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Responsible Data Science
February 16 @ 10:00 am - 12:00 pm
Description: Technical advancements through data science combined with the exponential increase in data has led to research breakthroughs across domains and generated entirely new industries. But lagging behind this growth is our understanding of the evolving socio-technical landscape and ability to predict the indirect consequences of our work. While laws determine the legal parameters governing data use, data science approaches that are technically legal can still be used unethically and irresponsibly, with disastrous consequences from loss of revenue to human rights violations. Through case studies and interactive sessions, this workshop provides an overview of how to practice responsible data science by incorporating considerations of ethics, equity, and justice. We will discuss FACT (fairness, accuracy, confidentiality, and transparency) based approaches to increasing the integrity of our work in data science. This workshop is being offered in conjunction with UC Love Data Week 2023 (https://uc-love-data-week.github.io/).
- By the end of this workshop, learners should be able to:
- – Describe examples of how the development of data science can both contribute to inequities and be leveraged to address them in their own domain.
- – Begin to identify the underlying goals and incentives influencing their data-driven research.
- – Assess whether a research project’s data meets FAIR criteria.
- – Use a responsible data science framework to evaluate the potential impact(s) of a research project case study.
- – Revise their research design using FACT principles.
Prerequisites: There are no prerequisites for this workshop.
Software: Latest version of Zoom installed; web browser (for example, Chrome or Firefox)
Instructors: Pamela Reynolds and:
Sydney Wood (she/they) is a PhD candidate in Quantitative Psychology with a Designated Emphasis in Computational Social Science. They received a joint Bachelors and Master’s degree in Forensic Psychology in 2017 from CUNY: John Jay College of Criminal Justice. Their research investigates the impact of incentive structures in science on the cultural evolution of social science research practices. They work towards enhancing research methodology education to improve the efficacy of scientific practices in academia. They have collaboratively developed the curricula for this workshop as part of their Professors for the Future Fellowship project to support data scientists and data-driven researchers in developing more equitable and just practices.
Michael Livanos (he/she/they) is a computer science PhD student. Their research is in the field of fairness and explanation in machine learning. They develop new algorithms and approaches for understanding how and why black box machine learning models make the decisions they do, and how to best use them for performance and equity.
Location: Zoom. Click link below to register for this workshop.
Cost: Free of charge.