AI Regulation: ECRs’ Perspectives is a CREATe blog series featuring the work of early career researchers who are exploring the contemporary challenges of AI regulation. Drawing primarily on research presented at the AI Regulation ECR Conference, while also including related work on AI regulation, each post provides concise and accessible insights into emerging legal and policy debates around artificial intelligence.
Our final blog post in this series comes from Qingqin Zhang, Weiwei Yi, Aline Iramina and Gabriele Cifrodelli.
What Comes Next for AI Regulation? Reflections from the AI Regulation ECR Conference Organising Committee
The question of how artificial intelligence (AI) should be regulated is no longer an abstract one. The answer is being shaped in real time by public and private actors across different sectors, markets and contexts.
In March 2026, the AI Regulation Early-Career Researchers Conference brought together ECRs from the UK and the EU to explore policy and legal developments related to AI regulation in four main areas: workers’ privacy and data protection, consumer protection, platform regulation, and intellectual property.
This blog series AI Regulation: ECRs’ perspectives has captured a snapshot of those conversations.
Rather than providing a summary, however, we would like to conclude this series by offering our own reflections on the challenges that lie ahead. Drawing on our respective research, we consider some of the main questions that we believe will shape the discussions about the future of AI regulation.
Qingqin Zhang (University of Glasgow) – AI and Workers’ privacy and data protection
Artificial intelligence is transforming the contemporary workplace in profound ways. Although AI can improve work efficiency, for example, by supporting recruitment, performance evaluation, and workforce management, it also poses significant risks to workers’ fundamental rights protection. Automated systems may influence employers’ decisions about hiring, promotion and dismissal, yet the workers affected often have little understanding of what data have been collected, how those data have been interpreted, or how an unfavourable decision can be challenged. Such automated systems make fairness and accountability increasingly difficult to secure because of the lack of transparency. Algorithmic management systems may also exacerbate existing bias, dismiss workers unfairly, and produce consequential decisions without offering an adequate explanation or an effective route to redress. At the same time, AI-enabled monitoring allows employers to track workers’ keystrokes, communications, locations, and productivity, and thus becomes more pervasive. Responding to these challenges requires data protection and labour law to develop alongside AI technological change. In particular, involving trade unions and worker representatives in decisions about the introduction and use of AI could limit intrusive monitoring and give workers a meaningful voice in shaping the technologies that increasingly govern their working lives.
My current research focuses on exploring how best to protect workers’ privacy rights in the UK in the age of artificial intelligence. I examine how the growing prevalence of algorithmic management, AI-driven workplace surveillance and automated decision-making is reshaping the power dynamics between employers and workers. Adopting a ‘Law and Political Economy’ (LPE) research approach, I aim to establish a distinctive theoretical basis for protecting workers’ privacy rights: strengthening institutional support for workplace democracy, especially in the context of workplace neoliberalism. My research does not treat privacy merely as an individual right or a matter of consent but delves into its collective and institutional dimensions. It explores how trade unions, collective bargaining, worker participation, co-determination and regulatory oversight can empower workers to have a say in decisions regarding the collection and use of their data. My research seeks to demonstrate that the protection of privacy is not only crucial to individual dignity and autonomy, but is equally indispensable to equality, justice and democratic governance in the workplace.
Weiwei Yi (University of Glasgow) – AI and Consumer protection
The main challenge in consumer protection that inevitably intersects with AI regulation comes down to a lack of clarity about what fairness means when internet users’ rights and interests are affected by this unprecedented and disruptive technology. The need to recalibrate our understanding of fairness in this context faces the need for renovation from two directions. On one hand, it concerns the definition of ‘consumer’, who is no longer simply a party to a transactional contract. The term must be understood in a broader manner, since individuals may never transact directly with a trader yet remain inevitably influenced by AI systems of various kinds. The lack of transparency in how these systems work (in terms of explainability and interpretability) makes it difficult for consumers to make informed decisions about aspects of their lives that the systems touch. This is an indirect harm: it does not challenge consumers’ rights directly, but nonetheless diminishes the welfare they could otherwise have enjoyed. On the other hand, there is the threat to unconventional interests that have little to do with the monetary interests traditionally associated with ’consumers’, such as mental health costs and damage in the form of addictive, endless consumption of attention-capturing content driven by AI-powered “recommended for you” algorithms. This represents a nascent dimension in understanding fairness as it relates to user and consumer welfare.
Aligning fairness in consumer protection with the need for technological innovation has been a difficult task for all jurisdictions, but I argue that this is a necessary step that requires regulators to move beyond narrow interpretation of consumer and consumer welfare, to depart from the focus of only transaction-based notions of harm and toward a framework that accounts for the diffuse, cumulative, and often invisible ways AI systems shape consumer welfare, for the sake of the greater societal good.
