The Use of Mobility Data for Responding to the COVID-19 Pandemic

As the COVID-19 pandemic continues to upend the way people move, work, and gather, governments, businesses, and public health researchers have looked increasingly at mobility data to support pandemic response. This data, assets that describe human location and movement, generally has been collected for purposes directly related to a company's business model, including optimizing the delivery of consumer services, supply chain management or targeting advertisements. However, these call detail records, smartphone-mobility data, vehicle-derived GPS, and other mobility data assets can also be used to study patterns of movement. These patterns of movement have, in turn, been used by organizations to forecast disease spread and inform decisions on how to best manage activity in certain locations.Researchers at The GovLab and Cuebiq, supported by the Open Data Institute, identified 51 notable projects from around the globe launched by public sector and research organizations with companies that use mobility data for these purposes. It curated five projects among this listing that highlight the specific opportunities (and risks) presented by using this asset. Though few of these highlighted projects have provided public outputs that make assessing project success difficult, organizations interviewed considered mobility data to be a useful asset that enabled better public health surveillance, supported existing decision-making processes, or otherwise allowed groups to achieve their research goals.The report below summarizes some of the major points identified in those case studies. While acknowledging that location data can be a highly sensitive data type that can facilitate surveillance or expose data subjects if used carelessly, it finds mobility data can support research and inform decisions when applied toward narrowly defined research questions through frameworks that acknowledge and proactively mitigate risk. These frameworks can vary based on the individual circumstances facing data users, suppliers, and subjects. However, there are a few conditions that can enable users and suppliers to promote publicly beneficial and responsible data use and overcome the serious obstacles facing them.For data users (governments and research institutions), functional access to real-time and contextually relevant data can support research goals, even though a lack of data science competencies and both short and long-term funding sources represent major obstacles for this goal. Data suppliers (largely companies), meanwhile, need governance structures and mechanisms that facilitate responsible re-use, including data re-use agreements that define who, what, where, and when, and under what conditions data can be shared. A lack of regulatory clarity and the absence of universal governance and privacy standards have impeded effective and responsible dissemination of mobility for research and humanitarian purposes. Finally, for both data users and suppliers, we note that collaborative research networks that allow organizations to seek out and provide data can serve as enablers of project success by facilitating exchange of methods and resources, and closing the gap between research and practice.Based on these findings, we recommend the development of clear governance and privacy frameworks, increased capacity building around data use within the public sector, and more regular convenings of ecosystem stakeholders (including the public and data subjects) to broaden collaborative networks. We also propose solutions towards making the responsible use of mobility data more sustainable for longterm impact beyond the current pandemic. A failure to develop regulatory and governance frameworks that can responsibly manage mobility data could lead to a regression to the ad hoc and uncoordinated approaches that previously defined mobility data applications. It could also lead to disparate standards about organizations' responsibilities to the public

Frontiers in Energy Storage: Next-Generation Artificial Intelligence (AI)

The Department of Energy's (DOE) Office of Electricity (OE) sponsored the "Frontiers in Energy Storage: Next-Generation Artificial Intelligence (AI) Workshop", which was hosted at Lawrence Berkeley National Laboratory on April 16, 2024. This hybrid event convened industry leaders, researchers, and innovators both in-person and virtually to discuss the transformative potential of AI in enhancing the development and adoption of grid-scale energy storage. Participants presented and discussed how advancements in machine learning (ML) and AI can catalyze innovation in material development, system integration and optimization, performance validation, and strategic policy development. The wide-ranging workshop spanned topics from accelerated materials development to policy and valuation of long duration energy storage systems as well as the use of AI-powered agentic systems to manage grid operations. Throughout the event, participants highlighted the technical, social, and financial hurdles in building and maintaining robust data infrastructures that serve as the foundation for AI/ML innovation. Key recommendations were made across the entire range of topics, with clear needs for the development of scalable and trustable AI tools, enhancement of data availability and interoperability, the need for interpretability and transparency in policy decisions, and promotion of cross-sector collaboration to realize the full potential of AI in energy storage. Additionally, the workshop underscored the importance of fostering collaboration between academic/government researchers and industry to fully unlock the potential of AI in energy storage and grid operations. This report summarizes these discussions, with the goal to guide and inform future advancements of AI for energy storage that align with national goals for energy efficiency and sustainability

Regulating Data as Property: A New Construct for Moving Forward

The global community urgently needs precise, clear rules that define ownership of data and express the attendant rights to license, transfer, use, modify, and destroy digital information assets. In response, this article proposes a new approach for regulating data as an entirely new class of property. Recently, European and Asian public officials and industries have called for data ownership principles to be developed, above and beyond current privacy and data protection laws. In addition, official policy guidances and legal proposals have been published that offer to accelerate realization of a property rights structure for digital information. But how can ownership of digital information be achieved? How can those rights be transferred and enforced? Those calls for data ownership emphasize the impact of ownership on the automotive industry and the vast quantities of operational data which smart automobiles and self-driving vehicles will produce. We looked at how, if at all, the issue was being considered in consumer-facing statements addressing the data being collected by their vehicles. To formulate our proposal, we also considered continued advances in scientific research, quantum mechanics, and quantum computing which confirm that information in any digital or electronic medium is, and always has been, physical, tangible matter. Yet, to date, data regulation has sought to adapt legal constructs for “intangible” intellectual property or to express a series of permissions and constraints tied to specific classifications of data (such as personally identifiable information). We examined legal reforms that were recently approved by the United Nations Commission on International Trade Law to enable transactions involving electronic transferable records, as well as prior reforms adopted in the United States Uniform Commercial Code and Federal law to enable similar transactions involving digital records that were, historically, physical assets (such as promissory notes or chattel paper). Finally, we surveyed prior academic scholarship in the U.S. and Europe to determine if the physical attributes of digital data had been previously considered in the vigorous debates on how to regulate personal information or the extent, if at all, that the solutions developed for transferable records had been considered for larger classes of digital assets. Based on the preceding, we propose that regulation of digital information assets, and clear concepts of ownership, can be built on existing legal constructs that have enabled electronic commercial practices. We propose a property rules construct that clearly defines a right to own digital information arises upon creation (whether by keystroke or machine), and suggest when and how that right attaches to specific data though the exercise of technological controls. This construct will enable faster, better adaptations of new rules for the ever-evolving portfolio of data assets being created around the world. This approach will also create more predictable, scalable, and extensible mechanisms for regulating data and is consistent with, and may improve the exercise and enforcement of, rights regarding personal information. We conclude by highlighting existing technologies and their potential to support this construct and begin an inventory of the steps necessary to further proceed with this process

