Front-end AI vs. Back-end AI: new framework for securing truth in communication during the generative AI era

Introduction

With the proliferation of artificial intelligence (AI) on digital platforms, the concept of truth in communication studies has become increasingly complex. By mimicking the tone, structure, and style of authentic human writing, AI-generated content (Wu et al., 2020), such as news articles and social media posts, can create a false impression of credibility. This complicates the process of distinguishing between authentic and fabricated information. In addition, social bots (Ferrara et al., 2016) can manipulate online discourse by mimicking human behavior, such as liking, sharing, and retweeting content. During political campaigns, for instance, these bots can disseminate false information or biased opinions to influence public sentiment, making it difficult to distinguish between genuine public opinion and perspectives generated artificially. AI is now capable of generating convincingly realistic content (Caporusso, 2021; Lim and Schmälzle, 2023), including text (e.g., GPT), images (e.g., DALL-E), and speech (e.g., Whisper). Deepfakes, synthetic media in which a person's image or voice is digitally altered to resemble another person (Johnson and Diakopoulos, 2021), have further blurred the line between reality and fabrication.

The creation of virtual identities further complicates discerning the truth in communication. These identities can engage in online discourse while obscuring their artificial origins and promoting particular viewpoints (Ross et al., 2019). For instance, virtual influencers on social media platforms can generate followers and shape opinions without the audience realizing they are not interacting with a real person (Kim and Wang, 2023). The opaque nature of AI algorithms can perpetuate and amplify existing societal biases, eroding confidence in the results (Durán and Jongsma, 2021). In hiring processes, for instance, biased AI algorithms may systematically discriminate against minority applicants, reinforcing societal inequalities. The difficulty in evaluating the dependability and precision of AI results (von Eschenbach, 2021) exacerbates the problem by making it difficult to determine whether a given result is trustworthy. This article will introduce a dichotomous framework of Front-end AI and Back-end AI to address these complexities. Front-end AI focuses on enhancing the transparency and explainability of AI-generated content. In contrast, Back-end AI seeks to improve the underlying algorithms to reduce biases and increase precision. By addressing these two facets of AI, distinguishing truth amidst the rapid development of AI technologies will be made more manageable.

Front-end AI and Back-end AI

There are two main categories of AI regarding truth in communication: Front-end AI and Back-end AI. Front-end AI refers to AI technology used up-front, often as the face of a product or service. One example of Front-end AI is virtual influencers (Kim and Wang, 2023). These computer-generated images of people are intended to appear and behave like real people, complete with distinct personalities and viewpoints. However, since these virtual influencers are not actual people, their authenticity and communication veracity are questioned. For instance, a virtual influencer promoting a product may not have personal experience with the item, leading to endorsements that may be misleading or superficial. Due to the AI-generated nature of virtual influencers' content, it may not be based on genuine opinions or experiences, which makes it difficult to determine the integrity and accountability of communication. It may even lead to psychological problems such as gender stereotypes (Zakharchenko et al., 2020) and body image concerns (Kim and Kim, 2023).

On the other hand, back-end AI refers to AI technology used in the background to influence communication without direct user interaction. An example of a Back-end AI is Chat-GPT, an Open AI-trained language model designed to simulate human conversation (Reynolds and McDonell, 2021). Unlike traditional chatbots (Kim, 2021), followers may be misled when individuals use Chat GPT to express opinions on social media without disclosing the use of AI-generated language. Moreover, although Chat GPT's responses may appear genuine, they may not always be entirely truthful or accurate, particularly if the input it receives is biased or insufficient. This situation can create a false sense of authenticity and authority, which can be exploited to disseminate incorrect information or improperly influence public opinion (Gerlich et al., 2023).

Although their applications are distinct, both Front-end AI and Back-end AI can potentially complicate the notion of truth in communication. As AI technology advances, consumers and businesses must be aware of AI-generated communication's potential biases and limitations to ensure that communication remains as honest as possible. By examining the implications of Front-end AI and Back-end AI on disinformation and privacy, this article aims to shed light on how these two types of AI shape the truth in communication and the challenges they pose in the context of these crucial issues.

