References

Buolamwini, J., and T. Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research 81:1–15.

Cohen, I. G., and M. M. Mello. 2019. Big data, big tech, and protecting patient privacy. JAMA 322(12):1141–1142.

Coravos, A., I. Chen, A. Gordhandas, and A. D. Stern. 2019. We should treat algorithms like prescription drugs. Quartz, February 14. https://qz.com/1540594/treating-algorithms-like-prescription-drugs-could-reduce-ai-bias (accessed April 15, 2020).

Du Bois, W. E. B. 1899. The Philadelphia Negro. Philadelphia: University of Pennsylvania Press.

EC (European Commission). 2014. Green paper on citizen science for Europe: Towards a society of empowered citizens and enhanced research. https://ec.europa.eu/digital-singlemarket/en/news/green-paper-citizen-science-europe-towards-society-empowered-citizens-and-enhanced-research (accessed April 15, 2020).

Flier, J. S., and J. Loscalzo. 2017. Categorizing biomedical research: The basics of translation. FASEB Journal 31(8):3210–3215. https://doi.org/10.1096/fj.201700303R.

Goodfellow, I. J., J. Shlens, and C. Szegedy. 2015. Explaining and harnessing adversarial examples. Conference paper presented at the 2015 International Conference on Learning Representations, San Diego, CA. https://arxiv.org/pdf/1412.6572.pdf (accessed April 15, 2020).

Grant, A. D., G. Wolf, and C. Nebeker. 2019. Approaches to governance of participant-led research: A qualitative case study. BMJ Open 9(4):e025633.

Hand, D. 2018. Aspects of data ethics in a changing world: Where are we now? Big Data 6(3):176–190.

Heigl, F., B. Kieslinger, K. T. Paul, J. Uhlik, and D. Dörler. 2019. Opinion: Toward an international definition of citizen science. Proceedings of the National Academy of Sciences 116(17):8089–8092.

Heyen, N. B. 2020. From self-tracking to self-expertise: The production of self-related knowledge by doing personal science. Public Understanding of Science 29(2):124–138.

Kirschenbaum, M.A., M. L. Birnbaum, A. Rizvi, W. Muscat, L. Patel, and J. M. Kane. 2019. Google search activity in early psychosis: A qualitative analysis of internet search query content in first episode psychosis. Early Intervention in Psychiatry. https://doi.org/10.1111/eip.12886 [Epub ahead of print].

Kistka, Z. A. F., L. Palomar, K. A. Lee, F. S. Cole, M. R. DeBaun, and L. J. Muglia. 2007. Racial disparity in the frequency of recurrence of preterm birth. American Journal of Obstetrics and Gynecology 196(2):131.e1–131.e6.

Mathews, D. J. H., D. M. Hester, J. Kahn, A. McGuire, R. McKinney, K. Meador, S. PhilpottJones, S. Youngner, and B. S. Wilfond. 2016. A conceptual model for the translation of bioethics research and scholarship. Hastings Center Report 46:34–39.

Maton, K. I., M. R. Sto Domingo, K. E. Stolle-McAllister, J. L. Zimmerman, and F. A. Hrabowski 3rd. 2009. Enhancing the number of African Americans who pursue STEM Ph.D.s: Meyerhoff Scholarship Program outcomes, processes, and individual predictors. Journal of Women and Minorities in Science and Engineering 15(1):15–37.

Maton, K. I., S. A. Pollard, T. V. McDougall Weise, and F. A. Hrabowski. 2012. Meyerhoff Scholars Program: A strengths-based, institution-wide approach to increasing diversity in science, technology, engineering, and mathematics. Mount Sinai Journal of Medicine 79(5):610–623.

Metcalf, J. 2014. Ethics codes: History, context, and challenges. Council for Big Data, Ethics, and Society. https://bdes.datasociety.net/council-output/ethics-codes-history-context-and-challenges (accessed April 20, 2020).

Miller, B. 2013. When is consensus knowledge based? Distinguishing shared knowledge from mere agreement. Synthese 190(7):1293–1316.