Revealing the Landscape of Privacy-Enhancing Technologies in the Context of Data Markets for the IoT: A Systematic Literature Review

IoT data markets in public and private institutions have become increasingly relevant in recent years because of their potential to improve data availability and unlock new business models. However, exchanging data in markets bears considerable challenges related to disclosing sensitive information. Despite considerable research focused on different aspects of privacy-enhancing data markets for the IoT, none of the solutions proposed so far seems to find a practical adoption. Thus, this study aims to organize the state-of-the-art solutions, analyze and scope the technologies that have been suggested in this context, and structure the remaining challenges to determine areas where future research is required. To accomplish this goal, we conducted a systematic literature review on privacy enhancement in data markets for the IoT, covering 50 publications dated up to July 2020, and provided updates with 24 publications dated up to May 2022. Our results indicate that most research in this area has emerged only recently, and no IoT data market architecture has established itself as canonical. Existing solutions frequently lack the required combination of anonymization and secure computation technologies. Furthermore, there is no consensus on the appropriate use of blockchain technology for IoT data markets and a low degree of leveraging existing libraries or reusing generic data market architectures. We also identified significant challenges remaining, such as the copy problem and the recursive enforcement problem that-while solutions have been suggested to some extent-are often not sufficiently addressed in proposed designs. We conclude that privacy-enhancing technologies need further improvements to positively impact data markets so that, ultimately, the value of data is preserved through data scarcity and users' privacy and businesses-critical information are protected.Comment: 49 pages, 17 figures, 11 table

Artificial Intelligence and Climate Change

As artificial intelligence (AI) continues to embed itself in our daily lives, many focus on the threats it poses to privacy, security, due process, and democracy itself. But beyond these legitimate concerns, AI promises to optimize activities, increase efficiency, and enhance the accuracy and efficacy of the many aspects of society relying on predictions and likelihoods. In short, its most promising applications may come, not from uses affecting civil liberties and the social fabric of our society, but from those particularly complex technical problems lying beyond our ready human capacity. Climate change is one such complex problem, requiring fundamental changes to our transportation, agricultural, building, and energy sectors. This Article argues for the enhanced use of AI to address climate change, using the energy sector to exemplify its potential promise and pitfalls. The Article then analyzes critical policy tradeoffs that may be associated with an increased use of AI and argues for its disciplined use in a way that minimizes its limitations while harnessing its benefits to reduce greenhouse-gas emissions

Artificial Intelligence and Climate Change

As artificial intelligence (AI) continues to embed itself in our daily lives, many focus on the threats it poses to privacy, security, due process, and democracy itself. But beyond these legitimate concerns, AI promises to optimize activities, increase efficiency, and enhance the accuracy and efficacy of the many aspects of society relying on predictions and likelihoods. In short, its most promising applications may come, not from uses affecting civil liberties and the social fabric of our society, but from those particularly complex technical problems lying beyond our ready human capacity. Climate change is one such complex problem, requiring fundamental changes to our transportation, agricultural, building, and energy sectors. This Article argues for the enhanced use of AI to address climate change, using the energy sector to exemplify its potential promise and pitfalls. The Article then analyzes critical policy tradeoffs that may be associated with an increased use of AI and argues for its disciplined use in a way that minimizes its limitations while harnessing its benefits to reduce greenhouse-gas emissions

The Transformative Integration of Artificial Intelligence with CMMC and NIST 800-171 For Advanced Risk Management and Compliance

This paper explores the transformative potential of integrating Artificial Intelligence (AI) with established cybersecurity frameworks such as the Cybersecurity Maturity Model Certification (CMMC) and the National Institute of Standards and Technology (NIST) Special Publication 800-171. The thesis argues that the relationship between AI and these frameworks has the capacity to transform risk management in cybersecurity, where it could serve as a critical element in threat mitigation. In addition to addressing AI’s capabilities, this paper acknowledges the risks and limitations of these systems, highlighting the need for extensive research and monitoring when relying on AI. One must understand boundaries when integrating AI into frameworks that ensure the security of sensitive data, otherwise, the ethicality of AI systems is compromised. This paper overviews compliance audits and their intricate relationship with cybersecurity frameworks CMMC and NIST 800-171, underscoring their complementary nature and shared objectives. Finally, the significance of AI in ensuring compliance with these frameworks will be explored, and the transformative potential of AI in automating processes and its advancements in risk management will be discussed