Early-Career View on Data Science Challenges: Responsibility, Rigor, and Accessibility

Shuang Frost,William Seaton,Sara Stoudt,Ana Trisovic,Aleksandrina Goeva

doi:10.1162/99608f92.b211611a

Abstract

Early-Career View on Data Science Challenges: Responsibility, Rigor, and Accessibility

Highlights

As emerging voices in the data science community, we see a lot of promise in the future of the field and our roles in it
We are focused on understanding how we should participate in the data science community and what our responsibilities are, to our colleagues and to broader society
Many of us fell into data science when we dared to peek behind the algorithmic curtain

Summary

Introduction

As emerging voices in the data science community, we see a lot of promise in the future of the field and our roles in it. The fact that we are still asking what data science is, and debating whether it is even a discipline, can be seen as a mixed blessing It signals that there might still be the opportunity for growth and plenty of research left to be done. Similar data science methodology is applied across different domains, showcasing the interdisciplinary utility of these methods and leaving us struggling to put data science in a box, or even a Venn Diagram (Conway, 2010). This commonality across domains allows researchers trained in one data-driven field to leverage and apply their skills into seemingly unconnected careers. We are learning about the best ways to collect, store, clean, analyze, and disseminate data while at the same time realizing that a clean and ordered data set does not necessarily represent a data set of inherent value

Useful Data

Data Selves

Building Public Trust

Transparency and Validation

Myriad of Roles and Broad Access

Path Forward