Astro2020 APC White Paper: Providing a Timely Review of Input Demographics to Advisory Committees

TL;DR

This paper advocates for advisory committees in astronomy to systematically collect and review demographic data of their members and input sources to identify biases and ensure diverse representation, enhancing decision-making quality.

Contribution

It proposes a new practice of collecting and reviewing demographic data within advisory committees to mitigate biases and improve inclusivity in scientific policy recommendations.

Findings

Demographic data review can reveal biases in advisory committees.

Including demographic summaries enhances transparency and inclusivity.

This approach can identify potential blind spots in community input.

Abstract

Organizations that support science (astronomy) such as federal agencies, research centers, observatories, academic institutions, societies, etc. employ advisory committees and boards as a mechanism for reviewing their activities and giving advice on practices, policies and future directions. As with any scientific endeavor, there is concern over complementing these committees with enough members who have as broad a range of expertise and understanding as possible, so that bias is mitigated. However, for a number of reasons (logistical, practical, financial, etc.), committees can also not be infinitely large and thus trade-offs must be made. It is often recognized that conflicts of interest must be acknowledged within these committees, but what is not often recognized it the potential for unmitigated biases and "group think" that can be introduced as part of these committees. In this white paper, we recommend that advisory committees that collect community input, (e.g., the Decadal Survey review committee), also collect, compile and review input demographic data before finalizing reports, (e.g., the final 2020 Decadal Survey Report). A summary of these data should be released alongside the final survey report. This information would enable the committee to understand potential "blind spots" and biases of the data collection phase and inform future data collections of any barriers that affect the omission of perspectives from various demographics.