Digital Marketing and User Rights: How Do Algorithms Influence Our Decisions?

March 18, 2025 / ,

Introduction

Digital Marketing is a prominent phenomenon in everyone’s daily experience with the Internet. It is rare to spend even a minute browsing the Internet without encountering one or more ads. When you read an article on a news site, ads appear at the top of the page, on its sides, within the text, or suddenly pop up in windows floating above the page.

When you watch a video on YouTube, ads appear every few minutes, interrupting your viewing. However, the most frequent exposure to advertisements often occurs while browsing social media platforms. They are embedded within the posts you scroll through on Facebook and interrupt your viewing of short TikTok videos.

It is no longer a secret to anyone that the ads you encounter are often based on your characteristics, preferences, and interests. They also rely more clearly on your behavior and activity on the websites you regularly interact with. Ads you come across are always related to the country you live in and typically also to your age group. Additionally, if you search on Google or Facebook for a product or service, it’s almost certain that you’ll come across many ads related to that product or service.

Digital marketing is the most influential factor in shaping how the Internet operates today. Undoubtedly, the largest portion of funding and profits for tech companies comes from online marketing activities. Digital marketing is one of the largest and fastest-growing industries today, and it is expected to become even more important in the future.

At the heart of all digital marketing activities and processes lie algorithms. These algorithms perform all their functions without anyone noticing, and users do not interact with them directly. Most algorithms rely on big data technologies and artificial intelligence (AI). In recent years, these algorithms have become more complex, and their capabilities have increased.

Algorithms collect an enormous amount of data about internet users and analyze it to determine the best ways to attract them to the products and services that advertisers want to market. Consequently, algorithms manage the targeting of users with ads that have a higher likelihood of getting a response. Often, algorithms also work autonomously to condition, influence, and perhaps even manipulate users, making them more likely to engage with ads.

Most internet users don’t mind the presence of some ads as long as it’s a condition for enjoying the services of these websites or apps for free. Many may also see targeted ads as helpful in finding products and services that suit them, and sometimes they even help save money when they offer attractive deals and discounts.

However, there is a gray area where algorithms operate without transparency. In this area, algorithms carry out many processes that threaten users’ rights. Foremost among these rights are freedom of choice, protection from manipulation and commercial fraud, as well as the right to privacy and the protection of personal data. Therefore, the relationship between digital advertising, online marketing algorithms, and consumers’ rights, as well as the digital rights of users, is an issue of great importance today.

This paper discusses the impact of digital advertising and online marketing algorithms on the digital rights of internet users, with a focus on freedom of choice, consumer rights, and the right to privacy. The paper presents how algorithms work in collecting and analyzing information for online marketing purposes and how they are exploited to influence users.

The paper also presents approaches that address the relationship between digital advertising, online marketing, and users’ rights. These approaches include legislative measures and attempts to strike a balance between technological advancement and benefiting from it while also protecting users’ rights.

How Do Algorithms Collect Data?

Behavioral Data

Behavioral data is like the footprints a person leaves behind as they wander through a place. When we browse the internet, particularly when using social media platforms, we leave traces of our actions and interactions. This represents the behavioral data that algorithms collect to analyze. Algorithms capture every virtual button click, every page scroll, and every touch on a mobile screen, and gather this data to tell a story that describes each person’s behavior.

In more detail, the behavioral data collected by algorithms includes:

  • Browsing history.
  • Search queries.
  • The time spent on each page visited.
  • How far a person scrolls through pages.
  • Mouse movements (cursor tracking).
  • Clicking or tapping to follow links.
  • Information about the device being used.
  • Location data.
  • Activities on social media platforms.
  • History of online purchases.

Algorithms obtain this data through various means. This includes information-gathering software, such as cookies, which websites implant on the user’s computer during browsing, and tracking software on different sites that records the user’s activities, known as trackers.

Demographic Data

Demographic data is one of the oldest types of data that the marketing industry has been interested in collecting, both before the digital age and today. This data pertains to the general characteristics of population groups and classifies individuals within these groups. It includes attributes such as age group, gender (male or female), ethnicity, country of birth, place of residence, average income, occupation, education level, and homeownership. The use of demographic data relies on a set of generalizations that reflect the common consumer behavior within a specific demographic group.

