Linking Societal Considerations and Integrating Public Values
EBRC roadmaps highlight a suite of technical ambitions and potential pathways for advancing the engineering of biology. They represent the initial outcome of a broad consultation of the scientific and engineering community to identify goals for the field and offer a vision of the technical possibilities and challenges of engineering biology in the service of broad social, ecological, and economic priorities. However, the material does not explicitly address the policy and social environments that new technologies and tools must navigate if they are to make a difference in the real world.
EBRC roadmaps exist on a yet-unspecified landscape of social priorities, cultural preferences, ethical minefields, political traditions, and economic realities. Interdisciplinary scholarship in fields complementary to science and engineering can be engaged in critically evaluating and framing the goals and aims of this technical roadmap. As the roadmap evolves, it will be important to question how engineering biology is utilized to address these global problems, and ways that novel technologies might create new problems or exacerbate existing social and political inequalities. To maximize the possibility of positive outcomes, technological pursuits will need to be coupled with ongoing study and negotiation of the social, cultural, political, and economic landscapes for which they are being designed. Doing this will require leveraging expertise in disciplines beyond science and engineering, including the arts, humanities, and social and behavioral sciences.
The challenges and achievements presented in EBRC’s roadmaps do not occur in a vacuum. Advancing engineering biology will require significant parallel, non-technical efforts to address societal, legal, ethical, economic, and ecological challenges.
Diversity in Scientists
Diversity within scientific disciplines, in all its forms (racial, gender, sexual identity, and more), strengthens research and helps advance science. Funding agencies have periodically made public efforts to support diversity in response to various current events, but they could better institutionalize practices to increase diversity, rather than enacting infrequent, reactionary policies. Funding agencies can play a key role in increasing diversity by providing a financial incentive for universities and labs to admit and support diverse trainees in the scientific workforce. Fully detailing the policy changes that could be implemented to increase diversity in science are beyond the scope of this roadmap, but a good starting point for any funding agency would be collecting more data from grant recipients and trainees supported by those grants. Grantee data on racial and gender diversity, academic outcomes of trainees, paper publication records, and community climate surveys are not adequate on their own, but can support future decision-making processes. It is essential that any data collected aims to capture the quality of training and mentorship, not simply the quantity of women or Black, Indigenous, and People of Color (BIPOC) scientists in a department or program. Additionally, collecting more information on scientists with disabilities will help universities understand how physical spaces may need to be redesigned (e.g., if laboratory spaces are not wheelchair friendly). Like in engineering biology, good measurements can aid in designing interventions and quickly understanding whether they are effective or not. Over time, these data should help identify the programs and policies that best support diversity, allowing an agency to increase investments even further.
Diversity in Data
Additionally, scientists and policymakers should also strive to ensure that diversity is considered in scientific data itself. Facial recognition systems have already demonstrated the pitfalls of incomplete data sets, with some programs misidentifying non-white faces up to 100 times more often than white faces.1Grother, P., Ngan, M., & Hanaoka, K. (2019). Face recognition vendor test part 3: Demographic effects (NIST IR 8280; p. NIST IR 8280). National Institute of Standards and Technology. View Publication As discussed throughout Engineering Biology and Microbiome Engineering, machine learning and artificial intelligence are essential for moving engineering biology forward. Regardless of whether a researcher is collecting genetic samples to study health, agricultural soil samples for enriching crop growth, or designing microbiomes to improve industrial production, they should make a conscious effort to avoid a similar problem that currently exists in other artificial intelligence systems. Mitigating biases, especially for health-related research, oftentimes requires extra effort and outreach to populations that are underserved by or distrustful of healthcare services, a reasonable sentiment in light of the long history of bias and discrimination in medicine.2Hoffman, K. M., Trawalter, S., Axt, J. R., & Oliver, M. N. (2016). Racial bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites. Proceedings of the National Academy of Sciences, 113(16), 4296–4301. View Publication,3Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. View Publication The example set by facial recognition software demonstrates the pitfalls of assuming that largely computational science does not have the same concerns about bias that are more readily apparent in medical or public health research. Human genomics research offers a potential model for engineering biology.4Guglielmi, G. (2019). Facing up to genome injustice. Nature, 568, 290–293. View Publication Researchers are making efforts to get community members directly involved in study design and implementation, which helps ensure that cultural traditions are respected and helps broaden community engagement as a result.
