Is Transparency Integral in AI-Driven Marketing for Building and Maintaining Consumer Trust?

How Predictive Analytics and Personalisation, and Personal Data Usage, Impacts Gen Z Consumers

This article explores how transparency in AI-driven marketing affects consumer trust, focusing on personalisation and predictive analytics amongst Gen Z.

Transparency means marketeers inform consumers how models are created, what data is used to train them, and how it makes decisions (Jonker et al., 2024).

Artificial Intelligence (AI) usage within digital marketing has seen a significant increase in recent years. Gen Z adults between 18 and 24 are the largest audience for AI-powered apps, showing how important it is to consider them in digital marketing decisions (cycles & Text, 2024).

AI tools are commonly used within digital marketing; 94% of UK marketeers report using AI, but only 61% claim to adhere to environmental and privacy ethics (Statista, 2023)

The AI Driven Paradigm Shift

AI has caused a “paradigm shift” within digital marketing, reflecting the shift from predominantly human lead decision making to AI often being implemented, particularly in managing large scale data.

This shift benefits marketeers by increasing personalisation, enabling real time monitoring, and allowinglarger scale data analysis than ever before. However, not all the patterns identified are meaningful (Iyer & Bright, 2024).

AI Methods and Their Ethical Implications

Sentiment Analysis

Sentiment analysis is commonly used to understand what emotions consumers feel towards marketing materials (Buhas et al., 2024). It also helps identify which creators resonate with a brand’s target audience (TA) to optimise resource and budget allocations in influencer marketing (Walz, 2023).

However, language such as slang and sarcasm is often misinterpreted by AI, which produces inaccurate results that negatively impact marketing decisions (Dilmegani, 2024). This language prevails on social media, which largely impacts Gen Z’s communication, making this a significant issue (Rett, 2023).

Predictive Analytics

Predictive analytics are mostly generated through AI driven machine learning methods. Machine learning identifies patterns by collecting data and using it to predict future outcomes (Basu et al., 2024). It generates insights from data sets that are too big to be processed by humans. However, some consumer segments may be unfairly represented, leading to inaccurate predictions that will negatively influence campaign decisions (Volkmar et al., 2022a).

Data Privacy and Ethics

Data privacy is a further ethical concern. AI models are made to evolve through machine learning, as opposed to being coded in a traditional way, therefore, if sensitive information is exposed their creators may be unable to trace or debug the source of the problem (Gomstyn & Jonker, 2024).

As a result, risks surrounding data protection and the exploitation of cyber systems are more likely (Gupta, 2026). Statistics from (Stanford University, 2025) support this, stating that the number of AI privacy incidents grew by 54.4% in one year.

Distribution of the main ethical challenges within AI (Kumar et al., 2025)

Critical Evaluation: Difficulties with Personalisation, Biases, and Power Asymmetries

AI use within digital marketing provides many advantages. Research shows that 78% of marketing teams use AI content for SEO, A/B testing, and optimisation, giving them an advantage over competitors (Shum, 2025).

AI also enhances personalisation by analysing real time behaviour rather than relying on stereotypes and generalisations (Kotyrlo et al., 2024). This boosts engagement by recommending relevant content, increasing marketing influence (Verma et al., 2025).

However, personalisation should be integrated sensitively because limited transparency can make consumers feel exploited, resulting in undermined long-term trust in exchange for short-term results (Jabr, 2023). Additionally, insights may be biased or unrepresentative of certain demographics when low quality data is used, which poses a risk of bias and unfair marketing (Akter et al., 2022).

Furthermore, personalisation is most effective when campaigns are monitored and adapted in real time(Nair et al., 2025). Social media algorithms have become increasingly AI driven to support this (Al et al., 2025). TikTok exemplifies this as its algorithm builds users an individualised “For You page” based on their interactions (https://www.facebook.com/keith.kakadia, 2025). Marketeers benefit from this as consumer targeting, influencing consumer choice and retention are improved (Al et al., 2025b).

Power Asymmetry

Many Gen Z consumers underestimate how much of their personal data is used, questioning whether informed consent can truly be provided (Wyman et al., 2023) This also highlights a power asymmetry between the consumer and marketeer (Grewal et al., 2021). When consumers become aware of this, they may view personalisation as manipulation and question how this influences their decisions (Abimbola, 2023).

Predictive Analysis

Predictive analysis supports budget allocation, real time decisions and competitive differentiation by predicting trends, churn and campaign responses (Ali et al., 2023).