Demographic data may be the easiest to collect, as some of it is either required or voluntarily provided by the user when creating a personal account on social media platforms. However, there are other types of data that require analyzing additional personal information collected by various tracking software.

Psychographic Data

The term psychographic data refers to a quantitative approach to describing and classifying consumers based on their psychological characteristics. This includes behavioral and personality preferences, beliefs, opinions, interests, behaviors, values, and to some extent, habits and lifestyle. A person’s psychographic profile can be accurately inferred from the data they leave behind while browsing websites and apps, their interactions on social media platforms, or when they purchase a product or service.

It is important to note that psychographic classification differs from the behavioral classification mentioned earlier, although they complement each other. Psychographic classification focuses on the consumer as a person and seeks to explore his inner world, values, and priorities. In contrast, behavioral data pertains to the observable actions of a person without attempting to delve deeper to form a complete picture of his personality.

Device and Technical Data

Algorithms collect a large amount of data about the devices individuals use. This data is easy to gather particularly from smartphones and tablets. This data is divided into:

  • Device and Software Details: The type of device, the operating system and its version, the browser and its version, the apps installed on the device, and any extensions added to them.
  • Network and Connection Data: The IP address, the mobile service provider, the internet service provider, and the use of Virtual Private Networks (VPN).
  • Location Data: GPS coordinates, approximate location via IP address, nearby Wi-Fi networks, and mobile phone towers.
  • Interaction and Sensor Data: Screen resolution and display settings, the use of touchscreens or other input methods, and access data for the camera and microphone.
  • Device-Specific Data: The device’s unique identification code, battery level and charging status, device time zone, and system clock settings.

Third-Party Data

The work of online marketing algorithms for social media platforms or others is not limited to collecting data from the user’s interaction with the platform itself. The scope of work of these platforms extends to collecting data from the user’s browsing and activities on other websites.

This helps complete the construction of behavioral and psychological profiles of users by recording their interactions and activities outside the platform. This data is made available through data brokers, entities that specialize in collecting and selling data to third parties. Additionally, third-party data can be gathered through partnerships established between various companies, where information collected by each site is exchanged with others. This data is also accessible through tracking and data collection software embedded by websites in users’ internet browsers.

How Do Algorithms Analyze Data?

Algorithms use big data analytics and processing mechanisms and apply them to the vast amounts of data collected through various methods. The goal of these analyses is to produce information that can be used to determine ways to target users with advertising materials while ensuring the highest probability of a positive response from them. The following points discuss different aspects of the data analysis processes carried out by digital marketing algorithms.

User Segmentation

User segmentation into different categories is one of the essential mechanisms for digital advertising and marketing processes. Segmentation based on various conditions and criteria allows for the identification of groups and categories of individuals who share specific characteristics that can influence their preferences, inclinations, and, consequently, their consumer behaviors.

Demographic classifications are the oldest and easiest model to use, and they remain highly effective in many cases to this day. For example, many products and services can be successfully marketed by targeting women or men of a specific age group within a certain income range. There is much that unites individuals within these categories and influences their consumer behaviors.

However, the segmentation processes carried out by online marketing algorithms today go beyond demographic classification. By relying on detailed individual profiles, these algorithms can segment users based on deeper behavioral and psychological characteristics, making the targeting and influencing of their consumer behaviors more precise and effective.

Highly accurate classification information is used in various practices, such as personalization and the selection of psychological incentives, which aim to maximize user interaction on social media platforms. This is essential for enhancing the cycle of collecting and analyzing information, then using it to create shopping opportunities and gather more information in successive and cumulative cycles.

Behavioral Prediction Models

In its simplest definition, behavioral prediction models answer the following questions: How will a specific person behave when encountering a particular ad for a product? Will the ad catch his attention? Will he stop to read or watch it if it’s a video? Will he engage with it by clicking a link to get more information? And finally, is there a chance he will make a purchase immediately or perhaps after some time? To provide as accurate answers as possible to these questions, analyzing the various data collected is required to build a detailed behavioral profile of the person, allowing for the prediction of how he will act when exposed to specific external stimuli.

The above describes what passive behavioral models can do, meaning they only attempt to predict individuals’ free responses when encountering an external stimulus, in this case, an advertisement. However, building behavioral prediction models based on a vast amount of detailed data allows these models to be positive.

