Data science is a discipline that has emerged at the intersection of computing and statistics – two disciplines with long standing guidance for ethical practice that feature professional integrity and responsibility. The 2018 National Academies of Science Report on Envisioning the Data Science Discipline recommends that “The data science community should adopt a code of ethics”, but due to its recency and to the diversity of paths into data science as a discipline, there is no real “community” that can do or organize this adoption. To support this recommendation, this white paper is an effort to document concordance across professional association practice standards, intended to support the ethical practice of data science by appealing to the consensus of these professional organizations on what constitutes ethical practice. The American Statistical Association (ASA) and the Association of Computing Machinery (ACM) recently revised their professional ethical practice standards in 2018. Both sets of guidance represent the perspectives of experienced professionals in their respective domains, but both organizations explicitly state that the guidelines apply to – should be utilized by – all who employ the domain in their work, irrespective of job title or training/professional preparation. Given that both statistics andcomputing are essential foundations for data science, their ethical guidance should therefore be a starting point for the community as it contemplates what “ethical data science” looks like.The work of analyzing concordance in ethical guidance begins with a qualitative examination of the overlap (similarly worded principles), alignment (thematically similar principles), and gaps (dissimilar principles) that exist between existing sets of standards. To that end, the ethical practice guidance has been thematically analyzed from the standards outlined by the ASA, ACM, the International Statistics Institute, Royal Statistical Society, and the Ethics in Action guidance drafted by the Institute of Electrical and Electronics Engineers (IEEE) Initiative on Ethics of Autonomous and Intelligent Systems. This synthesis is intended to capture similarities and differences in relevant practical guidelines, integrating professional organizational perspectives on what constitutes ethical practice in data science to support and strengthen the domain. Ultimately, guidelines for ethical data science that reflect the concordance of cognate disciplines can ensure coherent integration of the features of ethical practice into training of data scientists - for both the practitioner and those who use data science, or its outputs, in their work.
Keynote 5. Seeking synergies in data science and applications: How intelligent systems are driving transitions in management for Earth Resource systems. Dr. Suzanne A. Pierce
