Technical Talent Strategies to Build Capacity, Accelerate Priorities, and Drive Change
Summary
The Biden-Harris Administration is confronting multiple challenges that require a coordinated, innovative, and flexible response by the federal government. The recently released FY22 President’s budget sets a solid foundation for leveraging the capacity of the federal workforce, along with necessary science, technology and innovation expertise from the private sector, to meet the challenges ahead.
However, hollowed out agencies and technical skills gaps mean agencies lack the capacity to implement needed programs. Agencies have to rapidly scale up personnel, ensure they have the necessary skills, and implement underutilized hiring mechanisms to fill out talent gaps.
While the goals laid out in the budget will allow agencies to address climate, continue to fight the COVID-19 pandemic, rebuild the economy, and increase equity across government programs and services, it requires a sustained focus on building and hiring diverse expertise to accelerate progress on these initiatives – which increasingly rely on modernized IT infrastructure and equitable delivery of services.
This is an historic opportunity, driven by critical need, to focus on driving systemic change across government to equip all federal agencies with the capacity required to build back better while bolstering and reinvigorating the federal talent pipeline.
The following proposals are offered as ways to tackle hiring challenges, build a diverse technical talent pipeline, and continue to rebuild the public trust in government and interest in serving. The Day One Project and its partners stand ready to assist in fleshing out and supporting the proposals below.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.
When properly structured — with specific numeric targets, secured financial obligations, independent monitoring, and meaningful enforcement — CBAs transform data center deals into durable community partnerships.