Betting on Dual-Use Technology: How AI and Marketing Rewires Modern Gambling
Citation: X. Zhu, S. M. Liu, S. Vaid, D. Gozman and K. Michael, "Editorial Betting on Dual-Use Technology: How AI and Marketing Rewires Modern Gambling," in IEEE Transactions on Technology and Society, vol. 6, no. 4, pp. 326-334, Dec. 2025, doi: 10.1109/TTS.2025.3621784.
SECTION I.
Introduction
Credit: Katina Michael
Gambling, once limited to smoky casinos and sports bars, has now transformed into a rapidly expanding data-driven industry that generates hundreds of billions of dollars in revenue and represents a key global economic sector. The global market for gambling was estimated at U.S. 773.7 billion in 2023 and was projected to reach U.S. 1 trillion by 2030 [1]. Notably, offline and online casinos employ more than 2 million individuals across 5000 establishments around the world [2]. This tremendous growth has been further accelerated by technological advancements. Rapid technological development on mobile platforms, combined with sophisticated personalization algorithms, now allows gambling to be available 24/7 worldwide, improving both accessibility and user experience. Traditional gamblers have shown a strong preference for online gambling due to factors such as greater convenience in terms of accessibility and time, enhanced functionalities such as account management, and the ability to place tax-free bets [3]. Reported by the American Gaming Association (AGA), online gaming now accounts for 30% of U.S. gambling revenue, which was U.S. 71.92 billion in 2024, and has continued to grow over the last three years [4]. In their newest report, in April 2024, iGambling has achieved a 32.4% increase from the same period in 2023, signaling a trend that shows no signs of slowing [5].
However, the involvement of technologies has also allowed platforms to influence user behavior in an unprecedented way, raising concerns about problem gambling and ethical responsibilities. While customer data are used for personalized recommendations, it has also increased the risk of exploitation of vulnerable players such as the young and gambling addicts. Of concern, is that many still lack knowledge about how AI algorithms influence their behavior [6], and how current regulations fail to effectively govern data-driven behavioral interventions done “in user interest” [7]. Even more critically, what most people fail to realize is that many of the technologies now embedded in the gambling industry were originally developed for entirely different purposes, including military, defense, or security surveillance. These are examples of dual-use technologies: systems and tools designed for national security or enterprise risk management, now being repurposed to drive consumer engagement, and sometimes, addiction [8].
As online gambling has become a casino in the pocket, we need to examine this question: To what extent does the modern gambling industry, with its integration of technology and marketing, drive business innovation, and to what extent does it exacerbate social risks? In this article, we will discuss the role of AI and technology in the gambling industry, focusing on a societal perspective, and explore how operators can be socially responsible in this sector.
SECTION II.
Impact of Technology in the Gambling Industry
A. Ease of Clicks
With technical advancement, the gambling industry has undergone an evolution “from casinos to clicks” [9]. Traditional slot machines, which once required users to insert coins and physically pull handles to spin reels, are now accessible through a single tap on a smartphone screen [10]. In North Carolina, individuals previously had to travel to a charity to partake in a game of bingo, or to a gas station to buy a lottery ticket. Gamblers traditionally needed to travel hours by car to place sports bets [11]. After online sports betting became legalized there in 2023, those wishing to gamble could simply download apps on mobile devices, and it would take only a few minutes to set up an account and begin placing bets [11].
This ease of online payment and convenient access to the games are prompting more people to engage in internet gambling. In the U.K., the number of active online gambling accounts averaged 4.4 million per month in 2024, reflecting a 10% year-on-year increase [2]. However, the simplicity of the online gambling process can lead to a higher chance of becoming an addicted player. As proposed by Howard Shaffer, a prominent researcher in the field of gambling addiction, the potential of addiction is positively related to repeated interactions [12]. The ease in completing each betting action is undoubtedly making it more repetitive, which leads to a better chance of addiction. Also, traditional casinos required gamblers to line up and exchange physical money for gambling coins or cards, which would reinforce the diminishment of a resource: money. In contrast, today’s one-click payment option in placing bets psychologically makes money spending behavior more imperceptible. This seamless and frictionless transaction process reduces users’ perception of monetary loss, potentially accelerating addictive behavior.
B. Unfairness Caused by Accessibility of AI Tools
In recent years, the emergence of AI has furthered the influence of technology in the gambling sector. It has been used by bettors to increase chances of success and by bookmakers to enable dynamic odds setting and sophisticated risk management [13]. In the sports betting sector, for example, substantial research has been conducted focusing on how accurate machine learning (ML) can be in predicting the result of games. Juuri et al. [13], master students from Aalto University School of Business, have implemented an SVM model with data from Pro Football Focus covering scores and team statistics, receiving a high accuracy of 96% in predicting the outcomes of National Football League (NFL) games.
The high accuracy not only indicates the potential of using a machine learning betting approach to outperform traditional betting approaches [13], but also reveals the concern of loss of fairness in the gambling industry. In the past, gambling had a perceived fairness among all players since everyone had to follow the same rules and have the same chance of winning. Now, with all kinds of machine learning models being able to outperform human prediction, it creates an extensive advantage to those who have the knowledge and computational resources to train the ML models. What once felt like a contest of judgment is now becoming an arms race of data and models.
C. Immersion of CRM
Customer relationship management (CRM) systems in the gambling industry have significantly advanced beyond basic reward programs or simple email lists. Today, with data collection and analysis, CRM tools leverage sophisticated behavioral tracking, allowing gambling operators to create highly personalized experiences that boost user engagement and influence their decision-making.
