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At least 19 records

Including frameworks of public health ethics in computational modelling of infectious disease interventions

Decisions on public health interventions to control infectious diseases are often informed by computational models. Interpreting the predicted outcomes of a public health decision requires not only high-quality modelling but also an ethical framework for assessing the benefits and harms associated with different options. The design and specification of ethical frameworks matured independently of computational modelling, so many values recognized as important for ethical decision-making are missing from computational models. We demonstrate a proof-of-concept approach to incorporate multiple public health values into the evaluation of a simple computational model for vaccination against a pathogen such as SARS-CoV-2. By examining a bounded space of alternative prioritizations of three values relevant to public health ethics (aggregate clinical burden, equity in clinical burden, equity in adverse effects from vaccination), we identify value trade-offs, where the outcomes of optimal strategies differ depending on the ethical framework. This work demonstrates an approach to incorporating diverse values into decision criteria used to evaluate outcomes of models of infectious disease interventions.

"Mathematical Biology"↗

Ethical considerations in infectious disease modelling for public health policy: the case of school closures

Mathematical models of infectious diseases are frequently used as a tool to support public health policy and decisions around the implementation of interventions such as school closures. However, most publications on policy-relevant modelling lack an ethical framework and do not explicitly consider the ethical implications of the work. This creates a risk that the unintended consequences of interventions are overlooked or that models are used to justify decisions that are inconsistent with public health ethics. In this article, we focus on the case study of school closures as a commonly modelled intervention against pandemic influenza, COVID-19 and other infectious disease threats. We briefly review some of the key concepts in public health ethics and describe approaches to modelling the effects of school closures. We then identify a series of ethical considerations involved in modelling school closures. These include accounting for population heterogeneity and inequalities; including a diversity of viewpoints and expertise in model design; considering the distribution of benefits and harms; and model transparency and contextualization. Furthermore, we conclude with some recommendations to ensure that policy-relevant modelling is consistent with some key ethics values.

97 MATHEMATICS AND COMPUTING↗

Mitigating Adverse Effects of a Human Mission On Possible Martian Indigenous Ecosystems

Although human beings are, by most standards, the most capable agents to search for and detect extraterrestrial life, we are also potentially the most harmful. While there has been substantial work regarding forward contamination with respect to robotic missions, the issue of potential adverse effects on possible indigenous Martian ecosystems, such as biological contamination, due to a human mission has remained relatively unexplored and may require our attention now as this presentation will try to demonstrate by exploring some of the relevant scientific questions, mission planning challenges, and policy issues. An informal, high-level mission planning decision tree will be discussed and is included as the next page of this abstract. Some of the questions to be considered are: To what extent could contamination due to a human presence compromise possible indigenous life forms? To what extent can we control contamination? For example, will it be local or global? What are the criteria for assessing the biological status of Mars, both regionally and globally? For example, can we adequately extrapolate from a few strategic missions such as sample return missions? What should our policies be regarding our mission planning and possible interaction with what are likely to be microbial forms of extraterrestrial life? Central to the science and mission planning issues is the role and applicability of terrestrial analogs, such as Lake Vostok for assessing drilling issues, and modeling techniques. Central to many of the policy aspects are scientific value, international law, public concern, and ethics. Exploring this overall issue responsibly requires an examination of all these aspects and how they interrelate.

Lupisella, Mark L.↗

Mitigating Adverse Effects of a Human Mission on Possible Martian Indigenous Ecosystems

Although human beings are, by most standards, the most capable agents to search for and detect extraterrestrial life, we are also potentially the most harmful. While there has been substantial work regarding forward contamination with respect to robotic missions, the issue of potential adverse effects on possible indigenous Martian ecosystems, such as biological contamination, due to a human mission has remained relatively unexplored and may require our attention now as this presentation will try to demonstrate by exploring some of the relevant scientific questions, mission planning challenges, and policy issues. An informal, high-level mission planning decision tree will be discussed and is included as the next page of this abstract. Some of the questions to be considered are: (1) To what extent could contamination due to a human presence compromise possible indigenous life forms? (2) To what extent can we control contamination? For example, will it be local or global? (3) What are the criteria for assessing the biological status of Mars, both regionally and globally? For example, can we adequately extrapolate from a few strategic missions such as sample return missions? (4) What should our policies be regarding our mission planning and possible interaction with what are likely to be microbial forms of extraterrestrial life? (5) Central to the science and mission planning issues is the role and applicability of terrestrial analogs, such as Lake Vostok for assessing drilling issues, and modeling techniques. Central to many of the policy aspects are scientific value, international law, public concern, and ethics. Exploring this overall issue responsibly requires an examination of all these aspects and how they interrelate. A chart is included, titled 'Mission Planning Decision Tree for Mitigating Adverse Effects to Possible Indigenous Martian Ecosystems due to a Human Mission'. It outlines what questions scientists should ask and answer before sending humans to Mars.

