bahrini et
al., 2023; Equality Now, 2023; Robson, 2023; Li, 2023; Biswas, 2023a; Vidhya
et al., 2023; Khan and Umer, 2023; Helberger and Diakopoulos, 2023; Air et
al., 2023; Wolf, 2023; Vallance, 2022; Rozado, 2023-03; Paul et al., 2023;
Solaiman et al., 2019; Tamkin et al., 2021; Farrell et al., 2022; Tiunova and
Muñoz, 2023; Khowaja et al., 2023). This misuse of technology directly affects
rights related to access to accurate information and freedom of expression, as
well as democratic stability. This phenomenon is particularly relevant as,
although remarkable in artificial intelligence, the advancements and
improvements in generative models have raised concerns and uncertainties
regarding their impact on democratic processes. There is a special concern
about the ease with which false information can be generated and disseminated,
especially in critical contexts such as elections, referendums, political
instability, war conflicts, or under dictatorial regimes. Furthermore, this
potential negative impact includes the digital public sphere, where spreading
hate speech on social networks (Institute for Human Rights and Business, 2023)
can lead to social fragmentation and bolster the manipulation of democratic
institutions and social control. False information can be weaponized across
various domains, from the stock market to information warfare and propaganda.

In the same vein, another relevant concern is privacy (International Journal
of Human Rights Law Review, 2023; Ray, 2023; Paul et al., 2023; Deo, 2023;
Lee, 2023; Bahrini et al., 2023; Mhlanga, 2023; C and J, 2023; Biswas, 2023a;
Robson, 2023; Perrigo, 2023; Li, 2023; Biswas, 2023b; Vidhya et al., 2023;
Khan and Umer, 2023; Helberger and Diakopoulos, 2023; Vallance, 2022; Khlaif,
2023; Paul et al., 2023; Tiunova and Muñoz, 2023; Khowaja et al., 2023;
Iskender, 2023). Different aspects contribute to this concern, including the
coverage of these models under AI Act regulations, its extensive use of user
data (particularly minors), the potential for surveillance applications, the
data vulnerability to cyber attacks targeting ChatGPT, and the privacy
implications when integrating the model into various domains such as education
and the military. Privacy is a growing concern as illustrated by the case of
the Italian state ban. In this case, the Italian authorities asked OpenAI to
“expand its privacy policy for users and made it also accessible from the
sign-up page prior to registration with the service” (Garante per la
protezione dei dati personali, 2023) in order to operate in Italy. This case
highlights the urgency for ensuring responsible and transparent use of the
technology. Another important negative impact is the risk of job loss, as
automation and AI capabilities advance (Davis, 2022; Deo, 2023; Rivas and
Zhao, 2023; Lee and Yilmaz Soylu, 2023; Biddle, 2023; Robson, 2023; Khan and
Umer, 2023; Air et al., 2023; Wolf, 2023; Curtis and ChatGPT§, 2023;
Gabbiadini et al., 2023; Iskender, 2023; Stepanechko and Kozub, 2023).

Furthermore, other papers show apprehension about over-regulation (Helberger
and Diakopoulos, 2023), highlighting the needed balance between ensuring
ethical and responsible use of AI technologies while avoiding stifling
innovation. Additionally, the model’s own cybersecurity is listed as a
negative impact (Levente, 2023; Bahrini et al., 2023; C and J, 2023; Khan and
Umer, 2023; Helberger and Diakopoulos, 2023; Air et al., 2023; Khowaja et al.,
2023). The comprehensive list of negative perceptions on ChatGPT impacts can
be found in Table A1 in online appendix (Baldassarre et al., 2023), providing
further insights into the concerns found in the literature.

RQ2. What are the emerging trends perceived in ChatGPT development?