Millum. J., B. Sina, and R. Glass. 2015. International research ethics education. JAMA 313(5):461–462.

Nissenbaum, H. 2011. A contextual approach to privacy Online. Daedalus 140:32–48.

Nissenbaum, H. 2018. Respecting context to protect privacy: Why meaning matters. Science and Engineering Ethics 24(3):831–852.

Obermeyer, Z., B. Powers, C. Vogeli, and S. Mullainathan. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464):447–453.

Price, W. N. 2nd, and I. G. Cohen. 2019. Privacy in the age of medical big data. Nature Medicine 25(1):37–43.

Quiñonero-Candela, J., M. Sugiyama, A. Schwaighofer, and N. D. Lawrence. 2009. Dataset shift in machine learning. Cambridge, MA: The MIT Press.

Roberts, D. 2018. Fatal invention: How science, politics and big business recreate race in the 21st Century. New York: The New Press.

Rothstein, M. A., J. Wilbanks, L. M. Beskow, K. Brelsford, K. Brothers, M. Doerr, B. J. Evans, C. Hammack, M. McGowan, and S. A. Tovino. 2020. Unregulated health research using mobile devices: Ethical considerations and policy recommendations. Journal of Law, Medicine & Ethics 48(Suppl 1):196–226.

Saenz, C., E. Heitman, F. Luna, S. Litewka, K. W. Goodman, and R. Macklin. 2014. Twelve years of Fogarty-funded bioethics training in Latin America and the Caribbean: Achievements and challenges. Journal of Empirical Research on Human Research Ethics 9(2):80–91.

Saha, K., L. Chan, K. De Barbaro, G. D. Abowd, and M. De Choudhury. 2017. Inferring mood instability on social media by leveraging ecological momentary assessments. Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies 1(3).

Saria, S., and A. Subbaswamy. 2019. Tutorial: Safe and reliable machine learning. Presented at the 2019 Association for Computing Machinery (ACM) Conference on Fairness, Accountability, and Transparency. https://arxiv.org/pdf/1904.07204.pdf (accessed April 15, 2020).

Sharon, T. 2016. The Googlization of health research: From disruptive innovation to disruptive ethics. Personalized Medicine 13(6):563–574.

Solove, D. 2020. The myth of the privacy paradox. GW Law Faculty Publications & Other Works. No. 1482. https://scholarship.law.gwu.edu/faculty_publications/1482 (accessed April 30, 2020).

Sto Domingo, M. R., S. Sharp, A. Freeman, T. Freeman Jr, K. Harmon, M. Wiggs, V. Sathy, A. T. Panter, L. Oseguera, S. Sun, M. E. Williams, J. Templeton, C. L. Folt, E. J. Barron, F. A. Hrabowski 3rd, K. I. Maton, M. Crimmins, C. R. Fisher, and M. F. Summers. 2019. Replicating Meyerhoff for inclusive excellence in STEM. Science 364(6438):335–337.

Subbaswamy, A., and S. Saria. 2020. From development to deployment: Dataset shift, causality, and shift-stable models in health AI. Biostatistics 21(2):345–352.

van der Zee, T., and J. Reich. 2018. Open Education Science. AERA Open 4(3):1–15.

White, R. W., P. Murali Doraiswamy, and E. Horvitz. 2018. Detecting neurodegenerative disorders from web search signals. npj Digital Medicine 1:8.

Wiens, J., S. Saria, M. Sendak, M. Ghassemi, V. Liu, F. Doshi-Velez, K. Jung, K. Heller, D. Kale, M. Saeed, P. N. Ossorio, S. Thadaney-Israni, and A. Goldenberg. 2019. Do no harm: A roadmap for responsible machine learning for health care. Nature Medicine 25(9):1337–1340.

Zech, J. R., M. A. Badgeley, M. Liu, A. B. Costa, J. J. Titano, and E. K. Oermann. 2018. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLOS Medicine 15(11):e1002683.

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