Lupisella, M. L.↗

Clinical Trials at NASA: What Makes A Clinical Trial & What Are the Requirements for International Partners?

ClinicalTrials.gov is a public registry designed to fulfill ethical obligations by providing information about clinical research studies to the general public, patients, medical practitioners and the research community. In the United States, recent revisions to human subject’s regulations have prompted new criteria for what constitutes a clinical trial along with requirements not typically requested for other types of human subject’s research. Identification of investigational clinical trials is the shared responsibility of NASA, the IRB, and the scientific investigators designing and conducting the research. This talk aims to educate attendees on the history of the development of Clinicaltrials.gov, discussion of why these requirements are important to both participants and researchers, and information on how to identify clinical trials research. In addition, we will provide information on the different requirements for clinical trials conducted at NASA or aboard the International Space Station.

Clinicaltrials.gov↗

Astrobiology, Mars Exploration and Lassen Volcanic National Park

The search for evidence of life beyond Earth illustrates how the charters of NASA and the National Park Service share common ground. The mission of NPS is to preserve unimpaired the natural and cultural resources of the National Park System for the enjoyment, education and inspiration of this and future generations. NASA's Astrobiology program seeks to understand the origins, evolution and distribution of life in the universe, and it abides by the principles of planetary stewardship, public outreach, and education. We cannot subject planetary exploration destinations to Earthly biological contamination both for ethical reasons and to preserve their scientific value for astrobiology. We respond to the public's interest in the mysteries of life and the cosmos by honoring their desire to participate in the process of discovery. We involve youth in order to motivate career choices in science and technology and to perpetuate space exploration. The search for evidence of past life on Mars illustrates how the missions of NASA and NPS can become synergistic. Volcanic activity occurs on all rocky planets in our Solar System and beyond, and it frequently interacts with water to create hydrothermal systems. On Earth these systems are oases for microbial life. The Mars Exploration Rover Spirit has found evidence of extinct hydrothermal system in Gusev crater, Mars. Lassen Volcanic National Park provides a pristine laboratory for investigating how microorganisms can both thrive and leave evidence of their former presence in hydrothermal systems. NASA scientists, NPS interpretation personnel and teachers can collaborate on field-oriented programs that enhance Mars mission planning, engage students and the public in science and technology, and emphasize the ethics of responsible exploration.

