By inspiring a new generation to servethats youand working with federal leaders to bring top talentyou again!into the workforce, we are transforming the way government works. 1. The individuals listed below generously offered their input on how government leaders can apply responsible AI principles to the use of artificial intelligence in public service delivery. At PAI, equity and inclusion are core values which we seek to promote among our Partner organizations, in our own work, and throughout the greater AI field, including in machine learning and other automated decision-making systems. 18. Tulshyan, R., and J. Diggs, Schnequa Nicole. Read news stories about the Best Places to Work in the Federal Government rankings. We believe that our future and our democracy depend on our ability to solve big problemsand that we need an effective federal government to do so. Handbook on Gender and Public Administration. The reasons for these gender differences could be due to both employment and wage prospects in the public sector . Making information easily available can help build trust between agencies and the people they serve, and this is all the more important when customers may not have much familiarity with how these tools operate. Journal of Social Issues 75.2 (2019): 568-591. Please note, many agencies have specific application guidelines for their employees. While not statistically significant, employees of diverse racial and ethnic backgrounds self-rated higher than their white colleagues on the remaining three subcompetenciesembracing risk and uncertainty, evidence-based decision-making, and systems thinkingas well as the core value of commitment to public good. In all, these findings suggest that federal leaders gender, or racial/ethnic background affects how they are perceived by their colleagues and coworkers. Partnership for Public Service. We also train college and university career counselors to help their students understand the federal recruiting and hiring process and identify federal internship and fellowship opportunities. ActionAid International (AAI), Eastern and Southern Africa Small Scale Farmers' Forum (ESAFF), SAfAIDS, and Public Service Accountability Monitor (PSAM) of Rhodes University. Center for Digital Government, IBM and NASCIO, AI Meets the Moment, 2021, 11. They can influence actions and decisions such as whom we hire or promote, how we interact with persons of a particular group, what advice we consider, and how we conduct performance evaluations. In the event of a conflict, please notify a Partnership staff member as soon as possible, even before the first session is held. Sy, Thomas, et al. Breslin, Rachel A., Sheela Pandey, and Norma M. Riccucci. I am just beginning to see that connection [between data quality and readiness for AI] happen in a meaningful operational way in state and local governments, said Takai of the Center for Digital Government. Federal government websites often end in .gov or .mil. Once accepted into the program, each participant will be assigned to a cohort led by an executive coach. Partnership for Public Service. From increasing efficiency to finding data insights that enhance the customer experience, AI is an invaluable tool for federal leaders to serve the public and transform their agencies. Agencies also should consider how to establish governance processes that facilitate agility, so that they can adapt as circumstances change and continue to adhere to responsible AI principles. Partnership for Public Service and Accenture Federal Services, Government for the People: Designing for Equitable and Trusted Customer Experiences, Nov. 16, 2021. 7. .usa-footer .container {max-width:1440px!important;} Roadmap for Renewing our Federal Government, Best Places to Work in the Federal Government. Participants will choose to participate in the full program either virtually or in-person. 22. Full-Time. These race and ethnicity categories predate the authors work on this project. Our executive coaching team is dedicated to helping participants reach their leadership goals. Particularly when certain constituent characteristics are underrepresented in data or form a small portion of a services customer base, public service leaders should ensure that the data underpinning AI tools accounts for everyone who might interact with a service. The Partnership for Public Service is committed to building a culture of inclusion in which a diverse workforce has equitable opportunities to contribute, succeed and grow. Harvard Business Review, July 14 (2021). Without robust attention to representativeness, an AI model in this situation could fail to perform correctly and could even worsen service delivery. 2. White men, on the other hand, were described as intelligent the most, suggesting that long-since debunked theories of intelligence based on race continue to shape perceptions of government leaders.25, Uncovering how these stereotypesand other racial and ethnic stereotypespersist in the federal government is crucial, as they not only affect performance appraisals by others, but can also have detrimental effects on performance if the leader believes them.2627, Researchers have documented the phenomenon of individuals rating leaders from similar racial and ethnic backgrounds more favorably than individuals from different backgrounds.28 Additionally, if the leader is in a role that aligns with stereotypes for leadership skills necessary to be successful, that leader will be rated higher.29. 5 . Microsofts mission is to empower every person and every organization on the planet to achieve more. of participants agree that they have a greater understanding of adopting and managing AI. BCG is a global management consulting . Nonetheless, a broader acceptance of white men as leaders and an implicit bias against women and employees with diverse backgrounds has enabled the persistent belief that both groups are less competent in the workplace and closed off opportunities for underrepresented groups to advance into senior federal leadership roles.2324, For example, we found that white men and men of diverse backgrounds received more positively framed feedback on questions related to improving their leadership style than women from both demographic groups. We have identified eight main solutions that we believe are critical to improving the way our government works so that it can better serve the public. Women of diverse racial and ethnic backgrounds were identified as trustworthy the most in our sample. Men of diverse racial and ethnic backgrounds received more positively framed feedback than diverse groups of women. Retrieved from, 6. p.usa-alert__text {margin-bottom:0!important;} Different agencies and levels of government have widely varying experience with using artificial intelligence in public service delivery. This is the third brief in the Partnership for Public Services LeadHERship series, which explores these issues in greater depth. 