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Research data ethics are fundamental for U.S. federal research. Data ethics encompasses more than just ethics regarding participants in your study. Good data ethics dictates how researchers conduct their research, work with colleagues in the field, how they interact with communities such as Tribal Nations, and how they promote transparency in their research processes.
Data ethics can take many forms. Depending on your research project, you may not encounter all means of performing data ethics. However, being aware of all the aspects of data ethics is important to ensure accountability as a researcher. Good research practice is to adapt your approach to evolving norms and seek guidance from ethical organizations, funders, and your research community when navigating ethical dilemmas. |
The Federal Data Strategy's Data Ethics Framework is an essential document for any researcher who works with or receives government funding. This framework is meant to address the gap in U.S. policy in data ethics. As the United States is one of the largest data producers in the world, it is essential that researchers manage and create data from the population responsibly and ethically. The Data Ethics Framework's purpose is to guide federal leaders, employees, researchers, and data users on how to make ethical decisions when handling, creating, managing, and acquiring data. Researchers and data creators at universities, state DOTs, and local governments who receive federal funding are also bound to performing their research with good data ethics. This framework is relevant throughout the data lifecycle, from creation to publication. It is crucial that all who work with data and the federal government understand these principles, the benefits of good data ethics, what actions they need to take to perform good data ethics, and any gaps their processes currently have.
Protecting personally identifiable information (PII) is essential to performing good research data ethics. When receiving funding from U.S. DOT, all research must be anonymized before publication. Researchers, the government, libraries, and university Institutional Review Boards (IRB) must work together to ensure that research participants are protected and understand risks to their data when participating in research.
PII encompasses more than basic information such as name and demographics. It includes smaller, seemingly innocuous details that, when combined, can identify individuals. Establishing what data points need to be anonymized during the planning and acquiring data steps saves time down the road and ensures the safety of your participants.
PII can include but is not limited to:
While some of these data points are dangerous on their own, when combined with other data points, they can potentially expose your research participant. Ensuring that your research does not endanger your participants should be the utmost priority when working with humans and data.
Steps you can take to ensure that your data protects your participants includes:
As advanced technologies, such as artificial intelligence (AI), become integrated and relied on in the research process, it is important to uphold data ethics principles. Balancing the use of AI with data ethics is a tricky tightrope for researchers to navigate. However, there are important steps that a researcher should take when working with AI.
U.S. DOT Artificial Intelligence Activities
Supercharging Research: Harnessing Artificial Intelligence to Meet Global Challenges
More resources can be found on the Training and Resources page.
The CARE Principles are principles written by the Indigenous Peoples to outline and protect their data, it's use, and Indigenous interests. When working with Tribal communities, it is essential to incorporate the CARE Principles into every step of your research process. The Care Principles are as follows:
Governments and institutions must actively support the use and reuse of data by Indigenous nations and communities by facilitating the establishment of the foundations for Indigenous innovation, value generation, and the promotion of local self-determined development processes.
Data enrich the planning, implementation, and evaluation processes that support the service and policy needs of Indigenous communities. Data also enable better engagement between citizens, institutions, and governments to improve decision-making. Ethical use of open data has the capacity to improve transparency and decision-making by providing Indigenous nations and communities with a better understanding of their peoples, territories, and resources. It similarly can provide greater insight into third-party policies and programs affecting Indigenous Peoples.
Indigenous data are grounded in community values, which extend to society at large. Any value created from Indigenous data should benefit Indigenous communities in an equitable manner and contribute to Indigenous aspirations for wellbeing.
A: Authority to Control
A1: Recognizing rights and interests
Indigenous Peoples have rights and interests in both Indigenous Knowledge and Indigenous data. Indigenous Peoples have collective and individual rights to free, prior, and informed consent in the collection and use of such data, including the development of data policies and protocols for collection
Indigenous Peoples have the right to data that are relevant to their world views and empower self-determination and effective self-governance. Indigenous data must be made available and accessible to Indigenous nations and communities in order to support Indigenous governance.
A3: Governance of data
Indigenous Peoples have the right to develop cultural governance protocols for Indigenous data and be active leaders in the stewardship of, and access to, Indigenous data especially in the context of Indigenous Knowledge.
R: Responsibility
R1: For positive relationships
Indigenous data use is unviable unless linked to relationships built on respect, reciprocity, trust, and mutual understanding, as defined by the Indigenous Peoples to whom those data relate. Those working with Indigenous data are responsible for ensuring that the creation, interpretation, and use of those data uphold, or are respectful of, the dignity of Indigenous nations and communities.
R2: For expanding capability and capacity
Use of Indigenous data invokes a reciprocal responsibility to enhance data literacy within Indigenous communities and to support the development of an Indigenous data workforce and digital infrastructure to enable the creation, collection, management, security, governance, and application of data.
R3: For Indigenous languages and worldviews
Resources must be provided to generate data grounded in the languages, worldviews, and lived experiences (including values and principles) of Indigenous Peoples.
E: Ethics
E1: For minimizing harm and maximizing benefit
Ethical data are data that do not stigmatize or portray Indigenous Peoples, cultures, or knowledges in terms of deficit. Ethical data are collected and used in ways that align with Indigenous ethical frameworks and with rights affirmed in UNDRIP. Assessing ethical benefits and harms should be done from the perspective of the Indigenous Peoples, nations, or communities to whom the data relate.
E2: For justness
Ethical processes address imbalances in power, resources, and how these affect the expression of Indigenous rights and human rights. Ethical processes must include representation from relevant Indigenous communities.
E3: For future use
Data governance should take into account the potential future use and future harm based on ethical frameworks grounded in the values and principles of the relevant Indigenous community. Metadata should acknowledge the provenance and purpose and any limitations or obligations in secondary use inclusive of issues of consent.
While the FAIR Principles are foundational to practicing good research data management and open science, it is not enough to be just FAIR. Using both the FAIR Principles and the CARE Principles when conducting research with Tribal Nations is fundamental to good research data ethics. While open data is a foundational tenant of U.S. DOT research, purely open data does not take into consideration tribal history, knowledge, perspectives, and power differences that may take place between the community and the researcher. Acknowledging both of these sets of principles will keep data open, while also respecting indigenous communities and knowledge. To learn more about the CARE principles and their interaction with the FAIR principles, please consult CARE Principles for Indigenous Data Governance.
