Santa Clara Principles on Transparency V2

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Published date: December 1, 2021

Subject tag: Algorithmic systems | Data Access | Government transparency | Privacy and data protection

In 2018, alongside the Content Moderation at Scale conferences in the United States, a group of human rights organizations, advocates, and academic experts developed and launched a set of three principles for how best to obtain meaningful transparency and accountability around Internet platforms’ increasingly aggressive moderation of user-generated content. These principles, named after the group’s initial meeting place in Santa Clara, CA, represent recommendations for initial steps that companies engaged in content moderation should take to provide meaningful due process to impacted speakers and better ensure that the enforcement of their content guidelines is fair, unbiased, proportional, and respectful of users’ rights. This was the first iteration of the Santa Clara Principles.

Since 2018, twelve major companies—including Apple, Facebook (Meta), Google, Reddit, Twitter, and Github—have endorsed the Santa Clara Principles and the overall number of companies providing transparency and procedural safeguards has increased, as has the level of transparency and procedural safeguards provided by many of the largest companies.

At the same time, the importance of the role these companies play in society continues to increase, resulting in an ever greater responsibility to provide sufficient levels of transparency around the decisions they make, in order to enable accountability.

For these reasons, a broad coalition of organizations, advocates and academic experts worked together in 2020 and 2021 to develop this second iteration of the Santa Clara Principles. They were developed following a broad consultation exercise involving more than 50 organizations and individuals, and a thorough process of drafting and review. By drawing on experience and expertise from all parts of the world, this second iteration of the Santa Clara Principles better reflects the expectations and needs of the global community.
[This entry was sourced with minor edits from the Carnegie Endowment’s Partnership for Countering Influence Operations and its baseline datasets initiative. You can find more information here:]