This means the ability of the models to answer a different question: How can a specific person be encouraged to pay attention to a particular ad, interact with it, and have a high likelihood of making a purchase decision for the advertised product? In this case, the behavior prediction model is used to guide the user’s behavior.

But there is more. The next step is to condition the user to be generally more inclined to pay attention to a specific category of advertisements and more likely to interact with them. This increases the likelihood that the user will purchase the products being offered. This is what we call behavioral modification.

The Continuous Learning Cycle

Digital marketing algorithms increasingly rely on advanced artificial intelligence technologies that use machine learning and deep learning. This means the algorithms can modify themselves to change how they operate based on the results of their actions. This process is an advanced form of data analysis. Instead of data analysis being a one-way process that starts with specific data and produces specific information, this process follows a circular path. It begins with the available data and generates specific information used in targeting and actual marketing processes. Based on the success or failure of these latter processes, various elements of the cycle are adjusted, starting with modifying data collection processes and moving through data processing and analysis.

One of the results of the continuous learning cycle for online marketing algorithms is that they constantly improve their detailed profiles and behavioral prediction models built about users. This means that with each new cycle, they gain deeper and more accurate knowledge of the user’s personal details and preferences. What is more concerning is that they increase their ability to guide and modify the user’s behavior based on the outcomes of their previous attempts to achieve this.

How Do Algorithms Exploit Information to Influence Users?

Marketing algorithms utilize the vast amount of data previously collected, as well as the information generated by analysis mechanisms, to perform two primary functions: the first is maximizing user engagement and interaction with the advertising content presented to them. The second is maximizing the profits generated from ads, which typically means ensuring the highest possible likelihood that the user will purchase the advertised products and services.

Mechanisms for Maximizing User Engagement

  • Personalization: Personalization is the use of various mechanisms to tailor a user’s experience on a website or app to better align with their preferences and inclinations. This makes the experience more satisfying for the user, which helps to engage them for longer periods and at a deeper level, increasing their interaction rate. Personalization mechanisms can also go beyond this, being used to psychologically condition users, guiding and modifying their behavior.
  • Behavior Prediction: Predicting users’ behavior is a key tool for maximizing their engagement with a website or an app. Through behavior prediction, it becomes possible to determine which type of content will most effectively attract users and prompt them to engage more.
  • Emotional Incentives: Emotional incentives play on the users’ subconscious responses. In other words, they are stimuli that guide users’ behaviors by triggering emotional reactions, rather than being directly related to their preferences or inclinations. These incentives often rely on instinctive sensory triggers.
  • Continuous Feedback Loops: Feedback loops monitor users’ responses to the various mechanisms mentioned above, providing indicators of their success and identifying areas of weakness. This allows for adjustments to be made to improve their efficiency, effectiveness, and chances of success.

Profit Maximization Mechanisms

  • Targeting: Targeting can be considered the core of any online marketing process, with all its mechanisms working toward creating the conditions for its successful achievement. Targeting involves directing the right advertising content to the right person at the right time and under the right circumstances. All the previous mechanisms come together to determine the targeting criteria that ensure the user will complete the purchase of the advertised product or service. The higher the success rate of targeting on the platform, the more it can sell services to more advertisers in exchange for higher revenues.
  • Real-Time Bidding (RTB): Real-time bidding is a method of selling and buying advertising space on a website based on the opportunity for the user to see the ad, through a real-time automated auction. Advertisers bid for the chance to display their ad in real time, and once a bidder wins, their ad is shown in the available ad space immediately. The algorithms determine the winning bid based on the ad that has the highest potential to generate profit, predicting this based on what is most relevant to the user and the bid value. Platforms like Facebook and Google Ads use real-time bidding to maximize their profits when serving content directly targeted at users.
  • Cross-Selling Opportunities: Websites invest in every opportunity to successfully attract users to an advertisement for a product or service. This is achieved by presenting related purchasing opportunities and offers that the users might be interested in as an extension of their interest in the initial product or service. The algorithms work to increase the likelihood of users purchasing additional items by compiling a list of products and services they are likely to want to buy, based on the available data and information about the products and the users.

Examples of Algorithm Systems

The algorithms and advertising policies differ between platforms. Each platform has distinctive characteristics that align with the behaviors of its users and the available data environment. The following points describe the features of the advertising systems for Google, Facebook, Twitter, and TikTok. It is noticeable that both Facebook and TikTok’s advertising systems are the most intrusive and exploitative, due to their intensive data collection practices and psychological impact.

