A Strategy to Blend Domestic and Foreign Policy on Responsible Digital Surveillance Reform
Summary
Modern data surveillance has been used to systematically silence free expression, destroy political dissidents, and track ethnic minorities before placement in concentration camps. China’s surveillance-export system is providing a model of authoritarian stability and security to the 80+ countries using its technology, a number that will grow in the aftermath of COVID-19 as the technology spreads to the half of the world still to come online. This technology is shifting the balance of power between democratic and autocratic governance. Meanwhile, the purported US model is un-democratic at best: a Wild West absent of accountability and full of black box, NDA-protected public-private partnerships between law enforcement and surveillance companies. Our system continues to oppress marginalized communities in the US, muddying our moral claims abroad with hypocrisy. Surveillance undermines the privacy of everyone, but not equally. Most citizens remain unaware of, unaffected by, or disinterested in the daily violence propagated by the unregulated acquisition and use of surveillance. The lack of coordination between state and local agencies and the federal government around surveillance has created a deeply unregulated surveillance-tech environment and a discordant international agenda. Digital surveillance policy reform must coordinate both domestic and foreign imperatives. At home, it must be oriented toward solving a racial equity issue which produces daily harm. Abroad, it must be motivated by preserving 21st century democracy and human rights.
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.