Player-tracking systems were primarily designed by gambling operators to customize marketing promotions specifically for members enrolled in loyalty programs [14]. These early tracking mechanisms primarily served as simple vehicles for personalized offers, but modern iterations have evolved, using large amounts of gambler data to craft individualized incentives. These incentives are carefully tailored to match user behaviors, making them more persuasive and effective in driving continued engagement.
Notably, CRM systems nowadays go beyond simply rewarding gambling activity; they play an active role in shaping it. Personalized offers, exclusive promotions, and data-driven content streams work together to build an immersive, tailor-made gambling experience for each user. By utilizing extensive historical data, operators strengthen the emotional and psychological connections users feel towards the platform, which ultimately escalates their time spent and loyalty. The immersive nature of these intensive personalized gambling environments was vividly illustrated in the study “Addiction by Design,” where a gambler described the experience as feeling “like being in the eye of a storm” [12]. This highlights the powerful impact of CRM strategies with technology involved, which operate effectively on both conscious and subconscious levels. CRM is about creating what the industry calls “stickiness drivers” for bolstering repeat visits through known behavioral science using persuasive designs.
Furthermore, the gamification of loyalty programs raises serious ethical concerns. While users might perceive loyalty rewards as benign perks, empirical research indicates that they often exacerbate risky gambling behavior, especially among vulnerable gambling populations/compulsive gamblers. Statistics from an experiment in the Australian market show that at least 40% of problem gamblers reported using loyalty cards, in contrast to only around 10% of general gamblers in the sample population. This report suggests that these programs disproportionately attract and reinforce high-risk players [15]. On the other hand, tiered reward structures where higher spending unlocks more prestigious benefits are linked to increased spending even among lower-risk individuals, demonstrating that these designs may actively condition and reinforce behavior rather than simply reward it [16].
Therefore, by integrating operant conditioning principles: variable rewards, tier status, and perceived progress; loyalty programs create positive compulsion loops that reinforce repeat gambling [17]. What may seem like a marketing approach can function as a behavioral conditioning mechanism, amplifying gambling intensity and frequency. This requires rigorous ethical review. Loyalty programs in this industry should be evaluated not only as promotional strategies, but as behavioral engines capable of reinforcing compulsive gambling patterns, especially when located across large user bases.
D. Emotional Targeting Pop-Up Messaging System
Another critical technological innovation in gambling involves personalized pop-up messaging systems. Unlike the traditional forms of communication, these interventions are strategically timed, precisely targeting gamblers at moments of emotional and cognitive vulnerability. Just like loyalty programs, personalized messages don’t just inform users; they shape gambling decisions, particularly around risk-taking, withdrawal timing, and session continuation.
A message like “You’re playing faster than most people” can prompt users to self-reflect and potentially slow down. In contrast, a message such as “You’re on a winning streak—why not keep it going?” leverages emotional momentum and encourages continued betting. These contrasting uses show that personalized interventions are not always responsible or ethical; their effect depends on the intent of the operator. Whether a message interrupts risky behavior or reinforces behavior, it comes down to whether it is designed for prevention or profit. The effectiveness of this strategy is supported by data from the national Dutch Lottery operator, Nederlandse Loterij, that indicated “38% of gamblers reading the “withdrawal” messages withdrew money from their gambling account on the same day” [18].
However, it is not only the content that matters—timing is equally crucial. The recent progress made in machine learning allows companies to better identify when customers are most likely to leave and thus implement churn prevention accordingly [19]. One recurrent neural network (RNN) model that took data from the Belgian online gambling company Gaming1, used a number of casino “plays” (i.e., statistics), such as the company’s Gross Gaming Revenue (GGR) and the number of days since the last active day as inputs, is able to output a probability of churn within the next 30 days with an accuracy of 83% [19]. In practice, the influence of these interventions depends on how well content and timing work together. “Self-appraisal messages were recalled to a greater extent and were more effective when presented during critical decision-making moments” [20], suggesting that interventions aligned with a user’s state of mind or gameplay phase have a stronger behavioral impact.
That said, the power of these tools is a double-edged sword. When thoughtfully implemented, pop-ups can disrupt harmful gambling patterns and encourage healthier choices. However, if poorly timed, overly generic, or framed in the wrong tone, they risk backfiring, which can either be ignored altogether or, worse, unintentionally encourage continued play [20]. This underlines a key ethical tension: the same behavioral science that can reduce harm, can also be misused to nudge users towards further engagement, depending on how it is embedded, and that, is an operator’s choice.
SECTION III.
Cognitive Reframe—Normalization
A. Reframing the Word “Gambling”
As CRM systems and personalized rewards gradually reshape gambling behavior, a broader societal impact emerges: the normalization of gambling as part of everyday leisure and entertainment. Through tactical cognitive reframing, gambling has gradually lost much of its historical stigma, now appearing alongside common digital media like video games or sports apps. When gambling is framed as involving skill, effort, or responsibility, people are less likely to view it as morally questionable [21]. Operators intentionally promote this framing, associating gambling with skill, strategy, and personal control; moreover, they rebrand it as not only harmless, but potentially admirable.
This narrative shift is strengthened by the way problem gambling is portrayed nowadays. Risky gambling behaviors are being rebranded as lighthearted and fun, making people see betting as a normal part of everyday digital routines [22]. When risky behavior is presented as an enjoyable activity, albeit a beneficial interaction, it becomes easier for the public to accept and even embrace as a positive experience. The boundaries between gaming, competition, and wagering are blurring, especially in digital spaces where design and language are carefully planned. For example, online platforms have made gambling-like participation more accessible and appealing to a wider audience by live-streaming tournament brackets and results, further merging fan culture with betting behavior [23]. These streams shrink the gap between watching and making picks. Leaderboards, streak badges, and call-to-action alerts then normalize constant predictions as part of fandom, reframing wagering as playful participation rather than high-risk betting.