TikTok focuses on displaying content that encourages user addiction to it. Meanwhile, Facebook leverages detailed user profiles for precise targeting. On the other hand, while Google ads are intrusive in terms of tracking, they are less exploitative because they align with the user’s apparent intentions. Finally, X’s (formerly Twitter) advertising system occupies a middle ground, as its data collection practices are relatively moderate, as is the system’s impact on user behavior.

Google Ads

Google ads primarily operate on a keyword-based system, targeting users when they search for specific terms. Advertisers bid on keywords, and the ads are displayed based on their relevance to the searched keywords and the advertisers’ bid amounts.

Google ads leverage search queries, the user’s location, device information, and browsing history to display relevant ads. The strength of Google ads lies in their ability to reach users with the highest purchase intent, making them effective at generating direct responses. However, competitive bidding among advertisers can drive up costs, especially for the most popular keywords.

Google ads heavily rely on the following:

  • Search queries.
  • Browsing history through Google Chrome, the most widely used browser.
  • User activity on YouTube.
  • Location data.
  • Tracking software using Google Analytics.

While Google tracks a wide range of behavioral data, most of it is related to the user’s intentions. Its ads are highly accurate, as they are displayed based on the user’s search queries and browsing history. However, its targeting is less personalized compared to social media platforms.

Google Ads are moderately intrusive, as the data they collect relies on active user interaction, such as conducting searches, making them appear less exploitative. Thus, the level of exploitation in Google Ads ranges from low to moderate, given their alignment with users’ clear intentions, such as searching for products or services, which reduces their sense of being exploited.

Facebook

Facebook’s algorithms target users based on detailed demographic information, as well as their interests, behaviors, and connections with other users. Ads are embedded in the page through which the user repeatedly views content. Facebook ads utilize a vast and extensive amount of user data, including interactions such as liking posts, sharing them, and data provided by third parties.

Facebook employs this data to create detailed user profiles for precise targeting. This allows advertisers to reach specific audiences, targeting particular demographics, interests, and behaviors with high accuracy.

Facebook’s advertising system uses a software called Facebook Pixel to track user activities on off-platform websites. It also employs algorithms to predict the personal traits and tendencies of users, such as political opinions and relationship status.

Facebook’s advertising system is highly intrusive, tracking users even outside the platform through a data partnership network with third parties and various tracking tools. It is also highly exploitative, as personalized ads are designed to influence user behavior subtly, often leveraging psychological mechanisms. Additionally, this system reinforces information bubbles and echo chambers on the platform, further manipulating how users perceive reality.

Twitter (X)

X’s advertising algorithm promotes tweets and accounts to platform users based on their interests, the keywords they use, and the characteristics of their followers. Ads appear as promoted tweets or through the trending topics and popular accounts feature.

The algorithm analyzes interactions with tweets and hashtags, as well as users’ interests, to target them with ads. The system is characterized by real-time engagement, allowing advertisers to capitalize on trending topics and current events. On the other hand, the fast-paced nature of the platform can make it challenging to maintain ad visibility for an extended period.

The platform collects data based on users’ tweets, re-tweets of others’ tweets, hashtags they use, interactions, their network of followers or those they follow, and interests that can be inferred from their activity. The platform gathers less data compared to Facebook, but the amount of data it collects remains substantial.

The targeting accuracy of X’s advertising system is moderate. While it can direct ads based on user interests and real-time activity, its algorithms are less complex than those used by Facebook to create detailed user profiles.

The system’s level of intervention is moderate. The platform tracks public activity and interactions but does not monitor private or personal interactions to the same extent as Facebook. Finally, its exploitative nature is also moderate. Popular topics and promoted tweets can influence opinions and behaviors, particularly during significant events and heated discussions. However, its impact is limited compared to other platforms that collect larger amounts of personal user data.

TikTok

TikTok’s ad algorithm presents ads in the form of short, engaging videos that appear in the “For You” feed. The algorithm focuses on content discovery and its viral spread. It uses user data, such as video views, likes, shares, and comments, to personalize the ad display.

