Ensuring Platform Transparency and Accountability
Summary
Open-source investigations and public interest research using platform data (e.g., Facebook, YouTube) have enabled the collection of evidence of human rights atrocities, identified the role of foreign adversaries in manipulating public opinion before elections, and uncovered the prevalence and reach of terrorist radicalization and recruitment tactics. Nascent data privacy legislation such as the EU General Data Protection Regulation and the California Consumer Privacy Act have placed increased pressure on platforms to restrict third party access to data. In an overly cautious interpretation of these laws, platforms are increasingly restricting third-party access to the data they collect. In doing so, platforms shield themselves from public scrutiny and accountability.
To support transparency and accountability of platforms, the next administration should work with Congress to ensure that any new data privacy legislation proposed at the federal level does not inadvertently block the ability of third parties to gain access to platform data for open-source investigations and public interest research. The White House Office of Science and Technology Policy should take the lead by convening a workshop among key actors to make progress on these goals. Out of the workshop, a federal working group should be formed to develop principles and operational guides to support ethical third-party access to platform data, including the formation of technical standards to ensure data privacy and security.
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.