Moreover, many gambling platforms now leverage search engine optimization (SEO) strategies to maximize visibility and minimize stigma. Operators replace keywords such as “casino” or “betting” with more neutral or playful terms such as “entertainment,” “play,” and “challenge” [24], effectively repackaging gambling as part of the mainstream digital experience. As these platforms become more immersed in daily online life, the distinction between recreation and risk fades; which makes gambling easier to access, less recognizable, and more socially acceptable than ever before. This effect is amplified by single sign-on and linked accounts that let gamblers jump across affiliated apps without re-registering or resetting. This represents the gamblers with further temptation to continue gambling, than simply having the power to terminate a session instance completely.
B. Ways of Integration
Another mechanism driving the normalization of gambling is its integration into everyday routines, encouraging habitual behaviors. Unlike strategies that disguise or reframe gambling, some forms, such as casual or social gambling, are presented openly yet subtly as ordinary, acceptable activities. As noted in a 2002 report on the social impact of internet gambling, a major contributor to this trend is the rise of online gambling platforms, which are now globally available, easily accessible, and designed for convenience and anonymity [3]. These features effectively eliminate many of the traditional logistical and psychological barriers that once limited participation. Although this research may now seem dated, it is striking that scholars had already identified these risks over two decades ago, demonstrating how easily early concerns can evolve into broader societal issues if left unchecked. Gambling is no longer a distinct event; it is something users can engage in anywhere, anytime, with minimal obstruction.
This accessibility allows gambling to blend into familiar, culturally accepted settings like sports fandom or office pools. By implementing gambling in such frameworks, operators blur the line between entertainment and betting again, but this time encourage routine engagement. In the context of office pools, for instance, users get used to the mechanics of wagering, gradually disassociating betting from risks or stigma. It becomes a social means, a social norm, a way to participate, belong, and connect with their colleagues and community.
Over time, this leads to a deeper psychological shift. Gambling is no longer seen as a separate or potentially harmful behavior. Instead, it is absorbed into a fabric of digital daily life, placed alongside auto-scrolling in social media, playing mobile games, or checking sports scores. This seamless integration makes gambling feel natural, or even necessary, to modern leisure and social interaction. It entices the perception of a positive feedback loop to the end-user, despite there is no real feedback incoming; just further impetus toward pressing a key on a screen, disguised as a logical and premeditated thoughtful action.
C. Increasing Underage Exposure
With easier access to the internet and gambling applications, one of the most concerning social impacts in this industry is the increasing underage exposure, leading to gambling behaviors among youth and adolescents. Imagine a family watching a Friday night football match together on free-to-air television. If they are continuously exposed to betting-related advertising during breaks, and again to an extended sponsored betting segment at half-time, there are bound to be long-lasting effects among some members of the family, whether young or old, who are predisposed to certain behavioral triggers. Research shows that young people can easily recall gambling brands and marketing tactics even without having gambled themselves [25]. A study of Australian 11-year-olds shows that young participants were able to discuss concepts like “odds,” different betting markets, and how to place bets just based on exposure to marketing, with no previous experience in gambling [26]. This shows that marketing not only promotes gambling, it educates and socializes children into a gambling culture long before the legal age. Another key finding from a systematic review is that adolescent gambling marketing normalizes the behavior and cultivates positive attitudes towards it. “Marketing has a significant impact on the normalization of gambling for youth across the globe. This has included shaping positive attitudes towards gambling, as well as increasing the social and cultural acceptance of gambling—particularly aligned with valued activities such as sport” [27]. This strategic framing through sports sponsorships, influencer content, and digital ads effectively obscures harmless fandom and gambling, conditioning younger audiences toward participation.
Together, these studies show a worrying trend: marketing and digital technologies are no longer just promoting wagering; they are acculturating youth into it, eroding critical barriers, and accelerating the tendency of younger gambling populations. This combination of cognitive conditioning and widespread normalization suggests that betting is increasingly becoming an everyday leisure activity even for minors, which raises urgent ethical and policy concerns.
SECTION IV.
Origins and Emergence of Dual-Use Technologies Into Gambling
A. A Historical Look at Dual-Use Innovations
The phenomenon of dual-use technology systems originally designed for defense, surveillance, or security purposes, later repurposed into civilian markets, has a long lineage that now extends into the gambling sector. Historically, dual-use innovations emerged from mission-driven research, often underpinned by state security imperatives. Technologies such as radar, cryptographic computation, and early network architectures were funded by defense agencies but subsequently diffused into commercial and consumer applications as costs fell and civilian markets scaled production [28]. The idea of dual-use technology has evolved significantly over time. It first appeared in scientific and policy discussions to describe the transfer of technical knowledge and materials that could contribute to both civilian development and weapons production [29]. Early definitions focused on technologies capable of serving peaceful as well as military objectives, reflecting post-war efforts to convert the military industrial complex toward civilian production and support broader demilitarization. As innovation accelerated, this distinction weakened. Many technologies could now operate across both domains without modification, as seen with general-purpose computers used in defence operations. This led to a wider recognition that technologies often possess latent potential for diverse and sometimes conflicting purposes. Although there is still no single agreed definition, the way the term is understood continues to influence how researchers examine its risks and opportunities [28].