17. In addition, the adjective trustworthy is used statistically significantly less for white women than for any other group in our sample. 26. @media (max-width: 992px){.usa-js-mobile-nav--active, .usa-mobile_nav-active {overflow: auto!important;}} Public Administration Review 82.3 (2022): 537-555. 30. Through a partnership with Microsoft and Google, we are creating a cohort of senior leaders across government who are prepared to guide their agencies AI strategy. Organizations that have a rigorous, wholistic approach to cleaning and storing data will be better positioned to responsibly use artificial intelligence. The PPS pursues those goals by: Assistant Secretary Kathleen Martinez and Max Stier, President & CEO, Partnership for Public Service, sign ODEP's Alliance Agreement. "Gender and Race, Intersectionality Theory of." Our findings warrant future research to better understand how implicit bias affects the workplace experience of specific groups of federal employees. Retrieved from: www3.weforum.org/docs/WEF_GGGR_2021.pdf. This significant downturn in employee engagement and satisfaction occurred as the COVID-19 pandemic continued to disrupt the federal workforce as tens of thousands of civil servants faced uncertainty about returning to the office after more than a year and a half working remotely part or full-time, while a sizable portion of the workforce remained on the frontlines performing critical public services as the health crisis persisted. Artificial intelligence is computers and software performing tasks typically associated with people, such as recognizing speech or images, predicting events based on past information, or making decisions. And while technical experts play an integral role in deciding whether and how to employ artificial intelligence, many more of the government leaders who contribute to this processprogram managers, acquisition professionals, lawyers and frontline service providerslack technical backgrounds. X2 (6, N = 12,792) =24.76, p = .05.22. Average scores on core values and key competencies for the intersection of gender and race/ethnicity. Recognize when AI is not the answer. Learn more about these solutions below. About Us. The strategy for revitalizing public service is pursued through three strategic goals: securing the right talent, fueling innovation and efficiency, and building public support for the nation's civil service. Tho' tenacious in seeking buy-in her sometimes aggressive approach with peers could be more assertive and less aggressive, was coded as negative. Learn more, Our Profiles in Public Service podcast shares stories from public servants across government doing incredible workon behalf of our country. We recommend future research to explore if this trend persists in the data. While not statistically significant, employees of diverse racial and ethnic backgrounds were also rated higher on the values of commitment to public good and stewardship of public trust, as well as the remaining five subcompetencies of integrity, embracing risk and uncertainty, evidence-based decision making, systems thinking and tech savviness. 738 were here. Program Specific Contact Details. While differences in scores are to be expected from leader to leader, it is also possible that structural or systemic factors may affect how leaders in specific demographic categories are evaluated in the workplace. By recognizing this bias and working to combat it through empathy, perspective-taking, and respectful dialogue, we can promote a more nuanced and compassionate understanding of human behavior. "Does diversity-valuing behavior result in diminished performance ratings for non-white and female leaders?." Read more. Artificial intelligence has the potential to improve how government worksmore so than any other recent technological innovation. This trend suggests that the underrepresentation of diverse groups of women in senior federal leadership positions is not due to a lack of leadership ability, but rather due to more systemic or structural factors. Like traditional processes that provide opportunities for members of the public to appeal government decisions, public services using AI must provide customers with due process and opportunity for redress if they are negatively impacted by a decision made by or reliant on AI. There are statistically significant differences in how many positively framed statements were given to leaders based on their gender, and there were close to statistically significant differences based on a leaders race and ethnicity. An agency within the U.S. Department of Labor, 200 Constitution AveNW Phase 1 of the project ran from 2016 to . International Journal of Leadership Studies 1.1 (2005): 28-43. Figure 4. 11. These frameworks often center on the concept of responsible artificial intelligence: the idea that AI tools must meet certain governance and ethical standards in their development, implementation and operation. Our findings indicate that the racial and gender disparities within federal leadership reflect broader stereotypes and biases that have historically resulted in barriers for women and diverse racial and ethnic groups in the workplace. For more than 20 years, we have helped make this . For example, previous research outlines three main barriers to reducing racial and gender disparities in federal leadership: stereotypes about who makes a good leader and what good leadership looks like; a double-paned glass ceiling that holds back women and diverse groups from career advancement; and a lack of mentorship or professional development for these groups.15We cannot know for certain which of these causes, if any, is the most important factor driving our findings, but our data suggests that some combination of all three play a critical role. Leaders can collaborate better when they focus on ensuring the tool is achieving intended outcomes rather than getting caught up in technical specifications or program management frameworks. Explaining the algorithm itself is likely not sufficient, said Vince Dorie, principal data scientist at Code for America. 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