An essential part of our review shows emerging trends (Hillemann and Zimprich,
2023; Telegraph, 2023; DiBenedetto, 2023; Deo, 2023; Equality Now, 2023;
Institute for Human Rights and Business, 2023; Kumordzie, 2023; Vallance,
2022; Perrigo, 2023; Al Ashry, 2023; Bareis, 2023; Curtis and ChatGPT§, 2023;
Rutinowski et al., 2023; Lee and Yilmaz Soylu, 2023; Rozado, 2023-03; George
et al., 2023; Levente, 2023; Sun and Hoelscher, 2023; Biswas, 2023b; Vidhya et
al., 2023; Suguri Motoki et al., 2023; Rivas and Zhao, 2023; Khlaif, 2023;
Bahrini et al., 2023; Ray, 2023; Khan and Umer, 2023; Ausat et al., 2023; Paul
et al., 2023; Tamkin et al., 2021; Solaiman et al., 2019; Lee and Yilmaz
Soylu, 2023; Geertsema et al., 2023; Tiunova and Muñoz, 2023; Khowaja et al.,
2023; Iskender, 2023; Stepanechko and Kozub, 2023; Bruff, 2023). Within this
category we found uncertainty surrounding copyright, which is a prominent
trend, raising doubts about the possibility of profiting from chat-generated
content and whether such content is subject to copyright protection. Resolving
these issues requires legislative intervention, leading to a particular and
complex debate among authorities regarding the legal implications of AI-
generated content (Hillemann and Zimprich, 2023). There is also a growing
demand for the enhancement of regulatory frameworks to safeguard original
content, considering that models like ChatGPT have the potential to negatively
impact the work of scientists, writers, researchers, and artists (Al Ashry,
2023; Ray, 2023; Tiunova and Muñoz, 2023; Khowaja et al., 2023; Iskender,
2023; Stepanechko and Kozub, 2023). Notably, analyses such as by Khowaja et
al. (Khowaja et al., 2023) raise crucial questions regarding ownership rights
over the data used to train the model and the ownership of the model itself.
As mentioned earlier, there is a dominant call for establishing ethical use
guidelines, both at a general societal level (Gabbiadini et al., 2023) and
specifically within educational and research institutions (Mhlanga, 2023;
Khlaif, 2023; Ausat et al., 2023; Solaiman et al., 2019; Tamkin et al., 2021;
Lee and Yilmaz Soylu, 2023; Tiunova and Muñoz, 2023; Gabbiadini et al., 2023;
Iskender, 2023; Stepanechko and Kozub, 2023; Bruff, 2023). These guidelines
will be pivotal in ensuring responsible adoption models such as ChatGPT.

Another emerging trend is developing transparency mechanisms (Equality Now,
2023; Lee and Yilmaz Soylu, 2023; Lee, 2023; Bahrini et al., 2023; Ray, 2023;
Khan and Umer, 2023; Tiunova and Muñoz, 2023; Khowaja et al., 2023;
Stepanechko and Kozub, 2023). Transparency is considered a vital strategy to
address resistance toward adopting these models in various contexts (Bahrini
et al., 2023) and to mitigate potential AI stigmatization. However, it is also
acknowledged that transparency poses challenges that need to be overcome
(Khowaja et al., 2023).

Another crucial issue to highlight is the accountability for potentially
harmful uses of such technology (Deo, 2023; Biswas, 2023b; Vidhya et al.,
2023; Ray, 2023; Paul et al., 2023; Khowaja et al., 2023; Stepanechko and
Kozub, 2023; Helberger and Diakopoulos, 2023), which involves both the end-
users and the companies responsible for its development (Helberger and
Diakopoulos, 2023; Deo, 2023; Institute for Human Rights and Business, 2023;
George et al., 2023; Biswas, 2023b; Vidhya et al., 2023; Ray, 2023; Paul et
al., 2023; Khowaja et al., 2023; Stepanechko and Kozub, 2023). This concern
extends to applications in sensitive domains like the military (Deo, 2023).
Furthermore, other several notable trends emerge, including the need for
timely and appropriate regulation (Gabbiadini et al., 2023; Bruff, 2023), the
existence of political bias (29, 40, 42, 62), transformations within the AI
market (Deo, 2023; Equality Now, 2023; Kumordzie, 2023; Perrigo, 2023; Tamkin
et al., 2021; Farrell et al., 2022; Geertsema et al., 2023), and the
integration of renewable technologies and environmental awareness within this
field (C and J, 2023; International Journal of Human Rights Law Review, 2023).