B. Contemporary Interpretation of Dual-Use
The contemporary interpretation extends beyond military and industrial conversion to encompass systems initially developed for defense, surveillance, or intelligence operations that later diffuse into commercial use. Many foundational advances in computation, networking, and behavioral analytics were first created to enhance national security and support complex decision-making. As production costs fell and architectures became modular, these systems moved into civilian markets where their adaptive learning and predictive capacities generated new forms of value [30]. Embedded in digital services, they have become central to sectors that depend on behavioral data and user attention, including online gambling.
C. AI/ML-Enabled Behavioral Profiling in Gambling
This migration follows a recurring pattern in technological diffusion. Tools once designed for oversight, monitoring, or control under critical conditions are redeployed where they can yield commercial benefits through personalization or prediction. In gambling, machine learning frameworks that were originally used to identify threats, forecast anomalies, or inform operational decisions now underpin customer analytics. Behavioral profiling and adaptive feedback mechanisms first applied in security monitoring are used to detect player disengagement, trigger personalized prompts, and sustain continuous participation [28]. Once algorithmic oversight systems become modular and data-rich, their reuse beyond original regulatory contexts becomes likely [31]. Technologies designed for oversight or control under high-stakes conditions become commodified when their predictive or monitoring capacities can generate value elsewhere [32]. Modern gambling platforms now incorporate machine learning systems once used to identify threats, forecast anomalies, or support command decisions. Behavioral analytics frameworks initially applied in security environments, such as user entity and behavior analytics (UEBA), have been reengineered to profile players, predict churn, and trigger interventions aligned with engagement objectives rather than risk mitigation [33]. The underlying logic remains identical. Data streams are continuously analyzed to detect deviation from expected patterns; only the end goal has shifted from the prevention of malicious activity to the optimization of consumer behavior [34].
The emergence of dual-use AI architectures such as large language models (LLMs) further amplifies this shift. Initially designed in state programs to synthesize intelligence briefings or simulate decision scenarios, such as DHS-366’s generative AI for officer training—these initiatives reflect a strategic bet on dual-use technology [33]. These architectures were trained to dynamically adapt content in response to cognitive performance. In gambling, the same adaptability is retooled for emotional calibration, e.g., pop-ups, messages, and odds adjustments can now be sequenced based on inferred affective state, engagement level, and propensity to continue. The technological logic remains constant, continuous learning through human–machine interaction, but the end is transformed from situational readiness to behavioral reinforcement [35].
D. Neurobehavioral Foundations to Addictive Digital Design
Critically, the neurobehavioral foundations of such systems align with emerging scholarship in neurofinance and digital addiction design [36]. AI systems informed by cognitive neuroscience are increasingly capable of decoding decision-making biases under risk. When embedded in financialized entertainment contexts such as gambling apps, these systems can exploit the very neural pathways they model. Variable reinforcement loops, originally explored for military simulation and attention training, now underpin digital reward architectures that sustain compulsive interaction [8]. This transference illustrates how technical affordances, rather than intentions, drive dual-use emergence: once a system proves effective in capturing attention or predicting deviation, commercial adaptation follows [37]. These algorithmic frameworks, originally intended to enhance vigilance or decision quality under pressure, now sustain engagement loops that encourage longer play sessions and higher spending. The transition illustrates how technical affordances, rather than original intent, determine eventual use [37].
Recent work in neuroscience-informed artificial intelligence has clarified how these feedback architectures operate across contexts [36]. By mapping the cognitive and affective processes underpinning risk and reward, developers created models capable of decoding decision-making biases. When deployed within gambling interfaces, these systems not only predict behavior but also actively shape it through reinforcement. This mirrors findings on digital compulsion loops, where variable reward schedules, first used in simulation and training, now sustain repetitive engagement in consumer environments. In this sense, the gambling industry represents a contemporary endpoint of a longer trajectory. Systems built to detect anomalies, forecast threats, or maintain operational control have evolved into behavioral engines optimized for attention capture [35]. The same predictive precision that once safeguarded critical assets is now used to personalize inducements, reward patterns, and risk exposure. Such recontextualization reveals a continuum between protection and persuasion, highlighting how analytical infrastructures designed for vigilance can transform into instruments of influence when transferred into market settings.
The gambling industry represents a contemporary manifestation of dual-use diffusion. Technologies developed to enhance vigilance and control have evolved into mechanisms of influence and persuasion. Predictive precision and adaptive feedback, once protective capacities, are now used to optimize engagement and discourage restraint. This transformation highlights a shift in purpose rather than in underlying principle.
E. Governance Challenges and Human Oversight
The broader implications extend to governance and accountability and how technologies are implicated in creating unintended consequences [31]. Regulatory systems that traditionally separate defense, enterprise, and consumer technologies are ill-suited to manage tools that move freely across these boundaries. Governance challenges arise precisely because these systems no longer fit within traditional regulatory categories [38]. A framework capable of identifying insider threats can equally be deployed to detect high-value players. When such capabilities are concentrated in private hands without external oversight, asymmetries of information and power widen.
The danger, therefore, is not the technology itself, but the lack of awareness and regulation around its commercial purpose. When a system built to detect threats is instead used to keep players engaged in a betting loop, the ethical landscape shifts dramatically. Yet most users have no idea these powerful systems are operating behind the scenes. This raises not only concerns about manipulation and fairness, but also about consent, privacy, and transparency. The gambling industry’s integration of dual-use technology is a pressing issue that deserves attention not only from regulators, but from consumers and technologists as well. If left unchecked, these innovations could quietly normalize hyper-targeted, ethically questionable business practices under the disguise of entertainment [35].
Understanding the origins and trajectory of dual-use technologies is essential for addressing these challenges. Their integration into gambling illustrates how systems built for public safety can be recontextualized for behavioral influence when economic incentives outweigh ethical constraints. Recognizing this shared lineage is critical for designing policies that ensure technological innovation continues to serve collective well-being rather than exploit individual vulnerability.
