By Andrew M. Hargarten

– Executive Summary –

The emergence of novel technologies, including artificial intelligence, poses challenges and opportunities for governance frameworks to foresee and address their impacts on society and security. The swift progression of technological advancements, illustrated by the introduction of OpenAI’s ChatGPT4 in 2023, reveals the disparity between the rapid development of technology in the private sector and the slower, more considered approach to policy creation and regulation by government entities. This situation points to the pressing need for effective, practical tools to bridge this divide, ensuring policy formulation keeps pace with technological evolution.

RESEARCH QUESTION AND METHOD

This thesis builds the emerging technology awareness index (ETAI), a strategic tool for policymakers to keep pace with rapid technological developments. The central question of this inquiry is as follows: What are the essential components and factors to consider in constructing a comprehensive index to evaluate and alert policymakers about emerging technologies? This investigation merges critical elements and techniques to create a mechanism for accurately determining the societal implications of emerging technologies. Employing the future sign framework, the study integrates anticipatory systems theory, the social construction of technology (SCOT), and social identity theory (SIT), providing a multifaceted perspective that spans qualitative and quantitative methodologies. This diverse approach seeks to surpass the limitations of existing methods for monitoring technological innovations.

The development of the ETAI features both objective and subjective analyses. The objective side employs advanced quantitative techniques, such as natural language processing (NLP), to detect technological trends not yet widely recognized. These features reflect the thesis’s commitment to creating a theoretically robust and methodologically precise tool, designed to enhance the anticipatory abilities of policymakers as they navigate both the challenges and opportunities presented by new technologies. The subjective analysis deepens the ETAI’s analytical capacity by integrating SCOT and SIT within the conceptual framework. This integration enables a thorough exploration of how emerging technologies might align with or challenge the interests and viewpoints of diverse social groups, offering qualitative insights to inform policymaking approaches amid evolving technological landscapes.

A set of comprehensive index requirements guided the shaping of the ETAI. Initially, emerging technology was defined using five essential traits: radical novelty, rapid growth, coherence, significant potential socioeconomic impact, and inherent uncertainty and ambiguity.[1] These traits were then integrated into the ETAI’s structure, ensuring the index accurately reflected these characteristics in its analysis. Methodological approaches promoted by the National Research Council Canada were adopted for the objective aspect while the innovative combination of the SCOT and SIT frameworks for the subjective element enriched the analysis with insights into technology’s anticipated societal effects.[2]

The ETAI applies a detailed approach to detecting early signals of emerging technologies by analyzing vast data sets with techniques such as NLP. This quantitative analysis is designed to identify technologies still in their earliest stages, highlighting growth rates and coherence among technologies to form a data-driven basis for the index. The qualitative analysis enhances this capability by examining the interplay between technologies and social group narratives, enriching the understanding of their potential effects.

The ETAI operates through a systematic process that first identifies weak signals and then analyzes them to understand their implications. This dual approach, combining quantitative and qualitative methods, yields a comprehensive view of new technologies and their potential effects. The findings from this analysis are incorporated into the ETAI, designed to evolve continually through feedback and updates, ensuring its relevance and accuracy. This process equips policymakers with a tool for anticipating emerging technologies and making informed decisions about them.

CONCLUSION

The ETAI embodies an innovative step toward more proactive foresight in technology policy formulation, extending beyond traditional predictive or trend analysis models. By employing a semiotic approach to categorizing signals within a future-sign vector space, the proposal combines quantitative data with sociotechnological evaluations to offer a nuanced strategic perspective. The ETAI is envisioned as a valuable tool for policymakers, offering a new framework for strategic foresight in technology planning. By focusing on the early detection of weak signals and undertaking comprehensive analyses that include the perspectives of relevant social groups, the ETAI aspires to facilitate the early recognition of technological trends and foster a governance culture that proactively addresses the potential challenges and opportunities presented by technological advancements.

RECOMMENDATIONS

A systematic approach is essential to ensuring that the ETAI is practical and applicable. It involves validating the ETAI’s real-world utility through quantitative and qualitative analyses. Recommendations for this process include the following:

1. Prototype Development and Integration: A crucial initial step involves creating an ETAI prototype, incorporating a language model to analyze extensive textual data, and identifying early indicators of emerging technologies. The selection of a language model emphasizes the need for flexibility and continuous updates in response to swift technological and methodological changes.
2. Data Collection and Operational Workflow: Concurrently with model integration, establishing a real-time data collection system using various application programming interfaces is vital. This system will supply the ETAI with a constant stream of current, structured data from reliable sources. Following integration of the language model and data collection systems, developing a well-organized operational workflow is necessary to ensure efficient data processing, thus enabling the dynamic analysis of technological evolution.
3. Qualitative Analysis Integration: Upon detecting early signals, it is critical to incorporate qualitative analysis. This step involves applying SCOT and SIT frameworks to investigate the relative social groups’ narratives impacted by emerging technologies. This phase enriches the analysis by providing depth and context and evaluating the social and cultural ramifications of the identified technological signals.
4. Testing, Refinement, and Presentation: The ETAI prototype then moves into a testing and refinement phase to confirm its functionality and relevance. This phase includes examining and adjusting the prototype based on extensive feedback to improve its performance, efficiency, and user suitability. Communicating the insights of the ETAI effectively, through both visual and narrative formats, is crucial. This approach should summarize the findings clearly, making complex analyses understandable and actionable for policymakers.

The ETAI’s development encompasses a series of steps to transform it from a theoretical concept into a practical tool. This transformation positions the ETAI as a dynamic and relevant resource for anticipating emerging technologies and influencing policy decisions. The approach emphasizes the necessity of ongoing learning, flexibility, and the blending of precise analytical techniques with extensive qualitative insights. By focusing on these aspects, the ETAI promises to be a strategic foresight tool in technology awareness, ensuring it remains a cutting-edge resource for policymakers navigating the complexities of technological advancement.

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[1] John Gerring, “What Makes a Concept Good? A Criterial Framework for Understanding Concept Formation in the Social Sciences,” Polity 31, no. 3 (Spring 1999): 357–93, https://doi.org/10.2307/‌3235246.

[2] Ashkan Ebadi, Alain Auger, and Yvan Gauthier, “Detecting Emerging Technologies and Their Evolution Using Deep Learning and Weak Signal Analysis,” arXiv, May 11, 2022, http://arxiv.org/abs/‌2205.05449.