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Cultivating Cultures for Ethical STEM (CCE STEM) funds research projects that identify (1) factors that are effective in the formation of ethical STEM researchers and (2) approaches to developing those factors in all the fields of science and engineering that NSF supports. CCE STEM solicits proposals for research that explores the following: 'What constitutes responsible conduct for research (RCR), and which cultural and institutional contexts promote ethical STEM research and practice and why?' Factors one might consider include: honor codes, professional ethics codes and licensing requirements, an ethic of service and/or service learning, life-long learning requirements, curricula or memberships in organizations (e.g. Engineers without Borders) that stress responsible conduct for research, institutions that serve under-represented groups, institutions where academic and research integrity are cultivated at multiple levels, institutions that cultivate ethics across the curriculum, or programs that promote group work, or do not grade. Do certain labs have a 'culture of academic integrity'? What practices contribute to the establishment and maintenance of ethical cultures and how can these practices be transferred, extended to, and integrated into other research and learning settings?

The Ethics Education in Science and Engineering (EESE) program funds research and educational projects that improve ethics education in all fields of science and engineering that NSF supports, with priority consideration given to interdisciplinary, inter-institutional, and international contexts.  Although the primary focus is on improving ethics education for graduate students in NSF-funded fields, the proposed programs may benefit advanced undergraduates as well.

The Smart and Autonomous Systems (S&AS) program focuses on Intelligent Physical Systems (IPS) that are cognizant, taskable, reflective, ethical, and knowledge-rich. The S&AS program welcomes research on IPS that are aware of their capabilities and limitations, leading to long-term autonomy requiring minimal or no human operator intervention. Example IPS include, but are not limited to, robotic platforms and networked systems that combine computing, sensing, communication, and actuation. Cognizant IPS exhibit high-level awareness beyond primitive actions, in support of persistent and long-term autonomy. Taskable IPS can interpret high-level, possibly vague, instructions, translating them into concrete actions that are dependent on the particular context in which the IPS is operating. Reflective IPS can learn from their own experiences and those of other entities, such as other IPS or humans, and from instruction or observation; they may exhibit self-aware and self-optimizing capabilities. Ethical IPS should adhere to a system of societal and legal rules, taking those rules into account when making decisions. Knowledge-rich IPS employ a variety of representation and reasoning mechanisms, such as semantic, probabilistic and commonsense reasoning; are cognitively plausible; reason about uncertainty in decision making; and reason about the intentions of other entities in decision making.

The Smart and Autonomous Systems (S&AS) program focuses on Intelligent Physical Systems (IPS) that are cognizant, taskable, reflective, ethical, and knowledge-rich. The S&AS program welcomes research on IPS that are aware of their capabilities and limitations, leading to long-term autonomy requiring minimal or no human operator intervention. Example IPS include, but are not limited to, robotic platforms and networked systems that combine computing, sensing, communication, and actuation. Cognizant IPS exhibit high-level awareness beyond primitive actions, in support of persistent and long-term autonomy. Taskable IPS can interpret high-level, possibly vague, instructions, translating them into concrete actions that are dependent on the particular context in which the IPS is operating. Reflective IPS can learn from their own experiences and those of other entities, such as other IPS or humans, and from instruction or observation; they may exhibit self-aware and self-optimizing capabilities. Ethical IPS should adhere to a system of societal and legal rules, taking those rules into account when making decisions. Knowledge-rich IPS employ a variety of representation and reasoning mechanisms, such as semantic, probabilistic and commonsense reasoning; are cognitively plausible; reason about uncertainty in decision making; and reason about the intentions of other entities in decision making.

Markets for Good is launching two new grant programs focused on the role of digital data and infrastructure to improve decision-making in philanthropy (particularly individual giving) and in the social sector writ large. Grants will be awarded for two types of projects: scholarly research and practical innovations. We aim to support research, prototypes, and shared learning that can help donors and social sector organizations use digital data safely, ethically, and effectively to improve their work.

Markets for Good is launching two new grant programs focused on the role of digital data and infrastructure to improve decision-making in philanthropy (particularly individual giving) and in the social sector writ large. Grants will be awarded for two types of projects: scholarly research and practical innovations. We aim to support research, prototypes, and shared learning that can help donors and social sector organizations use digital data safely, ethically, and effectively to improve their work.

The NSF Engineering (ENG) Directorate is launching a multi-year initiative, the Professional Formation of Engineers, to create and support an innovative and inclusive engineering profession for the 21st Century.  Professional Formation of Engineers (PFE) refers to the formal and informal processes and value systems by which people become engineers.  It also includes the ethical responsibility of practicing engineers to sustain and grow the profession.  The engineering profession must be responsive to national priorities, grand challenges, and dynamic workforce needs; it must be equally open and accessible to all.

Increasingly, undergraduate computer science (CS) programs are being called upon to prepare larger and more diverse student populations for careers in both CS and non-CS fields, including careers in scientific and non-scientific disciplines. Many of these students aim to acquire the understandings and competencies needed to learn how to  use computation collaboratively across different contexts and challenging problems. However, standard CS course sequences do not always serve these students well. With this solicitation, NSF will support teams of Institutions of Higher Education (IHEs) in re-envisioning the role of computing in interdisciplinary collaboration within their institutions. In addition, NSF will encourage partnering IHEs to use this opportunity to integrate the study of ethics into their curricula, both within core CS courses and across the relevant interdisciplinary application areas.