Throughout the process of experimental design, development, and implementation, it will be important to carefully consider how accidental biases in sample selection may impact engineering biology, and consciously control for them or expand the data set to mitigate them. The exact interventions necessary will look different for every experimental setup, but as engineering biology moves toward more human-applied technologies, researchers and funding agencies should constantly push for as much diversity in data as possible.
Equitable Access to Science
Another important consideration is ensuring equitable access to benefits of engineering biology. This roadmap builds from state-of-the-art research and technology, looking forward to new discoveries that will benefit society. An unfortunate reality is that many existing technologies could have substantial benefits for society, but barriers such as cost or lack-of-infrastructure prevent people from reaping those benefits. As engineering biology advances, the inevitable trickle-down of technology will make some technologies more accessible, but this will not be true in all cases. For example, healthcare costs seem to resist the normal market forces that would drive down prices when new drugs or therapies are invented. Even if all of the engineering advances presented in our roadmaps are achieved, significant policy changes will be necessary to imagine that new therapies and drugs from engineered biology will be accessible for all people. The burden and responsibility for changing these systems falls mainly on policymakers, but scientists should also advocate for changes, if they want their work to have as broad an impact as possible.
In addition to costs, medical professionals and policymakers should ensure that patients have informed access to new technologies. Patients should be made aware of every potential therapy available and the efficacy of the treatment should be clearly explained. This is one component of a complex network of structures and individual choices that contribute to systemic racial and ethnic health disparities,5Fiscella, K., & Sanders, M. R. (2016). Racial and Ethnic Disparities in the Quality of Health Care. Annual Review of Public Health, 37(1), 375–394. View Publication but medicines derived from engineered biology will require special attention for a healthcare provider to understand the treatment themselves, and then invest the time to explain it so their patient understands.
Public Education and Scientific Engagement
Community, stakeholder, and public engagement offers the chance to bridge the gap between technical experts and lay citizens in meaningful ways that go beyond one-way education.6Committee on Gene Drive Research in Non-Human Organisms: Recommendations for Responsible Conduct, Board on Life Sciences, Division on Earth and Life Studies, & National Academies of Sciences, Engineering, and Medicine. (2016). Gene Drives on the Horizon: Advancing Science, Navigating Uncertainty, and Aligning Research with Public Values (p. 23405). National Academies Press. View Publication While science education and improving scientific literacy do play a role, scientists also need to engage with their communities to hear their concerns and address them accordingly. The long struggle for public acceptance of genetically modified organisms is a good example that boosting scientific literacy is not sufficient to quell public opposition. Scientists would benefit from drawing on the insights of social science to help navigate the challenge in gaining greater public buy-in engineering biology. Funding agencies should go out of their way to encourage and support scientists engaging with all scientific stakeholders, because public trust will be critical for the acceptance of new tools and technologies from engineering biology.
- Grother, P., Ngan, M., & Hanaoka, K. (2019). Face recognition vendor test part 3: Demographic effects (NIST IR 8280; p. NIST IR 8280). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.IR.8280
- Hoffman, K. M., Trawalter, S., Axt, J. R., & Oliver, M. N. (2016). Racial bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites. Proceedings of the National Academy of Sciences, 113(16), 4296–4301. https://doi.org/10.1073/pnas.1516047113
- Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
- Guglielmi, G. (2019). Facing up to genome injustice. Nature, 568, 290–293. View Publication
- Fiscella, K., & Sanders, M. R. (2016). Racial and Ethnic Disparities in the Quality of Health Care. Annual Review of Public Health, 37(1), 375–394. https://doi.org/10.1146/annurev-publhealth-032315-021439
- Committee on Gene Drive Research in Non-Human Organisms: Recommendations for Responsible Conduct, Board on Life Sciences, Division on Earth and Life Studies, & National Academies of Sciences, Engineering, and Medicine. (2016). Gene Drives on the Horizon: Advancing Science, Navigating Uncertainty, and Aligning Research with Public Values (p. 23405). National Academies Press. https://doi.org/10.17226/23405