Disinformation and truth

Disinformation, a form of communication that intentionally misleads or presents falsehoods, manifests in various ways, such as fabricated news stories, propaganda, and unfounded rumors (Benkler et al., 2018). Disinformation campaigns aim to manipulate public opinion or incite confusion and mistrust. They often proliferate on social media platforms where users rapidly share content without verifying its accuracy (Di Domenico et al., 2021). Consequently, disinformation has spread false information that is difficult to counter and carries significant repercussions for society. Upholding the concept of truth is crucial in combating disinformation. The belief in an objective reality, independent of individual opinions or perspectives, necessitates meticulously examining facts and establishing effective communication strategies (Newman and Gopalkrishnan, 2023).

Front-end AI, including virtual influencers and social bots, contributes to disinformation by raising concerns about the authenticity and truthfulness of the content they generate. As these entities lack human qualities, their content may be perceived as misleading or manipulative (Ferrara et al., 2016; Dwivedi et al., 2022). For instance, during the 2016 US presidential election, social bots disseminated politically-biased misinformation, creating confusion among voters and skewing public discourse (Shao et al., 2018; Rozado, 2023). Audiences often struggle to distinguish virtual influencers' content from real humans, making it unclear whether the opinions expressed are based on genuine experiences or merely marketing tactics. This ambiguity becomes particularly problematic when a social bot amasses a large following, as its views are amplified and widely circulated (Ferrara, 2017), perpetuating disinformation and confusion among consumers.

Back-end AI can further exacerbate disinformation when AI algorithms are trained using incomplete or biased data. The validity of predictions made by these algorithms hinges on the comprehensiveness of the data used for training. For example, a widely-used AI recruitment tool exhibited gender bias (Gross, 2023) due to training on male-dominated resume data, leading to the inadvertent promotion of gender inequality (Dastin, 2018). However, when people encounter such predictions, they may not know if AI generated them, resulting in disinformation and confusion. As disinformation continues to pose a significant challenge to society, countering it requires increased scrutiny of AI-generated content and reinforcing the concept of truth through thorough fact-checking and well-crafted communication strategies.

Privacy and truth

Privacy, the right to control access to one's personal information, has become increasingly complex in the digital age due to the pervasive collection and use of personal data by both private companies and governments (Schwartz, 2003). This complexity arises from various practices, such as targeted advertising, content personalization, and behavioral monitoring, which rely on extensive data collection. For instance, third-party tracking codes on websites exemplify how privacy can be compromised in the digital landscape. These codes are frequently embedded without users' knowledge or consent, enabling companies to monitor individuals' online activities across multiple platforms, including mobile devices and applications (Mayer and Mitchell, 2012). This invasive tracking can lead to significant risks to personal safety, as demonstrated by cases where individuals' location or medical information has been used against them for insurance purposes (Barocas and Selbst, 2016). Nonetheless, there are alternative digital advertising methods that do not require collecting personal data. Search ads, for example, provide relevant content based on a user's immediate query rather than personal information (Yoo, 2014). This demonstrates that it is possible to maintain privacy while still engaging in effective advertising.

Furthermore, the increasing use of AI technologies on the front-end and back-end raises additional privacy concerns. Open dialogue chatbots, which engage users in free-form conversations, can inadvertently collect and utilize a wealth of personal data (Hasal et al., 2021). This is particularly concerning since these chatbots are often trained on previous conversations, potentially exposing sensitive information to theft or misuse, even when no directly identifiable information is present. Back-end AI technologies, such as facial recognition and other surveillance systems, also present significant privacy concerns. These technologies can surreptitiously collect and use personal data without the knowledge or consent of the individuals involved (Almeida et al., 2022). This compromises individual privacy and raises critical ethical and legal questions regarding the appropriate use of such technologies (Gates, 2011). Consequently, there is an urgent need to develop and implement robust safeguards that protect privacy while allowing for the responsible use of AI and other data-driven technologies in the digital realm (Burr and Leslie, 2022).

