Research & Commentary

As state, federal, and global policymakers grapple with how to address concerns about the link between online algorithms and a variety of harms, KGI’s latest report authored by a distinguished group of researchers, technologists, and policy leaders is a roadmap for design and governance solutions that put users first. Product designers and policymakers can promote better algorithms through detailed design transparency, user choices and defaults, and assessments of long-term impact.

The Knight-Georgetown Institute (KGI) submitted comments to the United Kingdom regulator for communications services’ (Ofcom) call for evidence on researcher access to information from digital platforms. KGI’s comments highlight barriers to independent research and practical steps that Ofcom can take to strengthen access to data and independent research in the online information ecosystem.

Late last year, the public got its first glimpse into one of the EU Digital Services Act’s (DSA) key governance tools. Nineteen designated Very Large Online Platforms and Search Engines (VLOPs and VLOSEs) published systemic risk assessment and audit reports focusing on platform risks and mitigation measures.

The DSA’s required risk assessments and audits create the possibility to advance groundbreaking platform transparency and accountability, but this first round falls far short of realizing that potential.

KGI recently submitted comments to the European Commission on its draft Delegated Act on vetted researcher data access, highlighting the connection between vetted access and public access to data, offering  recommendations for specific additional categories of data and research that could be enabled under the Act, and underlying the need to understand systemic risks both across platforms and across borders.

KGI highlights three areas where emerging and established research can inform strategies to guarantee the privacy, safety, and security of minors online: Access to Platform Data, Platform Design Features, and Engagement-based Recommender Systems.