Algorithmic Accountability Complaint Form

Government agencies increasingly rely on algorithms and automated systems to make decisions that affect citizens' lives—from benefit eligibility to permit approvals, enforcement priorities to risk assessments. While these systems can improve efficiency, they can also produce biased, opaque, or unfair outcomes that require accountability and oversight.

This Algorithmic Accountability Complaint Form empowers citizens to challenge automated decisions, request transparency about how algorithms work, access bias audit reports, demand human review, and appeal adverse impacts. Whether you've been denied benefits by an automated system, flagged by a risk-assessment tool, or believe an algorithm treated you unfairly, this form provides a clear pathway to lodge your complaint and seek remedy.

Built for government agencies committed to responsible AI and algorithmic transparency, this template helps compliance teams, ombudsman offices, and oversight bodies manage accountability requests in line with emerging regulations around automated decision-making. It's designed for use by state and local governments, federal agencies, regulatory bodies, and public administrators implementing algorithmic accountability frameworks.

The form guides complainants through documenting their experience, specifying the type of automated system involved, describing the adverse impact, and selecting the remedies they're seeking—from explanations and audits to appeals and human review. Conditional logic ensures each submission captures the right details based on the type of complaint and decision in question.

With Paperform's flexible design and powerful conditional logic, you can brand this form to match your agency's visual identity while maintaining the clarity and accessibility required for public-facing accountability tools. Use Paperform's native integrations or connect Stepper to automatically route complaints to the right department, trigger case management workflows, send acknowledgment emails, and keep complainants informed throughout the review process—all without writing code.

Whether you're building an algorithmic transparency program from scratch or strengthening existing accountability mechanisms, this template provides a citizen-centered foundation for fair, transparent, and responsive governance in the age of automated decision-making.