NSF's Harnessing the Data Revolution (HDR) Big Idea is a national-scale activity to enable new modes of data-driven discovery that will allow fundamental questions to be asked and answered at the frontiers of science and engineering. Through this NSF-wide activity, HDR will generate new knowledge and understanding, and accelerate discovery and innovation. The HDR vision is realized through an interrelated set of efforts in: Foundations of data science; Algorithms and systems for data science; Data-intensive science and engineering; Data cyberinfrastructure; and Education and workforce development. Each of these efforts is designed to amplify the intrinsically multidisciplinary nature of the emerging field of data science. The HDR Big Idea will establish theoretical, technical, and ethical frameworks that will be applied to tackle data-intensive problems in science and engineering, contributing to data-driven decision-making that impacts society.

The program supports four main research thrusts that are envisioned to advance the goal of ubiquitous co-robots: scalability, customizability, lowering barriers to entry, and societal impact. Topics addressing scalability include how robots can collaborate effectively with multiple humans or other robots; how robots can perceive, plan, act, and learn in uncertain, real-world environments, especially in a distributed fashion; and how to facilitate large-scale, safe, robust and reliable operation of robots in complex environments. Customizability includes how to enable co-robots to adapt to specific tasks, environments, or people, with minimal modification to hardware and software; how robots can personalize their interactions with people; and how robots can communicate naturally with humans, both verbally and non-verbally. Topics in lowering barriers to entry should focus on lowering the barriers for conducting fundamental robotics research and research on integrated robotics application. This may include development of open-source co-robot hardware and software, as well as widely-accessible testbeds. Outreach or using robots in educational programs do not, by themselves, lower the barriers to entry for robotics research. Topics in societal impact include fundamental research to establish and infuse robotics into educational curricula, advance the robotics workforce through education pathways, and explore the social, economic, ethical, and legal implications of our future with ubiquitous collaborative robots.

Digital Impact (formerly Markets for Good) is an initiative of the Digital Civil Society Lab at the Stanford Center on Philanthropy and Civil Society. We work to improve the digital infrastructure for civil society and help social sector practitioners and policymakers use digital resources safely, ethically, and effectively for maximum impact.

The program supports four main research thrusts that are envisioned to advance the goal of ubiquitous co-robots: scalability, customizability, lowering barriers to entry, and societal impact. Topics addressing scalability include how robots can collaborate effectively with multiple humans or other robots; how robots can perceive, plan, act, and learn in uncertain, real-world environments, especially in a distributed fashion; and how to facilitate large-scale, safe, robust and reliable operation of robots in complex environments. Customizability includes how to enable co-robots to adapt to specific tasks, environments, or people, with minimal modification to hardware and software; how robots can personalize their interactions with people; and how robots can communicate naturally with humans, both verbally and non-verbally. Topics in lowering barriers to entry include development of open-source co-robot hardware and software, as well as widely-accessible testbeds. Topics in societal impact include fundamental research to establish and infuse robotics into educational curricula, advance the robotics workforce through education pathways, and explore the social, economic, ethical, and legal implications of our future with ubiquitous collaborative robots.

The program supports four main research themes that are envisioned to advance the goal of ubiquitous co-robots:scalability,customizability,lowering barriers to entry, andsocietal impact,includinghuman safety. Topics addressingscalabilityinclude how robots can collaborate effectively with orders of magnitude more humans or other robots than is handled by the current state of the art; how robots can perceive, plan, act, and learn in uncertain, real-world environments, especially in a distributed fashion; and how to facilitate large-scale, safe, robust and reliable operation of robots in complex environments.Customizabilityincludes how to enable co-robots to adapt to specific different tasks, environments, or people, with minimal modification to hardware and software; how robots can personalize their interactions with people; and how robots can communicate naturally with humans, both verbally and non-verbally. Topics inlowering barriers to entryshould focus on lowering the barriers for conducting fundamental roboticsresearchand research on integrated robotics application. This may include development of open-source co-robot hardware and software, as well as widely-accessible testbeds. Outreach or using robots in educational programs do not, by themselves, lower the barriers to entry for robotics research. Topics insocietal impactinclude fundamental research to establish and infuse robotics into educational curricula, advance the robotics workforce through education pathways, and explore the social, economic, ethical, security, and legal implications of our future with ubiquitous collaborative robots.

The Law & Science Program considers proposals that address social scientific studies of law and law-like systems of rules, as well as studies of how science and technology are applied in legal contexts. The Program is inherently interdisciplinary and multi-methodological. Successful proposals describe research that advances scientific theory and understanding of the connections between human behavior and law, legal institutions, or legal processes; or the interactions of law and basic sciences, including biology, computer and information sciences, STEM education, engineering, geosciences, and math and physical sciences. Scientific studies of law often approach law as dynamic, interacting with multiple arenas, and with the participation of multiple actors. Fields of study include many disciplines, and often address problems including, though not limited, to: Crime, Violence, and Policing Cyberspace Economic Issues Environmental Science Evidentiary Issues Forensic Science Governance and Courts Human Rights and Comparative Law Information Technology Legal and Ethical Issues related to Science Legal Decision Making Legal Mobilization and Conceptions of Justice Litigation and the Legal Profession Punishment and Corrections Regulation and Facilitation of Biotechnology (e.g., Gene Editing, Gene Testing, Synthetic Biology) and Other Emerging Sciences and Technologies Use of Science in the Legal Processes