The strength of TikTok’s ad system lies in its high efficiency in reaching younger demographics through creative, widely shared content. On the other hand, it offers less detailed targeting compared to Facebook. TikTok’s ad algorithm collects data on viewing habits, watch durations, likes, shares, comments, and device-related information to reach the site. It also tracks high-precision behavioral indicators, such as how long the cursor lingers over a specific video (even without actually watching it).

The algorithm achieves a high targeting rate, excelling at understanding user preferences and delivering highly engaging and personalized ad content. It doesn’t rely heavily on demographic data but focuses on real-time behavioral signals.

The TikTok algorithm is considered highly intrusive, as the platform’s data collection practices are extensive and wide-ranging, opening the door for user profiling and potential overreach. Additionally, the algorithm is also considered highly exploitative; the recommendation system is designed to encourage addiction by using short, engaging videos to sustain user interest. The platform seamlessly integrates ads into the original content, making it difficult for users to distinguish between promotional materials and regular posts.

The Impact of Algorithms on Freedom of Choice

The concept of freedom of choice is extremely broad, but in the context of consumption processes, it represents the cornerstone not only of consumer rights but also of the entire economic theory of what is called the free market. The concept of the free market relates to the exchange of goods, services, and money by individuals freely and without external coercive influences on their free will. The economic theory of the free market assumes that consumers make their purchasing decisions based on a logical balance between the utility value of the good or service and their ability to relinquish the financial value represented by the price of the product.

Any intervention in this decision-making process in any form strips the consumers of full control over their behavior and undermines the principle of freedom of choice. This is not only about pushing consumers to buy something they do not need but also about making the purchase decision based on objective grounds, particularly relying on accurate and sufficient information about the product or service. In other words, freedom of choice is about protecting the consumer from exposure to commercial fraud or deception. Certainly, consumers are always more vulnerable to fraud if their purchasing decision is influenced by factors that are not related to the objective circumstances.

Exploitative Advertising Mechanisms

Numerous studies confirm that digital marketing algorithms profoundly influence users’ decision-making processes through mechanisms such as: 

  • Playing on the Fear of Missing Out (FOMO): Digital advertising mechanisms use various tools to instill in consumers a sense that if they do not purchase the advertised product or service, they will miss out on an opportunity that may not recur in the future.
  • Exploiting Personalized Content: Digital ads are delivered through content tailored to address the user personally by leveraging their psychological traits, tendencies, desires, preferences, and vulnerabilities.
  • Illusion of Choice: Digital advertising algorithms use several tools to give users the impression that their purchase decision was a free choice, without any external influences. This is reinforced by the two mechanisms mentioned earlier. The user believes their choice is based on a logical need to avoid missing a one-time opportunity, and through personalization, they see their choice as stemming from their personal tendencies, preferences, and desires.

Loss of Autonomy

Autonomy is a concept closely related to freedom of choice but broader in scope. It pertains to an individual’s independence from external entities to the extent of losing control over their decision-making process. Studies confirm that users’ reliance on algorithms for decision-making is increasing unconsciously due to their significant impact on daily life through social media platforms, search engines, and other applications and websites.

Over time, this leads to the erosion of users’ autonomy and their loss of the ability to make decisions independently based on the information available to them. This is particularly evident in consumption-related decisions, where users turn to advertising and marketing mechanisms to find answers about the best choices for goods and services. However, the same phenomenon extends to other aspects of daily life, as users become increasingly dependent on the influences surrounding them through the internet, which shape their decisions and choices.

The Impact of Algorithms on the Right to Privacy

A large number of studies have been conducted over the past years on the impact of digital advertising and the algorithms used for digital marketing purposes on the right to privacy. Over time, concerns have grown among many about the encroachment of various data collection and analysis mechanisms and digital marketing practices on privacy. The credibility of these concerns becomes evident with the evolution of technologies used in developing digital marketing algorithms, making them more intrusive and exploitative.

Data Collection Practices

Most data collection practices carried out by social media platforms and many other websites represent serious violations of the right to privacy. The majority of these practices lack the conditions of informed consent, transparency, clarity of purpose, and the methods of processing and analyzing the data. This paper has detailed many of these practices in the section titled “How Do Algorithms Collect Data?”