RQ3. Which areas of improvement can be identified in the development of such
technologies?

The findings concerning areas for improvement within the field of generative
AI present a diverse range of perspectives (Hillemann and Zimprich, 2023;
Helberger and Diakopoulos, 2023; Deo, 2023; Equality Now, 2023; Biddle, 2023;
Institute for Human Rights and Business, 2023; WeeTech Solution, 2023;
Levente, 2023; Air et al., 2023; Kumordzie, 2023; Wolf, 2023; Vallance, 2022;
Perrigo, 2023; Al Ashry, 2023; Levente, 2023; Sun and Hoelscher, 2023; Biswas,
2023a; Zhuo et al., 2023; Abdullah et al., 2022; Bahrini et al., 2023; Ray,
2023; Khan and Umer, 2023; Paul et al., 2023; Sobieszek and Price, 2022;
Solaiman et al., 2019; Tamkin et al., 2021; C and J, 2023; Tiunova and Muñoz,
2023; Khowaja et al., 2023; Gabbiadini et al., 2023; Gupta et al., 2023;
Iskender, 2023; Stepanechko and Kozub, 2023; Bruff, 2023). One prominent area
is the examination of regulations (Hillemann and Zimprich, 2023; Helberger and
Diakopoulos, 2023; Deo, 2023; Equality Now, 2023; Biddle, 2023; Institute for
Human Rights and Business, 2023; Kumordzie, 2023; Wolf, 2023; Al Ashry, 2023;
George et al., 2023; Levente, 2023; Lee and Yilmaz Soylu, 2023; Ray, 2023;
Solaiman et al., 2019; Tamkin et al., 2021; Tiunova and Muñoz, 2023;
Gabbiadini et al., 2023; Bruff, 2023), particularly regarding whether the
risk-based approach outlined in the AI Act effectively covers generative
models (Helberger and Diakopoulos, 2023; Wolf, 2023). It is suggested that
comprehensive guidelines should encompass the entire spectrum, from its
application to the AI Research and Development (R&D) (George et al., 2023).
Furthermore, there is a growing advocacy for a people-centred vision (Wolf,
2023; Kumordzie, 2023; Ray, 2023) that emphasizes the importance of human
rights and ethical considerations in designing and implementing generative AI
systems. Specifically, Solaiman et al. (Solaiman et al., 2019) examine the
need to "build frameworks for navigating trade-offs" and develop decision-
making frameworks that account for the complexities and potential trade-offs
associated with generative AI. Similarly, Li (Levente, 2023) highlights the
necessity to transform the European regulatory paradigm to effectively address
the challenges posed by LLMs.

Another area of opportunity lies in addressing technical limitations within
generative AI models (Levente, 2023; Curtis and ChatGPT§, 2023; Sun and
Hoelscher, 2023; Abdullah et al., 2022; Bahrini et al., 2023; Cao et al.,
2023; Paul et al., 2023; Sobieszek and Price, 2022; Tamkin et al., 2021;
Tiunova and Muñoz, 2023; Thirunavukarasu et al., 2023; Gupta et al., 2023;
Iskender, 2023). For instance, a significant challenge is the presence of
fictional references (Tiunova and Muñoz, 2023) within the generated text,
which hampers its reliability. Additionally, words repetition further affects
the produced text’s overall quality (Wolf, 2023). Moreover, the phenomenon of
inaccurate information, named "hallucinations" (Wolf, 2023; Tamkin et al.,
2021), and the lack of context understanding (Paul et al., 2023), are also
identified as areas requiring attention and improvement. Furthermore, the
literature highlights the need for enhanced risk mitigation mechanisms
(Equality Now, 2023; Biddle, 2023; WeeTech Solution, 2023; Levente, 2023; Air
et al., 2023; Vallance, 2022; Perrigo, 2023). This entails refining processes
for filtering potentially harmful responses (Biddle, 2023), improving the
quality and reliability of the data used to train the models (Levente, 2023),
and incorporating ethical guidelines for its development (Air et al., 2023),
among other measures (Perrigo, 2023).