Different approach toward Front-end AI and Back-end AI

The problems of truth in communication with Front-end AI and Back-end AI require different solutions. While concerns regarding Front-end AI are focused on the message receivers, the solutions of Back-end AI are on the message senders. While Front-end AI may require verification and ethical guidelines, Back-end AI may require algorithmic transparency, bias detection and mitigation, and human oversight. By approaching the issues of disinformation and privacy with this framework, we can ensure that their use of AI is responsible, ethical, and authentic.

One approach to addressing the authenticity of Front-end AI-generated content is through verification. This may involve creating a system for verifying virtual influencers' or social bots' authenticity. For example, a digital watermark or certification system could be implemented to confirm the origin of the content (Westerlund, 2019). This can help build trust and ensure that individuals can rely on the information presented to them. Establishing ethical guidelines for using Front-end AI can also help prevent the misuse of these technologies. These guidelines may address issues such as privacy, transparency, and bias. For instance, rules could be established to prohibit using AI-generated deepfakes for malicious purposes (Wojewidka, 2020) or the unauthorized use of personal data in AI-generated content. Consumers' education about the comprehensive implementation of Front-end AI can also help to prevent misunderstandings and promote authentic communication. Individuals can better understand AI-generated content's limitations and potential biases by providing information about how virtual influencers or open-dialogue chatbots are created. For example, interactive educational materials or public awareness campaigns can help inform users about how AI algorithms function (Shin et al., 2022) and the potential pitfalls of relying solely on AI-generated content.

For addressing the truthfulness of Back-end AI-generated content, involving algorithmic transparency in the inner workings of AI algorithms more transparent to users is essential. By providing users with information about how AI systems make decisions, they can better understand the accuracy and reliability of the results. For instance, companies could publish whitepapers detailing the methodologies and data sources used in their AI models or provide user-friendly explanations of the AI's decision-making process (Goodman and Flaxman, 2017). To prevent Back-end AI from perpetuating biases, companies should also prioritize bias detection and mitigation in developing these technologies. This may involve using diverse and representative data sets to train AI systems and implement bias detection algorithms to identify and correct any present biases. For example, companies could collaborate with external organizations specializing in bias detection and fairness in AI (Bellamy et al., 2019) to ensure that their systems do not inadvertently discriminate against specific demographic groups.

Implementing human oversight through human-in-the-loop using Back-end AI can help ensure that interactions remain truthful and authentic. This may involve having human moderators review and approve the results generated by AI systems or having customer service representatives available to intervene when needed (Gillespie, 2020). For example, in a customer support scenario, AI-generated responses could be flagged for review by a human agent before being sent to the customer, ensuring that the information provided is accurate and contextually relevant. Combining human judgment with AI capabilities can achieve a more reliable and authentic communication process.

Conclusion

In conclusion, the complexities surrounding truth in communication caused by the rapid development of AI technologies, such as Front-end AI and Back-end AI, necessitate innovative solutions and heightened vigilance. The process of distinguishing truth can be made more manageable by enhancing the transparency, explainability, and ethical use of AI-generated content in Front-end AI and improving the underlying algorithms to reduce biases and increase precision in Back-end AI. To maintain authentic and ethical communication in the digital age, addressing the obstacles posed by disinformation and privacy concerns is essential. As AI develops, consumers and businesses must be aware of AI-generated communication's possible biases, limitations, and ethical implications. This can be accomplished through verification, ethical guidelines, algorithmic transparency, bias detection and mitigation, and human oversight. By adopting a comprehensive and proactive approach to addressing the challenges posed by AI in communication, society will be able to navigate the blurred lines between reality and fabrication and foster a digital landscape in which truth and authenticity remain paramount.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

DK: study conception, manuscript draft preparation, and final manuscript writing. JK: manuscript revision.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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