Matching Items (6)
Description
Several prominent research strategy organizations recommend applying life cycle assessment (LCA) early in the development of emerging technologies. For example, the US Environmental Protection Agency, the National Research Council, the Department of Energy, and the National Nanotechnology Initiative identify the potential for LCA to inform research and development (R&D) of photovoltaics and products containing engineered nanomaterials (ENMs). In this capacity, application of LCA to emerging technologies may contribute to the growing movement for responsible research and innovation (RRI). However, existing LCA practices are largely retrospective and ill-suited to support the objectives of RRI. For example, barriers related to data availability, rapid technology change, and isolation of environmental from technical research inhibit application of LCA to developing technologies. This dissertation focuses on development of anticipatory LCA tools that incorporate elements of technology forecasting, provide robust explorations of uncertainty, and engage diverse innovation actors in overcoming retrospective approaches to environmental assessment and improvement of emerging technologies. Chapter one contextualizes current LCA practices within the growing literature articulating RRI and identifies the optimal place in the stage gate innovation model to apply LCA. Chapter one concludes with a call to develop anticipatory LCA – building on the theory of anticipatory governance – as a series of methodological improvements that seek to align LCA practices with the objectives of RRI.
Chapter two provides a framework for anticipatory LCA, identifies where research from multiple disciplines informs LCA practice, and builds off the recommendations presented in the preceding chapter. Chapter two focuses on crystalline and thin film photovoltaics (PV) to illustrate the novel framework, in part because PV is an environmentally motivated technology undergoing extensive R&D efforts and rapid increases in scale of deployment. The chapter concludes with a series of research recommendations that seek to direct PV research agenda towards pathways with the greatest potential for environmental improvement.
Similar to PV, engineered nanomaterials (ENMs) are an emerging technology with numerous potential applications, are the subject of active R&D efforts, and are characterized by high uncertainty regarding potential environmental implications. Chapter three introduces a Monte Carlo impact assessment tool based on the toxicity impact assessment model USEtox and demonstrates stochastic characterization factor (CF) development to prioritize risk research with the greatest potential to improve certainty in CFs. The case study explores a hypothetical decision in which personal care product developers are interested in replacing the conventional antioxidant niacinamide with the novel ENM C60, but face high data uncertainty, are unsure regarding potential ecotoxicity impacts associated with this substitution, and do not know what future risk-relevant experiments to invest in that most efficiently improve certainty in the comparison. Results suggest experiments that elucidate C60 partitioning to suspended solids should be prioritized over parameters with little influence on results. This dissertation demonstrates a novel anticipatory approach to exploration of uncertainty in environmental models that can create new, actionable knowledge with potential to guide future research and development decisions.
Chapter two provides a framework for anticipatory LCA, identifies where research from multiple disciplines informs LCA practice, and builds off the recommendations presented in the preceding chapter. Chapter two focuses on crystalline and thin film photovoltaics (PV) to illustrate the novel framework, in part because PV is an environmentally motivated technology undergoing extensive R&D efforts and rapid increases in scale of deployment. The chapter concludes with a series of research recommendations that seek to direct PV research agenda towards pathways with the greatest potential for environmental improvement.
Similar to PV, engineered nanomaterials (ENMs) are an emerging technology with numerous potential applications, are the subject of active R&D efforts, and are characterized by high uncertainty regarding potential environmental implications. Chapter three introduces a Monte Carlo impact assessment tool based on the toxicity impact assessment model USEtox and demonstrates stochastic characterization factor (CF) development to prioritize risk research with the greatest potential to improve certainty in CFs. The case study explores a hypothetical decision in which personal care product developers are interested in replacing the conventional antioxidant niacinamide with the novel ENM C60, but face high data uncertainty, are unsure regarding potential ecotoxicity impacts associated with this substitution, and do not know what future risk-relevant experiments to invest in that most efficiently improve certainty in the comparison. Results suggest experiments that elucidate C60 partitioning to suspended solids should be prioritized over parameters with little influence on results. This dissertation demonstrates a novel anticipatory approach to exploration of uncertainty in environmental models that can create new, actionable knowledge with potential to guide future research and development decisions.
ContributorsWender, Ben A. (Author) / Seager, Thomas (Thesis advisor) / Guston, David (Committee member) / Westerhoff, Paul (Committee member) / Arizona State University (Publisher)
Created2016
Description
Three dilemmas plague governance of scientific research and technological
innovation: the dilemma of orientation, the dilemma of legitimacy, and the dilemma of control. The dilemma of orientation risks innovation heedless of long-term implications. The dilemma of legitimacy grapples with delegation of authority in democracies, often at the expense of broader public interest. The dilemma of control poses that the undesirable implications of new technologies are hard to grasp, yet once grasped, all too difficult to remedy. That humanity has innovated itself into the sustainability crisis is a prime manifestation of these dilemmas.
