Artificial intelligence and mass personalization of communication content—An ethical and literacy perspective

Hermann, Erik

doi:10.1177/14614448211022702

Cited by 65 publications

(30 citation statements)

References 129 publications

(276 reference statements)

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“…In addition, the loss of privacy is an inevitable downside of an agentic technology that autonomously provides individually tailored content and facilitates experiences (e.g., Agguire et al, 2016;Sundar & Marathe, 2010;Hermann, 2022). This is because personalization algorithms cannot function without accessing and controlling user data.…”

Section: Ambivalent Feelings Towards Machine Agencymentioning

confidence: 99%

AI agency vs. human agency: understanding human–AI interactions on TikTok and their implications for user engagement

Kang

Lou

2022

Journal of Computer-Mediated Communication

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Artificial intelligence (AI) technology has vastly reshaped user experiences on social media. AI-powered social media use and its outcomes largely depend on how users collaborate with AI that exercises agency. Through in-depth interviews with TikTok users, this study investigates how users collaborate with AI when using AI-powered social media and how such dynamics shape user engagement. We found that TikTok users are receptive to personalized experiences enabled by machine agency. However, by influencing each other, user agency and machine agency also led to user–AI synergy. Users deliberately influence content curation algorithms to make them cater more precisely to their needs; AI also facilitates users’ content creation and networking. Such AI–user collaboration on TikTok significantly influences medium engagement and social-interactive engagement. These findings advance our understanding of the dynamics between human agency and machine agency and, thus, how AI transforms user experiences on social media.

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Section: Ambivalent Feelings Towards Machine Agencymentioning

confidence: 99%

AI agency vs. human agency: understanding human–AI interactions on TikTok and their implications for user engagement

Kang

Lou

2022

Journal of Computer-Mediated Communication

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“…More specifically, research should investigate how consumer experiences benefit from AIP to build trust, facilitate the purchase and develop loyalty and advocacy. In contrast, it is equally, if not more, important to understand the dark side of AIP (Hermann, 2022; Thomaz et al , 2020). For example, future research needs to clarify the potential hard feelings caused by AIP and investigate the impact of AIP on consumers' autonomy, control and self-regulation (Andre et al , 2018).…”

Section: Future Research Agendamentioning

confidence: 99%

Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective

Gao

Liu

2022

JRIM

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PurposeArtificial intelligence (AI) technology has revolutionized customers' interactive marketing experience. Although there have been a substantial number of studies exploring the application of AI in interactive marketing, personalization as an important concept remains underexplored in AI marketing research and practices. This study aims to introduce the concept of AI-enabled personalization (AIP), understand the applications of AIP throughout the customer journey and draw up a future research agenda for AIP.Design/methodology/approachDrawing upon Lemon and Verhoef's customer journey, the authors explore relevant literature and industry observations on AIP applications in interactive marketing. The authors identify the dilemmas of AIP practices in different stages of customer journeys and make important managerial recommendations in response to such dilemmas.FindingsAIP manifests itself as personalized profiling, navigation, nudges and retention in the five stages of the customer journey. In response to the dilemmas throughout the customer journey, the authors developed a series of managerial recommendations. The paper is concluded by highlighting the future research directions of AIP, from the perspectives of conceptualization, contextualization, application, implication and consumer interactions.Research limitations/implicationsNew conceptual ideas are presented in respect of how to harness AIP in the interactive marketing field. This study highlights the tensions in personalization research in the digital age and sets future research agenda.Practical implicationsThis paper reveals the dilemmas in the practices of personalization marketing and proposes managerial implications to address such dilemmas from both the managerial and technological perspectives.Originality/valueThis is one of the first research papers dedicated to the application of AI in interactive marketing through the lenses of personalization. This paper pushes the boundaries of AI research in the marketing field. Drawing upon AIP research and managerial issues, the authors specify the AI–customer interactions along the touch points in the customer journey in order to inform and inspire future AIP research and practices.

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“…The current public discussion of RecSys's (often just referred to as the algorithm or AI personalization), focuses, among other topics, on their invasive behavior concerning personal data collection [20,29,42,43]. This might create a negative relationship between user and RecSys potentially resulting in anything from user discontent to "algorithmic hate" [73].…”

Section: Federated Recommender Systemsmentioning

confidence: 99%

A Privacy Preserving System for Movie Recommendations using Federated Learning

Neumann¹,

Lutz²,

Müller³

et al. 2023

Preprint

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Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are employed by many online businesses to drive engagement and sales. Besides other criticisms, like creating filter bubbles within social networks, recommender systems are often reproved for collecting considerable amounts of personal data. However, to personalize recommendations, personal information is fundamentally required. A recent distributed learning scheme called federated learning has made it possible to learn from personal user data without its central collection. Accordingly, we present a complete recommender system for movie recommendations, which provides privacy and thus trustworthiness on two levels: First, it is trained using federated learning and thus is, by its very nature, privacy-preserving, while still enabling individual users to benefit from global insights. And second, a novel federated learning scheme, FedQ, is employed, which not only addresses the problem of non-i.i.d. and small local datasets, but also prevents input data reconstruction attacks by aggregating client models early. To reduce the communication overhead, compression is applied, which significantly reduces the exchanged neural network updates to a fraction of their original data. We conjecture that it may also improve data privacy through its lossy quantization stage.

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Artificial intelligence and mass personalization of communication content—An ethical and literacy perspective

Cited by 65 publications

References 129 publications

AI agency vs. human agency: understanding human–AI interactions on TikTok and their implications for user engagement

AI agency vs. human agency: understanding human–AI interactions on TikTok and their implications for user engagement

Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective

A Privacy Preserving System for Movie Recommendations using Federated Learning

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