SECTION V.
Social Response and Legislation
The evolution of dual-use technologies highlights why a risk-based approach, such as that found in the EU AI Act, is essential for regulating systems that move between contexts [39]. Tools developed for defense or security can be reconfigured for commercial exploitation, meaning their potential for harm depends on how and where they are used rather than on their technical form. A risk-based framework recognizes this fluidity by linking regulatory obligations to the severity and likelihood of harm. The EU AI Act, for example, categorizes AI systems as unacceptable, high, limited, or minimal risk, reflecting concern that technologies designed for oversight or behavioral monitoring may become harmful when repurposed for consumer influence or manipulation. This approach anticipates dual-use diffusion by focusing on the context of use, the distribution of power, and the impact on fundamental rights, ensuring that oversight increases as the potential for harm grows. Applying such a model to dual-use systems like those in gambling would allow regulators to balance innovation and accountability, requiring stronger governance and transparency when predictive or adaptive tools are redeployed to shape behavior or exploit cognitive bias.
Furthermore, operators increasingly offer tools to promote self-regulation as part of their corporate social responsibility (CSR) efforts. One behavioral trial in Australia introduced a voluntary opt-out pre-commitment system that required all licensed gambling platforms to allow users to set binding personal limits on spending and time [40]. This gave gamblers a greater sense of agency over their behavior, marking a clear attempt to align with CSR values by encouraging healthier engagement.
Similarly, the Behavioral Insights Team found that timely interventions like pop-ups and spending limit prompts can serve as useful early-stage tools [41]. While these nudges may not be universally effective, especially for those with severe gambling problems, they are simple, scalable, and can interrupt risky behavior before it escalates. In this context, marketing messages and interface cues act as frontline safeguards; when designed effectively, they can interrupt compulsive usage patterns and encourage users to pause and reflect before continuing [20].
Moreover, with increased personalization in the user interface, pop-up messages, and CRM, it has made players concerned: if we have not been informed of the differentiated treatment, or if we cannot fully understand the algorithms being used, does transparency ever exist in online gambling platforms? Gamblers are often unaware of how they are portrayed in the data systems used by gambling companies and how algorithms are generated for them, and many platforms do not allow their users to turn off this personalization feature. It creates an imbalance of information between customers and gambling companies, which indicates that the decision and consent from customers do not necessarily mean they understand and agree with the algorithms being implemented by the companies.
With the increasing integration of AI in the gambling platforms, operators have the technological capacity to detect problematic behavior patterns through algorithmic monitoring [36]. These systems can flag irregular betting habits or spending patterns in real-time, making proactive harm reduction more achievable than ever [42]. Operators are the ones who have the power when it comes to taking on the ethical responsibility. Even if these interventions are not the most effective, they represent the bare minimum a responsible company must do to maintain its moral bottom line. For operators with AI technology like this, their CSR in this context is not about public relations or image management; it is about upholding a duty of care to users through both design and data.
To guard against operator behavior and prevent problem gamblers, countries where gambling is legalized have created a regulatory framework. However, the rapid advancement in technology has created differences in the modern gambling industry that need to be addressed in legislation. While more consumer data is under exposure on the operators’ end and to more sophisticated algorithms, existing legislation remains outdated and often fails to address these emerging risks. There is an urgent need for updated regulatory measures that consider algorithmic targeting and data-driven behavioral manipulation. That means moving from “legalization” to “algorithmic governance,” such as requiring independent audits and impact assessments for any model that profiles players or personalizes odds; publishing plain and easy-to-understand language summaries of risk and mitigation. It is essential to limit both the amount of data collected and its intended purpose, ensuring that engagement data is not repurposed for manipulation or used for secondary purposes without proper authorization. Safety should be the default: cooling-off windows, speed caps, deposit and loss limit defaults, and frictions on instant re-deposit should be standard, not opt-ins buried in settings. The marketing gaps should be closed by banning youth look-a-like segments and limiting real-time “boost” prompts during high-arousal moments. Finally, giving regulators real visibility, such as incident reporting, whistle-blower protections, and aligned cross-border enforcement so platforms cannot jurisdiction-shop around safety via loopholes, should be mandatory.
SECTION VI.
Conclusion
Gambling was once a negotiation with chance; now it is a negotiation with systems that learn. When the same dual-use tools that predict fraud, churn, or threats are turned toward predicting your expected behaviors, “personalization” can slip into persuasion. That does not make the pastime illegitimate; rather, it makes the boundary work urgent. The question is not whether data should be linked to entertainment, but to set limits as to which parts of our attention we refuse to let it optimize. Until we draw the line between personalization and exploitation, engagement and addiction, the quiet pull will keep running in the background. The final wager is simple and uncomfortable: do we want technology that serves intention, or technology that shapes desire?
SECTION VII.
In This Issue
A. Two Special Issues
IEEE Transactions on Technology and Society vol. 6, no. 4 contains two Special Issues. The first Special Issue was guest edited by Lindsay James Robertson of Massey University, Clinton Andrews of Rutgers University, and Lucy Resnyansky of Defence Science and Technology, titled: “Imagining Tomorrow’s Infrastructure.” Lindsay was a Practitioner-Researcher, Clinton is an academic, and Lucy is from defense, encouraging very diverse types of inquiry. Seven papers were accepted in that Special Issue. The second Special Issue was guest edited by Mallory James of Ludwig-Maximilians-Universität München, Daniel Schiff of Purdue University, Heather A. Love of the University of Waterloo, Iven Mareels of Federation University, Ketra Schmitt of Concordia University, and Greg Adamson of The University of Melbourne, titled: “Extraction by Design—AI, Value, and the Future of Work.” This Special Issue was inspired by the 2023 IEEE Workshop on Norbert Wiener in the 21st Century (21CW2023) with the theme: “The Future of Work in the Age of Automation.” This Workshop was co-located with IEEE ETHICS-2023. Five papers were accepted for this ad hoc Special Issue based on intersecting thematic areas of interest.