Responsible innovation (RI), with foci on anticipation, inclusion, reflection, coordination, and adaptation, aims to mitigate dilemmas of orientation, legitimacy, and control. The aspiration of RI is to bend the processes of technology development toward more just, sustainable, and societally desirable outcomes. Despite the potential for fruitful interaction across RI’s constitutive domains—sustainability science and social studies of science and technology—most sustainability scientists under-theorize the sociopolitical dimensions of technological systems and most science and technology scholars hesitate to take a normative, solutions-oriented stance. Efforts to advance RI, although notable, entail one-off projects that do not lend themselves to comparative analysis for learning.
In this dissertation, I offer an intervention research framework to aid systematic study of intentional programs of change to advance responsible innovation. Two empirical studies demonstrate the framework in application. An evaluation of Science Outside the Lab presents a program to help early-career scientists and engineers understand the complexities of science policy. An evaluation of a Community Engagement Workshop presents a program to help engineers better look beyond technology, listen to and learn from people, and empower communities. Each program is efficacious in helping scientists and engineers more thoughtfully engage with mediators of science and technology governance dilemmas: Science Outside the Lab in revealing the dilemmas of orientation and legitimacy; Community Engagement Workshop in offering reflexive and inclusive approaches to control. As part of a larger intervention research portfolio, these and other projects hold promise for aiding governance of science and technology through responsible innovation.
ContributorsBernstein, Michael J. (Author) / Wiek, Arnim (Thesis advisor) / Wetmore, Jameson M. (Thesis advisor) / Grimm, Nancy (Committee member) / Anderies, John M (Committee member) / Arizona State University (Publisher)
Created2016
Description
While artificial intelligence (AI) has seen enormous technical progress in recent years, less progress has occurred in understanding the governance issues raised by AI. In this dissertation, I make four contributions to the study and practice of AI governance. First, I connect AI to the literature and practices of responsible research and innovation (RRI) and explore their applicability to AI governance. I focus in particular on AI’s status as a general purpose technology (GPT), and suggest some of the distinctive challenges for RRI in this context such as the critical importance of publication norms in AI and the need for coordination. Second, I provide an assessment of existing AI governance efforts from an RRI perspective, synthesizing for the first time a wide range of literatures on AI governance and highlighting several limitations of extant efforts. This assessment helps identify areas for methodological exploration. Third, I explore, through several short case studies, the value of three different RRI-inspired methods for making AI governance more anticipatory and reflexive: expert elicitation, scenario planning, and formal modeling. In each case, I explain why these particular methods were deployed, what they
produced, and what lessons can be learned for improving the governance of AI in the future. I find that RRI-inspired methods have substantial potential in the context of AI, and early utility to the GPT-oriented perspective on what RRI in AI entails. Finally, I describe several areas for future work that would put RRI in AI on a sounder footing.
produced, and what lessons can be learned for improving the governance of AI in the future. I find that RRI-inspired methods have substantial potential in the context of AI, and early utility to the GPT-oriented perspective on what RRI in AI entails. Finally, I describe several areas for future work that would put RRI in AI on a sounder footing.
ContributorsBrundage, Miles, Ph.D (Author) / Guston, David (Thesis advisor) / Keeler, Lauren (Committee member) / Fisher, Erik (Committee member) / Bryson, Joanna (Committee member) / Arizona State University (Publisher)
Created2019
Description
Research confirms that climate change is primarily due to the influx of greenhouse gases from the anthropogenic burning of fossil fuels for energy. Carbon dioxide (CO2) is the dominant greenhouse gas contributing to climate change. Although research also confirms that negative emission technologies (NETs) are necessary to stay within 1.5-2°C of global warming, this dissertation proposes that the climate change problem has been ineffectively communicated to suggest that CO2 emissions reduction is the only solution to climate change. Chapter 1 explains that current United States (US) policies focus heavily on reducing CO2 emissions, but ignore the concentrations of previous CO2 emissions accumulating in the atmosphere. Through political, technological, and ethical lenses, this dissertation evaluates whether the management process of CO2 emissions and concentrations in the US today can effectively combat climate change.