Risks to Privacy

The various practices of digital marketing algorithms expose users’ privacy to numerous risks. These algorithms collect a significant amount of sensitive personal data from users of social media platforms and others. The processes of storing and securing this data are not sufficiently transparent, making it vulnerable to breaches and leaks, which threaten the security and safety of its owners to varying degrees.

Additionally, data processing and analysis produce information, some of which may be highly sensitive, such as detailed profiles, behavioral prediction models, and psychological models of users. Such information can be used to cause significant harm to its owners. It can also be used to manipulate and exploit them for various purposes that are not limited to marketing goods and services.

The principles of protecting the right to privacy require two main factors in any process of collecting personal data:

  • Prior consent from the individual for the collection of their personal data.
  • Full transparency regarding the data collection process and its purpose.

In practice, it is nearly impossible for these two factors to be present in the personal data collection processes carried out by digital marketing algorithms. Given the vast scope and enormous volume of data collected by these algorithms, it is not feasible to define this data in a way that allows obtaining user consent for its collection.

Moreover, the way algorithms operate requires the data they collect to evolve as these algorithms continuously modify themselves through a learning process. This means that, in many cases, no one knows exactly what data the algorithms are collecting. This issue also intersects with the requirement for transparency, as algorithms always function covertly without the user’s knowledge.

Finally, it is impossible for the purposes of data collection to be fully and precisely known in advance. Additionally, companies refuse to disclose exactly how their algorithms operate for reasons related to industrial secrecy, which affects their ability to compete in the market.

The Impact of Algorithms on Digital Rights and Society

Threats to Digital Rights

Digital advertising is the cornerstone of the business model for the majority of profit-driven websites. This model is based on providing services to users for free in exchange for opportunities to market goods and services to them. Websites generate their profits by selling these marketing opportunities to interested parties.

Ultimately, this means that digital marketing processes and all their supporting operations become the primary factor shaping the user’s experience of the internet. What this implies is that the internet user’s freedom of movement within it is subject to continuous interference to direct this movement according to the requirements of exposing the user to the maximum number of advertisements. At the very least, digital advertising practices disrupt the user’s experience by interrupting the content they are interested in and have chosen with advertisements.

This worsens as algorithms gain the authority to determine what content a user can encounter. In cases such as searching for information through search engines, algorithms arrange the search results in a way that maximizes profitability. Advertising content blends with search results in a manner that makes it difficult for users to distinguish between them.

Additionally, advertising content is presented as a reliable source of information, often prioritized over sources like academic research and journalistic investigations. This clearly threatens the right to freely access information and, at the same time, endangers the right to freedom of expression due to algorithms interfering with the reach of content to others.

Reinforcing Economic and Social Inequality

Advertising and digital marketing algorithms are deeply embedded with various social biases during the stages of data collection and processing. These algorithms reproduce such biases by determining the content presented to users based on the classifications they are assigned to.

For example, algorithms may present lower-quality goods and services to specific groups based on the assumption of limited income among their members. Conversely, they direct advertisements for higher-quality products and services to residents of high-income areas. In many cases, these algorithms misclassify individuals into incorrect income brackets due to biases related to race, gender, place of residence, and other factors.

Ethical Responsibility and Accountability

The way algorithms operate, along with the lack of transparency surrounding them, hinders efforts to hold the entities that use them ethically responsible for any potential harm they may cause. It also makes it difficult to hold these entities accountable.

Some of the more severe collateral damages resulting from the operation of personalization and targeting algorithms cannot be proven to be intentional on the part of the developers of these algorithms. Examples of such damages include the emergence of phenomena like echo chambers and information bubbles. The weak possibility of holding the entities developing digital marketing algorithms accountable opens the door to the increasing scope and severity of violations resulting from the development of more intrusive and exploitative algorithms.

Approaches to Addressing Digital Advertising Issues

Existing Regulatory Frameworks

  • General Data Protection Regulation (GDPR): This law has been in effect since May 2018. It regulates the processing of personal data within the European Union. The law mandates that any entity must obtain explicit consent from individuals before collecting or processing their data for advertising purposes. Additionally, the GDPR grants individuals rights over their data, including the right to access it, and the right to erase or modify their personal information held by any entity.
  • California Consumer Privacy Act (CCPA): This law has been in effect since January 2020. It grants residents of California, USA, rights related to their personal data. This includes the right to know that their data is being collected, the right to delete their personal data and the right to opt-out of having their data sold to third parties.
  • Egyptian Personal Data Protection Law: Law No. 151 of 2020, which came into effect in October 2020, regulates the collection, processing, and storage of personal data. The law requires entities to obtain explicit consent from individuals before collecting or processing their data and imposes penalties for non-compliance. It also mandates the appointment of a Data Protection Officer in any entity that handles personal data.