Aline Iramina (University of Glasgow) – AI and Platform regulation
As AI becomes increasingly embedded in platform ecosystems, it is important to understand the intersection between platform governance and AI regulation. AI systems are now integrated into a wide range of platform activities, including content moderation and recommendation systems on social media and streaming services, and automated decision-support tools on specialised platforms, such as those being developed in the healthcare sector. Common questions (and challenges) arise across these contexts in relation to accountability, transparency, data governance, and the allocation of responsibility when harm occurs. At the same time, the specific risks associated with these systems vary. On social media platforms, for example, concerns include the spread of misinformation, manipulation, harmful content, and the amplification of existing biases and inequalities. In healthcare settings, concerns may relate to the quality of automated decision-making, the governance of sensitive data and potential consequences of errors or bias. These developments suggest that platform regulation cannot be considered separately from AI regulation. This is why regulators and other stakeholders, when developing and implementing frameworks such as the UK Online Safety Act and the EU Digital Services Act (DSA), must recognise that AI systems are now an integral part of the socio-technical environments that constitute online platforms. These instruments must be considered within a broader regulatory landscape that also includes AI-specific and data governance instruments, such as the Artificial Intelligence Act and the General Data Protection Regulation (GDPR), as AI creates risks and opportunities that cannot be adequately addressed by a single regulatory regime.
My own research focuses on the regulation of AI systems embedded within online services, particularly from the perspective of music creators and the creative industries more broadly. It focuses specifically on algorithmic transparency as the main policy solution. Even though they are not considered high-risk or even (in some cases) integrated into online platforms, I argue that some AI systems employed in the creative sector may still require some form of transparency regulation. This is the case with recommendation and content moderation systems and generative AI applications used in streaming services, for example, as these can have significant impact on creators’ visibility, income and opportunities. Therefore, existing AI and platform regulatory frameworks do not fully address these effects. Consequently, some regulatory questions extend beyond AI and platform regulation to other areas of law, including intellectual property law.
Gabriele Cifrodelli (Maastricht University)– AI and Intellectual Property
A key challenge for the regulation of AI in the field of Intellectual Property lies in reconciling the demand for transparency with the heterogeneous ways in which AI systems are developed and deployed across industries. A “one-size-fits-all” solution appears increasingly inadequate: while consumer-facing models (such as text-to-text or text-to-image systems) can reasonably be required to disclose aspects of their training data – including the use of copyrighted materials- this expectation becomes significantly more complex in sectors where data is commercially sensitive or legally protected. For instance, in areas such as drug discovery, datasets may embed trade secrets, rendering full disclosure either impractical or undesirable.
Against this backdrop, a tailored, industry-focused regulatory approach seems necessary, but it also suggests that the solution may not reside within Intellectual Property law alone. Instead, effective governance of AI should integrate multiple regulatory domains, including consumer protection, data protection, and competition law, while still remaining sensitive to sector-specific dynamics. The key challenge ahead is therefore to design a coherent and flexible framework that balances transparency and accountability with the protection of confidential and sensitive information, possibly through differentiated obligations and cross-disciplinary regulatory coordination.
Taken together, these perspectives illustrate how AI regulation is shaped not by a single framework, but by the intersection of several legal areas and instruments, involving different actors and normative commitments. Regardless of whether the focus is on workers’ rights, consumer protection, platform regulation, intellectual property rights, or any other legal domain, the questions raised by AI ultimately come back to broader concerns about governance, transparency and accountability.
Throughout this series, contributors have demonstrated how ECRs are engaging with, and helping to shape, these discussions.
We would like to thank all the contributors to this blog series, as well as the speakers, participants and supporters of the 2026 AI Regulation ECR Conference, for making these conversations possible. This series is just the beginning, and we hope that it will spark ongoing discussion and collaboration regarding the future of AI regulation.
Finally, we encourage readers to revisit the contributions that made up this blog series. Each post offers a different perspective on the regulation of AI, showcasing the diverse range of research being conducted by early-career researchers in the UK and EU.
Read the full blog series:
- The Performance of Transparency under Article 15(1)(h) UK GDPR (Neil Saddington)
- Black Box Medicine and the Governance of AI Platforms: Transparency, Risk and Regulation (Federico C. La Vattiata)
- The Translation Layer: Why AI Regulation Needs More Than Just Good Intentions (Derya S. Esen)
- AI filmmaking and Streaming Giants (Panagiotis Lampropoulos)
- Beyond Risk Classification: Worker-Centred Governance of AI in Industrial Workplaces (Ines Neves)
- Unsettled Categories: Neurodata Processing in Occupational Safety and Health at the Boundaries of Data Protection (José Miguel Diéguez Rodríguez)
- Copyright protection of prompted generative AI outputs in the United Kingdom: A preliminary taxonomy (Cody Rey-Anderson)
- Can we have a platform regulation without AI regulation? (Sejal Chandak)
- Can we CREATe a Pro-Consumer AI Regulation? (Anna Katharina Suzuki-Klasen)
- What Comes Next for AI Regulation? (Qingqin Zhang, Weiwei Yi, Aline Iramina & Gabriele Cifrodelli)