B. Scopus Indexing Announcement
IEEE TTS has received an unconditional acceptance into Scopus with the entire back file, i.e., first six volumes of the publication (2020-2025) being incorporated. We can confirm a 2025 Cite Score is pending. We will communicate more as we receive further official news. For now we thank our Vice President of Publications Professor John Impagliazzo of IEEE SSIT, our Managing Editor AndreAnna McLean of IEEE, our founding Editorial Board, including our Senior and Associate Editors, Steering Committee and Publications Board-At-Large. But most of all we are indebted to our authors and reviewers.
References
1. Global Gambling Market To Reach \$1 Trillion By 2030–New Table Games Expand the Consumer Base at Casinos. Accessed: Jul. 12, 2025. [Online]. Available: https://finance.yahoo.com/news/global-gambling-market-reach-1-143000457.html
2. J. T. D. Burke. Global Casino Statistics & Trends. Accessed: Jul. 15, 2025. [Online]. Available: https://casinoindustrynews.com/global-casinostatistics/
3. M. D. Griffiths and J. Parke, “The social impact of internet gambling,” Social Sci. Comput. Rev., vol. 20, no. 3, pp. 312–320, 2002.
4. Commercial Gaming Revenue Tracker. Accessed: Jul. 12, 2025. [Online]. Available: https://www.americangaming.org/resources/commercial-gamingrevenue-tracker/
5. Commercial Gaming Revenue Tracker. Accessed: Jul. 12, 2025. [Online]. Available: https://www.americangaming.org/2024-commercial-gamingrevenue-reaches-71-9b-marking-fourth-straight-year-of-record-revenue/
6. I. A. Wong, K. F. Chau, and H. U. Chan, “An empirical study on customers' gambling intention in AI-supported casinos,” J. Hospitality Tourism Technol., vol. 14, no. 2, pp. 121–136, Feb. 2023.
7. F. Binesh and K. Ghaharian, “Identifying risks and ethical considerations of AI in gambling: A scoping review,” Int. J. Hospitality Tourism Admin., vol. 2025, pp. 1–31, Apr. 2025, doi: 10.1080/15256480.2025.2494575.
8. M. Beltrán, “Defining, classifying and identifying addictive patterns in digital products,” IEEE Trans. Technol. Soc., vol. 6, no. 3, pp. 314–323, Sep. 2025, doi: 10.1109/TTS.2025.3564840.
9. B. Saran, “From casinos to clicks: The evolution of gambling in the digital age,” Santosh Univ. J. Health Sci., vol. 10, no. 2, pp. 173–183, 2024.
10. K. Albrecht, K. Michael, and M. G. Michael, “The dark side of video games: Are you addicted?,” IEEE Consum. Electron. Mag., vol. 5, no. 1, pp. 107–113, Jan. 2016, doi: 10.1109/MCE.2015.2484820.
11. C. Martin, “High stakes, low barriers: The role of technology and regulation in the rise of online sports gambling,” NCJL & Tech., vol. 26, no. 4, p. 511, May 2025. [Online]. Available: https://scholarship.law.unc.edu/ncjolt/vol26/iss4/2
12. N. D. Schüll, Addiction by Design: Machine Gambling in Las Vegas. Princeton, NJ, USA : Princeton Univ. Press, 2012.
13. R. M. Galekwa, J. M. Tshimula, E. G. Tajeuna, and K. Kyandoghere, “A systematic review of machine learning in sports betting: Techniques, challenges, and future directions,” 2024, arXiv:2410.21484.
14. P. Newall and T. B. Swanton, “Beyond 'single customer view': Player tracking's potential role in understanding and reducing gambling-related harm,” Addiction, vol. 119, no. 7, pp. 1156–1163, Jul. 2024, doi: 10.1111/add.16438.
15. P. Delfabbro and D. L. King, “The prevalence of loyalty program use and its association with higher risk gambling in Australia,” J. Behav. Addictions, vol. 9, no. 4, pp. 1093–1097, Jan. 2021.
16. S. J. Hollingshead, M. J. A. Wohl, and C. G. Davis, “On being loyal to a casino: The interactive influence of tier status and disordered gambling symptomatology on attitudinal and behavioral loyalty,” J. Behav. Addictions, vol. 10, no. 3, pp. 675–682, Oct. 2021.
17. B. F. Skinner, The Behavior of Organisms: An Experimental Analysis. Cambridge, MA, USA : B. F. Skinner Foundation, 1938.
18. M. Auer and M. D. Griffiths, “Nudging online gamblers to withdraw money: The impact of personalized messages on money withdrawal among a sample of real-world online casino players,” J. Gambling Stud., vol. 40, no. 3, pp. 1227–1244, Dec. 2023, doi: 10.1007/s10899-023-10276-1.
19. F. Merchie and D. Ernst, “Churn prediction in online gambling,” 2022, arXiv:2201.02463.
20. S. M. Gainsbury, D. Aro, D. Ball, C. Tobar, and A. Russell, “Optimal content for warning messages to enhance consumer decision making and reduce problem gambling,” J. Bus. Res., vol. 68, no. 10, pp. 2093–2101, 2015, doi: 10.1016/j.jbusres.2015.03.003.