The Need for Better Oversight Systems

There is a need for more effective oversight systems to address the ethical and rights-related concerns surrounding digital advertising systems and their algorithms. Among the features these systems should possess is sufficient flexibility to keep up with the continuous evolution of digital advertising and marketing technologies. This flexibility cannot be provided by legislation due to the nature of the processes required to enact or amend it. Therefore, oversight systems should rely on specialized entities that operate continuously and adjust regulatory mechanisms and rules as needed.

On the other hand, oversight systems should adopt stricter policies regarding the enforcement of fundamental principles such as transparency, informed user consent, and data minimization. Specifically concerning the principle of data minimization, rules, and standards should be established to determine what can be accepted as a necessity justifying the collection, processing, or storage of data. Additionally, oversight systems should include ethical principles governing the use of artificial intelligence technology, such as fairness, non-bias or non-discrimination, accountability, and especially the necessity of human intervention in decision-making processes.

Balancing Development and Rights

Achieving a balance between the development of technologies used in digital advertising and marketing and the rights of users is unattainable under the current business models. The alternative solution lies in introducing parties that are biased in favor of users and their rights into the equation. These parties should not be profit-driven and must be capable of developing technological tools aimed at protecting users against the intrusiveness and exploitation of commercial technological systems.

Possibilities for Supporting Ethical Advertising Systems

There are numerous proposals aimed at encouraging companies, especially major tech firms, to develop and use ethical advertising systems. What makes any of these proposals impractical is that they attempt to position themselves within the framework of a profit-driven business model.

On the other hand, an advertising system can only be ethical when it is not an advertising system, but an information system. The difference is that the purpose of an information system would be to provide an objective and honest presentation of the available shopping options to users, based on mechanisms that help them make a decision based on a precise understanding of their needs and preferences. Such systems could indeed be supported using artificial intelligence technologies. They could also be developed as tools that users turn to when they need information about goods and services that align with their needs and capabilities.

Empowering Users

Empowering users means providing them with the information and tools necessary to protect their data and themselves against the risks and threats posed by digital marketing algorithms. This includes equipping users with information about the algorithms operating on the various websites they regularly interact with, particularly social media platforms and search engines.

Users should become familiar with how algorithms operate, the data they collect, and the methods they use to gather it. Based on this information, users can be provided with tools that enable them to determine which of their data they can allow to be collected, under what conditions, and for what purposes.

Some of these tools have become mandatory for many websites and platforms to provide in order to comply with data protection laws. However, many users remain unable to use them, either because they are unaware of their existence in the first place or because they do not know how to access or use them. Therefore, it is essential to provide this information through various channels to raise awareness.

Conclusion

Digital advertising and marketing algorithms represent a major challenge to digital rights and human rights in general. The most prominent issue in this regard is that this challenge pits technological advancement against human rights. The more digital technology evolves, the more users’ rights are exposed to the encroachment of digital marketing algorithms, and the more users are at risk of losing their autonomy and freedom of choice, as well as their privacy. This reality is not expected to undergo a radical change or see an end to the threats it poses shortly, as it is deeply rooted in the profit-driven business model governing how the internet operates today. This does not mean that it cannot be resisted and that efforts cannot be made to mitigate the risks it creates by every possible means.

This paper has sought to discuss the impact of digital advertising and digital marketing algorithms on freedom of choice and the rights of Internet users. In its first section, the paper provided an overview of how digital marketing algorithms operate by addressing three questions: How do algorithms collect data? How do they analyze it? And how do they exploit the resulting information to influence users?

The paper also presented examples of algorithm systems used by Google Ads, Facebook, Twitter, and TikTok. In its second section, the paper discussed the impact of advertising algorithms on users’ rights, addressing the effects on freedom of choice, the right to privacy, and digital rights. Finally, in its third section, the paper discussed approaches to addressing digital advertising issues, covering both legal and regulatory approaches, as well as efforts to balance development and rights.

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