Call for Papers ― EMSE Special Issue on “Equitable Data and Technology”

A special issue of the Empirical Software Engineering Journal. http://www.springer.com/computer/swe/journal/10664

Editors of the Special Issue

Brittany Johnson, George Mason University
Justin Smith, Lafayette College

Contact

Brittany Johnson (johnsonb@gmu.edu)
Justin Smith (smithjus@lafayette.edu)

Description of the Special Issue

There are too many recent examples where software has been implicated in the perpetuation of social injustice. Society now demands that its software be fairer and more equitable. Governments, companies and professional societies such as the European Union, Microsoft, and the IEEE have white papers discussing requirements for fair and ethical software. This special issue in the Empirical Software Engineering journal is provides a venue for researchers and practitioners to share their contributions to the space of designing, building, and maintaining fair and equitable data-driven software systems. Specifically, we seek papers that offer empirical studies based on software engineering project data that:

explores problems of equity in data-driven software;
proposes & critiques ways to improve software inclusiveness;
defines tools and techniques for supporting the development of data-driven solutions; or
enumerate challenges that need to be addressed to improve data-driven software development

Since many issues associated with fairness are often sociological in nature, we welcome commentaries from both inside and outside of computer science that can shed light on the complex issue of fairness. We say this since the challenges raised by unfair and inequitable software may be algorithmic and/or socio-technical in nature.

Submission Topics

Topics of interest include, but are not limited to:

Sociotechnical challenges

What are the considerations, beyond implementation and testing, when designing dat-driven software systems?
How to determine the trade-offs between making fair(er) systems and other objectives of a system?
How do we build equitable software given the inherently unfair social pressures behind it?

Algorithmic challenges

How to identify bias in AI models?
How to explain the source or reason for this bias?
How to measure the level of bias in systems?
How to mitigate the effect of this bias by changing how models are trained?
How to provide support for explanation of automated decisions and redress for stakeholders and other mechanisms for accountability and transparency of deployed systems?

Schedule

Submission Deadline: September 15, 2022

Submission Instructions

Papers should be submitted through the Empirical Software Engineering editorial manager website (http://www.editorialmanager.com/emse/). More instructions to come.