Lassen Volcanic National Park↗

Evaluating Process Effectiveness to Reduce Risk

It is well documented that government agencies do not have the same incentive as the private sector to focus on process effectiveness and continual improvement of those processes. It is also well documented whenever government agencies fail to deliver efficient, effective, consistent, and fair services to the citizens. In spite of the various "reinventing government" and "effectiveness initiatives" of the past decades, and in spite of the efforts on the part of many agencies to improve, government in general still lags behind industry in creating a culture of effective processes and systems. While the tragic events that unfolded recently in Flint, Michigan, teach us that running government "like a business" does not always take the needs of the citizenry into account, there are many lessons and techniques from the private sector that government agencies can use to improve. The incentive to improve, while mandated by various administrations1, needs to come from within the workforce, in order to effectively take root. The best, most effective incentive is to reduce, control or eliminate risk. Government agencies face some of the same risks as the private sector, while some are unique. While ISO 310002 has been around since 2009, risk has taken on increased visibility within the private sector with the advent of the emphasis on risk-based thinking in ISO 9001:20153. The relationship between risk-based thinking and effective processes is simple and direct. Those processes that are well thought out and standardized (i.e. Plan-Do-Check-Act), will have taken into account the applicable policy, statutory, regulatory, safety, quality and technical parameters, which may not occur to someone performing the process with minimal experience or training; and thus protect the employees, the public and the agency from statutory and regulatory violations; delay in providing services; non-delivery of services; harm to public or employee safety and health; cost overruns; breaches in security; loss of confidence in government; failure of publicly funded projects; damage to the environment; ethics violations, and the list goes on; with local, national and even international consequences. The Plan-Do-Check-Act process, also known as the "process approach" can be used at any time to establish and standardize a process, and it can also be used to check periodically for "process creep" (i.e., informal, unauthorized changes that have occurred over time), any necessary updates and improvements. While ISO 9001 compliance is not mandated for all government agencies, if interpreted correctly, it can be useful in establishing a framework and implementing effective management systems and processes.4 Another method that can be used to evaluate effectiveness is the scorecard definitions in Mallory's Process Management Standard5 as a basis for evaluating work on the process level on effective, and continuously improved and improving processes. With processes on the lower end of the scale, agencies are vulnerable to a great many risks, with employees and managers making up many of the rules as they go, leading to the above listed negative results. Without clear guidance for nominal operations, off-nominal situations can, and do, increase the likelihood of chaos. In an increasingly technical environment, with inter-agency communication and collaboration becoming the norm, agencies need to come to grips with the fact that processes can become rapidly outdated, and that the technical community should take on an increased role in the maturation of the agency's processes. Industry has long known that effective processes are also efficient, and process improvement methods such as Kaizen, Lean, Six Sigma, 5S, and mistake proofing lead to increased productivity, improved quality, and decreased cost. Again, government agencies have different concerns, but inefficiencies and mistakes can have dire and wide reaching consequences for the public that they serve. While no one goes to work planning to cause harm, it is up to agencies to establish upper level systems, which make establishment and compliance with processes possible. Again, Mallory provides us with a Systems Management Standard6, similar to the Process Management Standard, with a scale of 0-5 for systems effectiveness and maturity. Deming determined that "eighty-five percent of the reasons for failure are deficiencies in the systems and process rather than the employee. The role of management is to change the process rather than badgering individual employees to do better." 7 It is not just the working level employees who need effective processes, but the mid-and upper level managers as well. A disciplined management culture sets the tone for the employees, aids both routine and off-nominal decision-making, and incorporates risk -based thinking into the systems and processes as a matter of normal activity. Figure 1, illustrates the relationship between ineffective and effective processes and risk, through the use of the "stoplight" colors that are commonly used to show serious situations (red), situations which may be improving or deteriorating depending on trends (yellow), and situations that are under control and continuously improved (green).

Shepherd, Christena C.↗

Standards of conduct for NASA employees

'Standards of Conduct' for employees (14 CFR Part 1207) is set forth in this handbook and is hereby incorporated in the NASA Directives System. This handbook incorporates, for the convenience of NASA employees, the regulations now in effect prescribing standards of conduct for NASA employees. These regulations set forth the high ethical standards of conduct required of NASA employees in carrying out their duties and responsibilities. These regulations have been approved by the Office of Government Ethics, Office of Personnel Management. The regulations incorporated in this handbook were first published in the Federal Register on October 21, 1967 (32 FR 14648-14659); Part B concerning the acceptance of gifts, gratuities, or entertainment was extensively revised on January 19, 1976 (41 FR 2631-2633) to clarify and generally to restrict the exceptions to the general rule against the acceptance by a NASA employee from persons or firms doing or seeking business with NASA. Those regulations were updated on January 29, 1985 (50 FR 3887) to ensure conformity to the Ethics in Government Act of 1978 regarding the public financial disclosure statement. These regulations were published in the Federal Register on June 16, 1987 (52 FR 22755-764) and a correction was printed on Sept. 28, 1987 (52 FR 36234).

John E O'Brien↗

AGU Publications Updates Authorship Policy to Foster Greater Equity and Transparency in Global Research Collaborations

AGU Publications encourages research collaborations between regions, countries, and communities. When well-resourced researchers complete research or field work in low-resourced settings while excluding local communities or researchers from the process, this can be referred to as parachute science or helicopter research. To help address concerns of parachute science and to promote greater equity and transparency in global research collaborations, AGU Publications has updated its authorship policy across its scholarly journals. The implementation of this policy follows a successful 18-month pilot at JGR: Biogeosciences. For research completed in low-resourced regions, authors are encouraged to include a disclosure statement pertaining to the ethical and scientific considerations of their research collaborations.

