Principles of diversity, equity, and inclusion (DEI) are important across academia, industry, and government, yet progress has been uneven. As individuals, we tend to evaluate others in terms of our own norms, beliefs, and standards. We also tend to find comfort in likeness. History has demonstrated that it is difficult to share power and the faces on organization charts often don’t change with any regularity. Change also encounters political obstacles, particularly when relying solely on “representationalism” to increase diversity. Dimensions of DEI tend to fall into two categories. Some dimensions are visible characteristics - age, color, gender, and physical ability. But others are less visible - education, ethnicity, experience, gender identity, heritage, language, national origin, occupation, political beliefs, mental ability, religious beliefs, sexual orientation, veteran status, etc. An understanding of the past can help us understand how DEI efforts have fallen short or failed.

The software profession benefits from diversity and inclusion because the community is challenged by situations requiring a multi-disciplinary approach. Effective software solutions often involve a blend of techniques and practices drawn from business, engineering, government, science, and the social sciences. End-user stakeholders are diverse and may include aviators, bankers, doctors, educators, engineers, government officials, and scientists. Without DEI, lives and economies can be at stake. While many practitioners may lack a formal education in software or computer science, DEI principles and their adoption remain relevant to all. This Systems, Programming, Languages, and Applications: Software for Humanity (SPLASH) Conference panel will reflect on the questions that need be addressed to foster the adoption of DEI principles by individuals, organizations, and communities as the software field continues to grow.

Questions for discussion and debate raised during panel planning include:

• When, in the process of designing and developing solutions, should DEI dimensions be considered?
• What needs to change in our software practices and tools to make the way we engineer software more inclusive?
• What are the implications of not including underrepresented groups in the development of AI technology, and why is diversity so crucial to the development of Artificial Intelligence?
• How are we evaluating any potential harm to users caused by biased or non-inclusive technology - and what steps can we take to mitigate that harm?
• How do we build diverse teams, and create inclusive work environments to make sure we're building software we won't regret later?
• How can we motivate the post-pandemic use of virtual/hybrid meeting technology to improve participant accessibility and equity - particularly for international conferences?
• How do you get meeting attendees to share diverse points of view – rather than simply going with “majority” opinions?
• How should we encourage the design and use of more inclusive user interfaces to avoid cultural bias?
• How should we design "training data" for AI systems to minimize data bias?

Session attendees will be polled using MentiMeter Code (menti.com): 4154 2718