This enhances marketing efficiency; however, limited expertise can reduce reliability (Rana, 2025).

Algorithmic Bias

Algorithmic bias is another concern. These biases can stem from political views, gender, and raceimpacting data (Karami et al., 2024). An example of this is Amazon’s AI recruiting tool displaying gender bias against women as the model was trained on data that displayed inequalities, highlighting ethical implications and the risk discrimination related legal issues (Nieem Sekiri, 2025).

This demonstrates how biases affect consumer segments (Kumar et al., 2025)

Hyper-personalisation

Hyper-personalisation poses further ethical debates due to the extent of data marketeers possess. For instance, facial recognition software and voice analysis can be seen as invasive and negatively impact long-term trust (Raian et al., 2025). The power asymmetry this creates can also manipulate autonomy by nudging consumers towards decisions they would not have otherwise made.

Starbucks’ “deep brew” AI engine exemplifies this. It analyses customer data to provide personalised recommendations such as tailored discounts to influence purchase decisions (Marketer In The Loop, 2024).

This particularly appeals to Gen Z with research showing they greatly value personalised rewards, often using personal data as an exchange (Rawat, 2024). As a collective, normalising this constant surveillancerisks impacting societal norms around privacy altogether (Karami et al., 2024).

Transparency to the Rescue: XAI and Regulation

Black Box AI

AI systems that operate without disclosing their decision-making processes are referred to as black box algorithms, they pose security risks and mislead accuracy. Chat GPT is an example, (Christiano, 2024) which especially concerns Gen Z, who use it often and for many things, even decision making and life advice (Caddy, 2025).

There are legal regulations such as the European union AI act to minimise risks, however, the non-transparent nature of black box AI makes regulation difficult to enforce (Kosinski, 2024). Studies also found that the lack of interpretability results in negative consumer responses to AI system faults (Chen, 2024).

This shows that understanding AI and personal data usage matters to consumers (Statista, 2024).

Explainable AI (XAI)

Governance frameworks and transparent AI approaches, such as white box AI, avoid this (Kosinski, 2024). Therefore, to help consumers understand how AI models process and interpret their data, organisations should use explainable AI techniques, known as XAI.

Explainability increases trust by making algorithmic processes transparent and understandable. This reduces uncertainty by explaining AI in a consumer-focused way (Saranya & Subhashini , 2023).

GDPR and Responsible AI Usage

General Data Protection Regulation (GDPR) is a mandatory legal framework. GDPR protects consumer data by regulating transparency, data handling, and privacy protection (Irfan et al., 2025). For instance, a principle of GDPR is data minimisation, which requires marketeers to collect only the data required to fulfil their research, limiting how invasive it will be towards consumers (Hordern & Schwarz, 2025).

GDPR also requires human oversight to protect consumer rights (Lazcoz & Hert, 2023). Additionally, consent or legitimate interest is essential, as it needs to be explicit and informed whilst not overriding individual rights (ICO, 2023).

Importance of Transparency

Transparency helps recognise bias, such as misrepresentation or discriminatory targeting in unrepresentative or incomplete datasets, which maintains ethical practice (Hanna et al., 2024). Alongside transparency, human oversight, data audits, and diverse data sampling are needed to keep consumer trust(Akter et al., 2023).

Gen Z, as alongside Gen X, they are the least trusting generation towards AI (Statista, 2024b)

Privacy First Personalisation

Research shows that Gen Z consumers prefer privacy first personalisation, they value personalised marketing but worry about how their data is handled. Allowing them to limit data usage and control personalisation can help with these concerns, benefitting both the brand and the consumers (McKee et al., 2023). Concerns can also arise from changes occurring in AI marketing that are yet to be regulated and understood. To reassure consumers, ethical training and assessment frameworks are needed (Hari et al., 2024).

Summing It All Up

In conclusion, transparency is the key to gaining consumer trust, particularly amongst Gen Z, as they are the most sceptical generation towards AI. Trust is also gained through consumers having the ability to control their personal data, and through the brand maintaining ethical practice by adhering to regulatory frameworks such as GDPR.

Ethical AI use also requires explainable algorithms, human oversight, and respectful personalisation to avoid bias and power asymmetry. Adhering to these principles makes consumers feel comfortable with tools such as predictive analytics and hyper personalisation, leading to the growth of trust between the consumer and a brand.

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