The AI Federal Leadership Program is designed for members of the Senior Executive Service/Equivalents and select GS-15s. Considering questions such as how will frontline employees interpret model outputs and what are the privacy implications of using this system will help technical and non-technical leaders find concrete points of collaboration and ensure the AI tool is well-integrated into the broader system. We also found that white women received the least positively framed feedback of these groups. Because our previous analyses identified significant differences in how men and women were rated by others, such as supervisors, direct reports, colleagues, or friends and family, we also ran an additional statistical test, a two-way analysis of variance (ANOVA), to explore any trends in how others rated women based on their race and ethnicity. Robust data quality is an important consideration regardless of an agencys intent to use AI, but agencies should take particular care to have this foundation in place if they are interested in using artificial intelligence tools to deliver public services. Specifically, scores on the two core values stewardship of public trust and commitment to public good. Our detailed analysis and identification of key trends in how federal leaders are rated by themselves and others provides support for the persistence of stereotypical perceptions of what it means to be a leader.2456. Join the Partnership on Tuesday, Feb. 21, at 12 p.m. EST for a conversation on the Excellence in Government Fellows program, our . "Leadership perceptions as a function of raceoccupation fit: The case of Asian Americans." Schmader, Toni. 27. The racial and ethnic breakdown of these employees is in the figure below: !function(){"use strict";window.addEventListener("message",(function(e){if(void 0!==e.data["datawrapper-height"]){var t=document.querySelectorAll("iframe");for(var a in e.data["datawrapper-height"])for(var r=0;r