21. N. O. Ndubisi, R. Nataraajan, and J. Chew, “Ethical ideologies, perceived gambling value, and gambling commitment: An Asian perspective,” J. Bus. Res., vol. 67, no. 2, pp. 128–135, Feb. 2014, doi: 10.1016/j.jbusres.2012.11.004.
22. R. Gordon, L. Gurrieri, and M. Chapman, “Broadening an understanding of problem gambling: The lifestyle consumption community of sports betting,” J. Bus. Res., vol. 68, no. 10, pp. 2164–2172, Oct. 2015, doi: 10.1016/j.jbusres.2015.03.016.
23. E. H. Kaplan and S. J. Garstka, “March madness and the office pool,” Manage. Sci., vol. 47, no. 3, pp. 369–382, Mar. 2001.
24. S. Go. ( 2023 ). The Ultimate Guide to Casino SEO: How To Rank Higher, Faster. Accessed: Jul. 28, 2025. [Online]. Available: https://www.semrush.com/blog/casino-seo/
25. C. Nyemcsok, S. L. Thomas, A. Bestman, H. Pitt, M. Daube, and R. Cassidy, “Young people's recall and perceptions of gambling advertising and intentions to gamble on sport,” J. Behav. Addictions, vol. 7, no. 4, pp. 1068–1078, Dec. 2018.
26. H. Pitt, S. L. Thomas, A. Bestman, M. Daube, and J. Derevensky, “What do children observe and learn from televised sports betting advertisements? A qualitative study among Australian children,” Austral. New Zealand J. Public Health, vol. 41, no. 6, pp. 604–610, Dec. 2017, doi: 10.1111/1753-6405.12728.
27. H. Pitt, S. McCarthy, M. Randle, G. Arnot, M. Daube, and S. Thomas, “It's changing our lives, not for the better. It's important that we have a say': The role of young people in informing public health and policy decisions about gambling marketing,” BMC Public Health, vol. 24, no. 1, Jul. 2024, Art. no. PMC11282717.
28. S. V. Pustovit and E. D. Williams, “Philosophical aspects of dual use technologies,” Sci. Eng. Ethics, vol. 16, no. 1, pp. 17–31, Mar. 2010.
29. R. Abbas, K. Michael, M. G. Michael, C. Perakslis, and J. Pitt, “Machine learning, convergence digitalization, and the concentration of power: Enslavement by design using techno-biological behaviors,” IEEE Trans. Technol. Soc., vol. 3, no. 2, pp. 76–88, Jun. 2022, doi: 10.1109/TTS.2022.3179756.
30. J. Molas-Gallart, “Which way to go? Defence technology and the diversity of 'dual-use' technology transfer,” Res. Policy, vol. 26, no. 3, pp. 367–385, Oct. 1997.
31. D. Gozman and W. Currie, “The role of rules-based compliance systems in the new EU regulatory landscape: Perspectives of institutional change,” J. Enterprise Inf. Manage., vol. 27, no. 6, pp. 817–830, Oct. 2014.
32. S. Estrin, D. Gozman, and S. Khavul, “Equity crowdfunding and early stage entrepreneurial finance: Damaging or disruptive?,” London School of Economics and Political Science, London, U.K., Tech. Rep. CEPDP1498, Sep. 2017.
33. U.S. Department of Homeland Security. (Feb. 24, 2025). United States Citizenship and Immigration Services–AI Use Cases. Accessed: Aug. 18, 2025. [Online]. Available: https://www.dhs.gov/ai/use-case-inventory/uscis
34. D. Gozman, W. Currie, and J. Seddon, “The role of big data in governance: A regulatory and legal perspective of analytics in global financial services,” Tech. Rep., Dec. 2015.
35. R. Abbas, K. Michael, M. G. Michael, C. Perakslis, and J. Pitt, “Safeguarding the guardians to safeguard the bio-economy and mitigate social injustices,” in Cyberbiosecurity. Cham, Switzerland : Springer, 2023, pp. 185–215, doi: 10.1007/978-3-031-26034-6_12.
36. C. Bormpotsis, M. Nanos, and A. Patel, “A neuroscience-informed AI framework to decode the complexities of neurofinance,” IEEE Trans. Technol. Soc., vol. 6, no. 3, pp. 305–313, Sep. 2025, doi: 10.1109/TTS.2025.3556355.
37. Leonardi, “When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies,” MIS Quart., vol. 35, no. 1, pp. 147–167, Mar. 2011. [Online]. Available: https://www.jstor.org/stable/23043493
38. D. Schlagwein, D. Gozman, and A. P. Manusu, “Cryptocurrency frames of reference: A case study of accepting ‘Bitcoin-as-X,’” Eur. J. Inf. Syst., vol. 2025, pp. 1–36, Jul. 2025.
39. European Parliament.( 2025 ). EU AI Act: First Regulation on Artificial Intelligence. Accessed: Sep. 30, 2025. [Online]. Available: https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence
40. N. Hing, M. Browne, A. M. T. Russell, M. Rockloff, and C. Tulloch, “A behavioural trial of voluntary opt-out pre-commitment for online wagering in Australia,” Gambling Res., Sydney, NSW, Australia, Tech. Rep., Aug. 2021.
41. ( Oct. 2018 ). Can Behavioural Insights Be Used to Reduce Risky Play in Online Environments?. [Online]. Available: https://www.bi.team/publications/can-behavioural-insights-be-used-to-reduce-risky-play-in-online-environments/
42. V. Marionneau, H. Ruohio, and N. Karlsson, “Gambling harm prevention and harm reduction in online environments: A call for action,” Harm Reduction J., vol. 20, no. 1, Jul. 2023, Art. no. 92, doi: 10.1186/s12954-023-00828-4.