Chapter 2 discusses the historical management of US air pollution, why CO2 is regulated as an air pollutant, and how the current political framing of climate change as an air pollution problem promotes the use of market-based solutions to reduce emissions but ignores CO2 concentrations. Chapter 3 argues for the need to reframe climate change solutions to include reducing CO2 concentrations along with emissions. It presents the scientific reasoning and technological needs for reducing CO2 concentrations, why direct air capture (DAC) is the most effective NET to do so, and existing regulatory systems that can inform future CO2 removal policy. Chapter 4 explores whether Responsible Innovation (RI), a framework that includes society in the innovation process of emerging technologies, is effective for the ethical research and deployment of DAC; reveals the need for increased DAC governance strategies, and suggests how RI can be expanded to allow continued research of controversial emerging technologies in case of a climate change emergency. Overall, this dissertation argues that climate change must be reframed as a two-part problem: preventing new CO2 emissions and reducing concentrations, which demands increased investment in DAC research, development, and deployment. However, without a national or global governance strategy for DAC, it will remain difficult to include CO2 concentration reduction as an essential piece to the climate change solution.
Chapter 2 discusses the historical management of US air pollution, why CO2 is regulated as an air pollutant, and how the current political framing of climate change as an air pollution problem promotes the use of market-based solutions to reduce emissions but ignores CO2 concentrations. Chapter 3 argues for the need to reframe climate change solutions to include reducing CO2 concentrations along with emissions. It presents the scientific reasoning and technological needs for reducing CO2 concentrations, why direct air capture (DAC) is the most effective NET to do so, and existing regulatory systems that can inform future CO2 removal policy. Chapter 4 explores whether Responsible Innovation (RI), a framework that includes society in the innovation process of emerging technologies, is effective for the ethical research and deployment of DAC; reveals the need for increased DAC governance strategies, and suggests how RI can be expanded to allow continued research of controversial emerging technologies in case of a climate change emergency. Overall, this dissertation argues that climate change must be reframed as a two-part problem: preventing new CO2 emissions and reducing concentrations, which demands increased investment in DAC research, development, and deployment. However, without a national or global governance strategy for DAC, it will remain difficult to include CO2 concentration reduction as an essential piece to the climate change solution.
ContributorsMorton, Evvan (Author) / Lackner, Klaus S (Thesis advisor) / Allenby, Braden R. (Committee member) / Graffy, Elisabeth A. (Committee member) / Arizona State University (Publisher)
Created2020
Description
This paper outlines a responsible innovation framework to evaluate technologies designed for education. Traditionally, technologies being implemented for development of education come from foreign nations with less cultural understanding of the needs of the country they are trying to serve. This framework outlines categories that impact the success or failure of an educational technology. The framework is explained and then applied to the SolarSPELL case; an offline digital library designed to bring information to resource constrained areas around the world. The purpose of this research is to explore the factors determining success and failure of educational technology projects and design a framework that can be used moving forward to assess projects prior to the final implementation stage to encourage more successful projects. The framework designed in this research proved useful for evaluating educational technology designed for resource constrained areas.
ContributorsArnold, Madison (Author) / Parmentier, Mary Jane (Thesis director) / Hosman, Laura (Committee member) / School for the Future of Innovation in Society (Contributor) / Walter Cronkite School of Journalism & Mass Comm (Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
Description
ABSTRACTThis dissertation addresses two pivotal challenges within the US technology industry: racial equity and the rise of artificial intelligence (AI). It investigates whether the integration of AI in human resources (HR) can foster inclusivity and diversity for Black women in the tech workforce. Despite numerous diversity initiatives, Black women account for less than 2% of the US tech workforce, symbolizing a persistent challenge. Furthermore, AI often perpetuates structural biases, magnifying workforce inequities. This dissertation employs intersectionality, responsible innovation, and algorithmic bias theories to amplify the voices of Black women. It poses three critical questions: 1) How have Black women's HR experiences influenced diversity issues in the tech industry? 2) How is AI in HR developed considering the experiences of Black women? 3) What measures can enhance the role of AI in HR to promote diversity without deepening inequalities? Key findings reveal that current HR practices do not adequately serve Black women, driven by competing corporate priorities. Solutions should concentrate on recruiting, developing, promoting, and retaining Black women. Black women acknowledge the potential of AI to either reinforce or mitigate biases, yet they express apprehension about the development and implementation of AI in HR, which often lacks Black women's input. For AI to facilitate positive diversity results, companies must actively involve Black women in its development. This entails understanding the problems Black women face, using insights to design AI that addresses these issues and supports Black women's success, and engaging Black women in the development and assessment of AI implementations in HR, thereby enhancing accountability for diversity outcomes.
ContributorsWhye, Barbara Hickman (Author) / Miller, Clark (Thesis advisor) / Richter, Jennifer (Committee member) / Scott, Kimberly (Committee member) / Arizona State University (Publisher)
Created2023