Fast-paced AI innovation outruns slow regulation, undermining accountability

A new study reveals that the effectiveness of artificial intelligence (AI) accountability measures depends not only on technology and regulation but also on the narratives and structures of time embedded in these systems. The research underscores how temporal factors influence which interventions are seen as possible, desirable, or ultimately successful.

Published in the Social Science Computer Review, the study "Unblackboxing How Sociotemporalities Inform AI Accountability: The Case of Targeted Advertising", the paper examines targeted advertising, a sector long criticized for privacy violations, opaque practices, and discriminatory outcomes, as a prime example of how sociotemporal dynamics shape AI governance. Through a retrospective analysis of interviews and transparency interventions, the study challenges the dominant focus on technical solutions and highlights the need for a more contextually informed approach.

Why do temporal narratives matter in AI governance?

The research introduces sociotemporalities as a critical lens for understanding AI accountability. Unlike traditional transparency efforts that focus on opening the “black box” of algorithms, sociotemporalities reveal how perceptions of time, such as innovation speed, regulatory delays, and deadlines, actively shape both technology and governance outcomes.

Among others, the key finding is the “pacing problem,” a persistent tension between the rapid evolution of AI-driven technologies and the slower, more deliberate pace of regulation. Participants in the study noted that by the time regulatory frameworks are implemented, they often fail to address the latest industry practices, making them ineffective or irrelevant. This mismatch not only undermines accountability efforts but also reinforces the dominance of powerful industry players capable of moving faster than oversight mechanisms.

Moreover, the study shows that these temporal dynamics are deeply intertwined with sociomaterial factors, including business models and market structures. For example, targeted advertising thrives on opaque operations and asymmetrical information, making transparency efforts difficult to enforce. Transparency, rather than serving as a neutral public good, often conflicts with the profit-driven logic of the adtech industry, where withholding information is a source of competitive advantage.

How did temporal structures shape transparency efforts?

The authors re-analyzed a transparency intervention known as the Targeted Advertising Tracking Extension (TATE), designed to make the operational processes behind targeted ads more visible. Interviews with stakeholders revealed divergent perceptions of its feasibility and desirability, shaped by temporal and material constraints.

For many participants, transparency tools were seen as impractical unless they could be reframed as business assets. Regulatory interventions, particularly under frameworks like GDPR, were criticized for their rigid timing and disproportionate impact on smaller companies. Compliance deadlines imposed significant costs, reinforcing market concentration and sidelining smaller actors who lacked the resources to adapt.

The study also identified how notions of the “right time” for intervention influenced decisions. Industry stakeholders argued that premature regulation could stifle innovation, while late interventions would fail to address entrenched practices. These perceptions shaped what forms of accountability were considered realistic. Ultimately, interventions were either delayed until the market stabilized or watered down to align with industry priorities, limiting their transformative potential.

Alternative approaches such as micropayment models, reversed transparency (where users disclose targeting preferences), and direct consumer relationships were discussed but faced similar constraints. Each scenario was shaped by temporal logics that prioritized short-term efficiency over long-term ethical accountability.

What lessons does this study offer for future AI accountability?

The findings highlight that AI governance cannot rely solely on technical transparency or isolated regulatory efforts. Instead, it must account for the temporal dynamics that condition what is perceived as achievable. The authors argue that unblackboxing, the process of exposing hidden elements of AI systems, must extend beyond technical and material dimensions to include temporal narratives that shape the present and future of technology.

This expanded perspective reveals why many accountability interventions have failed to deliver meaningful change. Efforts that ignore the pacing problem or fail to consider the timing of regulation risk reinforcing existing inequalities rather than addressing them. For example, interventions focused narrowly on technical transparency overlook how past decisions and future expectations influence current practices.

The study further warns against relying on short-term cost-benefit reasoning, which often dismisses interventions that require structural change or long-term investment. Instead, policymakers should consider how regulatory timing, market maturity, and historical legacies interact to shape the space of possible futures for AI governance. By challenging deterministic assumptions about the inevitability of technological progress, sociological analysis can open new pathways for designing interventions that are both practical and ethically grounded.