ACKNOWLEDGMENT
The authors acknowledge in this issue of IEEE Transactions on Technology and Society one of our founding editorial board members, Dr. Lindsay James Robertson, for his effort in leading the first Special Issue (February 2, 1954–September 25, 2025). Memory everlasting. They also acknowledge the unending contribution of Mrs. Katie Sullivan of IEEE, Senior Manager Journal Productions. Her tenacity and endurance is part of the reason our publication is so successful. Memory eternal.
Authors
DeGroote School of Business, McMaster University, Hamilton, ON, Canada
Xuanyan Zhu is currently pursuing the Bachelor of Commerce degree (Hons.) with the DeGroote School of Business, McMaster University, Hamilton, ON, Canada, with a focus on marketing and a minor in biology. She is particularly interested in how artificial intelligence, data analytics, and digital communication influence consumer behavior and social well-being. She has contributed to projects exploring responsible innovation, technology ethics, and the societal implications of emerging digital marketing strategies. Her academic and research interests include the intersection of technology and marketing fields.
Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
Si Min Liu is currently pursuing the B.A.Sc. degree in computer engineering with the University of Toronto, Toronto, ON, Canada. She has contributed to interdisciplinary projects bridging technology design, data ethics, and societal impact. Her research interests include artificial intelligence and emerging technologies, with a particular focus on their societal and ethical implications.
DeGroote School of Business, McMaster University, Hamilton, ON, Canada
Shashank Vaid received the M.B.A. degree from Duke University, Durham, NC, USA, and the Ph.D. degree in marketing and econometrics from the University of Houston, Houston, TX, USA.
The University of Sydney Business School, The University of Sydney, Darlington, NSW, Australia
Henley Business School, University of Reading Berkshire, RG6 6UR Reading, U.K.
Daniel Gozman received the B.Sc. (Hons.), M.Sc. (Hons.), P.G.Cert. H.E., F.H.E.A., M.B.C.S., and Ph.D., degrees from Freeman of the City of London. He is an Associate Professor at the University of Sydney Business School (Australia) and an Honorary Fellow at Henley Business School, University of Reading (UK). He earned his PhD from the London School of Economics and Political Science, where his research explored the institutional transformation of financial regulation and compliance technologies in the aftermath of the global financial crisis. He is also aMember of the University College London’s Centre for Blockchain Technologies, a Freeman at the Worshipful Company of Information Technologists, and a Fellow of the Higher Education Academy. He is a Member of Standards Australia Committee on ISO/TC 307, Blockchain and Distributed Ledger Technologies. His research examines the intersection of policy, technology, and innovation, with a particular focus on how emerging technologies, such as artificial intelligence, blockchain/distributed ledgers and digital currencies, and platform infrastructures, transform governance, compliance, and risk management in both private and public sector contexts. His work investigates how regulators, policymakers, and firms adapt to digital transformation across domains including FinTech, RegTech, SupTech, cybersecurity, digital identity, and critical national infrastructure.He has served in multiple academic and leadership capacities at Sydney, including Director of Engaged Research (2019-2023), member of the Centre for AI, Trust and Governance Management Committee, and co-chair of the Communications and Technology for Society Research Group reflecting his interdisciplinary commitment to understanding the societal impact of emerging technologies. He has led or co-led several major funded projects supported by organisations such as the UK Engineering and Physical Sciences Research Council (EPSRC), EU Horizon 2020, and SWIFT Institute. His research has produced high-quality journals, including the Journal of Management Information Systems, Journal of Information Technology, Information Systems Journal, European Journal of Information Systems, MIS Quarterly Executive, and Journal of Business Research. His studies have informed policymaking and regulatory consultations at the Bank of England, Financial Conduct Authority (UK), International Monetary Fund, Standards Australia, and the Monetary Authority of Singapore. An active member of the global Information Systems community, he is a Senior Editor for the Journal of Information Technology and an Associate Editor for the Information Systems Journal. He has co-edited major special issues on digital regulation, emerging technologies, and critical national infrastructures for leading journals and serves on panels and track chair for premier international conferences such as ICIS, PACIS, and AOM. His thought leadership extends beyond academia. His research and commentary have appeared in the Wall Street Journal, Sunday Times, SBS World News, Vice Magazine, an Computer Weekly. Before entering academia, he worked at Accenture and Deloitte, experiences that continue to inform his practice-led teaching and research on digital transformation, financial technology ecosystems, and responsible AI governance.
The University of Sydney Business School, The University of Sydney, Darlington, NSW, Australia
School for the Future of Innovation in Society, Arizona State University, Tempe, AZ, USA
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
Newcastle University Business School, Newcastle upon Tyne, U.K.
Katina Michael (Senior Member, IEEE) received the B.S. degree in information technology from the School of Mathematical and Computing Science, University of Technology, Sydney, NSW, Australia, in 1996, the Ph.D. degree in information and communication technology from the Faculty of Informatics, University of Wollongong, Wollongong, NSW, Australia, in 2003, and the Master of Transnational Crime Prevention degree from the Faculty of Law, University of Wollongong, in 2009.
Citation: X. Zhu, S. M. Liu, S. Vaid, D. Gozman and K. Michael, "Editorial Betting on Dual-Use Technology: How AI and Marketing Rewires Modern Gambling," in IEEE Transactions on Technology and Society, vol. 6, no. 4, pp. 326-334, Dec. 2025, doi: 10.1109/TTS.2025.3621784.