Data governance is another significant area that requires attention (Deo,
2023; Ray, 2023; Khan and Umer, 2023; Tiunova and Muñoz, 2023); this crucial
task involves safeguarding sensitive information against security breaches,
unauthorized access, and information theft (Ray, 2023; Khan and Umer, 2023).
In the same vein, establishing clear guidelines regarding the scope and
limitations of information exchange with third parties is also paramount
(Tiunova and Muñoz, 2023).

Other areas of opportunity include the need for up-to-date data (Sun and
Hoelscher, 2023; Zhuo et al., 2023; Abdullah et al., 2022; Gupta et al., 2023)
to ensure the accuracy and relevance of generative AI models. However, this
requirement presents a trade-off between incorporating new data to improve
performance or addressing data governance issues first.

The literature review also highlights several other areas of opportunity for
improvement; these include promoting end-user responsibilities (Li, 2023),
advocating for timely regulation (Li, 2023; Tiunova and Muñoz, 2023), and
raising awareness of environmental impact (Tiunova and Muñoz, 2023; Geertsema
et al., 2023), among other considerations.

##  7 CONCLUSION AND FUTURE WORK

While we are still in the early stages of evaluating the social impact of
generative AI models, this systematic literature review allows us to gain
initial insights into the perceptions surrounding their emergence in
contemporary society. Our analysis has revealed notable areas of concern,
particularly privacy and the potential for bias. As generative models continue
to be adopted in diverse social contexts, addressing and mitigating issues
related to inequality, bias, discrimination, and stereotypes becomes urgent.

In light of this, it is essential to note that generative AI reflects the
social context in which it was created. As these models are trained on data
that captures various aspects of our reality, it becomes clear that addressing
their flaws and biases requires a comprehensive understanding of the broader
social context within which they operate. Rather than solely focusing on
repairing the model, it is imperative to also engage in a critical examination
of the social factors that contribute to these biases and limitations.

Some analyses argue that generative AI models, such as ChatGPT, are not
intended to address social inequalities. While this may be true, it is also
essential that these models do not inadvertently contribute to exacerbating
social issues. Acknowledging that scientific breakthroughs do not occur within
a social vacuum is critical. Therefore, we must foster a conscious,
responsible, and ethically-driven progression of generative AI. Equally
important is to emphasize that generative AI models hold immense potential and
offer substantial benefits across various fields and sectors, including
education, medicine, marketing, business, research, and science. Their impact
extends beyond innovation and significantly influences the legislative
landscape. Consequently, policymakers need to address the necessity for
appropriate regulation that not only addresses significant concerns associated
with their use, but also supports and facilitates the ethical development of
generative AI models (Clayton, 2023; Anthropic, b). Undoubtedly, AI generative
models have reshaped our way of being in the world, triggering profound
changes in our perception and engagement with it. In our relentless pursuit to
emulate human interaction, we have also confronted stereotypes, biases, and
imperfections. Rather than succumbing to discouragement, we should use them as
motivation to address them diligently and strive for continuous improvement.
More work is required to develop more robust frameworks and ethical
guidelines, not only to improve accuracy and efficiency but also to ensure
responsible deployment. As part of future work, we propose evaluating ChatGPT
regulation in the US, Europe, and Latin America. This analysis will examine
how current legal tools address generative models’ challenges in particular
locations. Additionally, to understand ChatGPT users’ professional and social
views, a survey has been designed and will be distributed among professionals
and researchers from diverse universities and research centers worldwide. With
these exercises, we want to gain a comprehensive understanding of its
adoption, regulatory challenges, user perspectives, and deepening into its
social impact.

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