99 GENERAL AND MISCELLANEOUS↗

Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review

Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.

AI↗

Florida Atlantic Coast Telemetry (FACT) Array: A Working Partnership

The Florida Atlantic Coast Telemetry (FACT) Array is a collaborative partnership of researchers from 24 different organizations using passive acoustic telemetry to document site fidelity, habitat preferences, seasonal migration patterns, and reproductive strategies of valuable sportfish, sharks, and marine turtles. FACT partners have found that by bundling resources, they can leverage a smaller investment to track highly mobile animals beyond a study area typically restrained in scale by funds and manpower. FACT is guided by several simple rules: use of the same type of equipment, locate receivers in areas that are beneficial to all researchers when feasible, maintain strong scientific ethics by recognizing that detection data on any receiver belongs to the tag owner, do not use other members detection data without permission and acknowledge FACT in publications. Partners have access to a network of 480 receivers deployed along a continuum of habitats from freshwater rivers to offshore reefs and covers 1100 km of coastline from the Dry Tortugas, Florida to South Carolina and extends to the Bahamas. Presently, 49 species, (25 covered by Fisheries Management Plans and five covered by the Endangered Species Act) have been tagged with 2736 tags in which 1767 tags are still active.

Scheidt, Douglas↗

Software engineering ethics

Software engineering ethics is reviewed. The following subject areas are covered: lack of a system viewpoint; arrogance of PC DOS software vendors; violation od upward compatibility; internet worm; internet worm revisited; student cheating and company hiring interviews; computing practitioners and the commodity market; new projects and old programming languages; schedule and budget; and recent public domain comments.

Bown, Rodney L.↗

Application of Artificial Intelligence/Machine Learning to Operations Research

This report examines the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on operations research, private industry, and government sectors, highlighting their applications in automating processes, enhancing decision-making, and optimizing complex systems. AI/ML technologies have revolutionized industries through predictive maintenance, supply chain optimization, and autonomous systems, while also advancing public safety and defense operations. However, challenges such as data integrity, model transparency, and the need for human oversight persist, particularly in high-consequence environments. The report emphasizes the critical role of explainable AI (XAI) and human-computer interaction models like Human-in-the-Loop (HITL) and Human-on-the-Loop (HOTL) in fostering trust and accountability. Balancing automation with ethical responsibility and transparency is essential for the continued successful integration of AI/ML into operational and strategic decision-making frameworks.

97 MATHEMATICS AND COMPUTING↗

NASA ESDS Citizen Science Data Working Group

This document provides guidelines for legal, policy, and ethical issues; standards for citizen science data collection and management; information on ensuring usability of citizen science data and communication regarding its use; and best practices for long-term archival of citizen science data.Section1 contains a detailed discussion of policy, ethical, and legal considerations influencing citizen science data collection. Section 2 considers standards for documentation, including documentation of instrumentation, procedures, and the data itself. It concludes with a discussion of how citizen science data should be attributed. Section 3 provides guidance about how to ensure citizen science data are collected and stored in a useable way. It also considers how NASA and data producers should notify the scientific community, including citizen scientists and the public, about citizen science datasets and the scientific conclusions reached using them. Finally, Section 4 provides detailed information regarding what should be archived from projects using a citizen science approach, including data and code. It provides guidance about archive location, process, and timeframe, as well as information about data access and distribution services provided by NASA that may be relevant to data producers working with citizen scientists.

Citizen Science↗

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗

The Artificial Intelligence Ontology: LLM-Assisted Construction of AI Concept Hierarchies

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. We use the term “branches” for classes, and their subclasses, in our ontology that are subclasses of owl:Thing. AIO contains eight branches: Bias, Layer, Machine Learning Task, Mathematical Function, Model, Network, Preprocessing, and Training Strategy, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO uses the Ontology Development Kit (ODK) for its creation and maintenance, with its content being more easily updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub ( https://w3id.org/aio/ ) and BioPortal ( https://bioportal.bioontology.org/ontologies/AIO ).

Joachimiak, Marcin P. [Biosystems Data Science Dep↗