At the AIAL, we have a comprehensive view of AI accountability. Some of the most stagnant issues we currently encounter are rooted in extractive technological ecologies and oppressive capitalist structures.
Our inquiries into AI accountability, therefore, span from studies of large systems, structures, and ecologies (such as the AI field itself and regulatory processes) to executions of audits and evaluations on specific AI models, tools, and training datasets. We are also invested in conceptual and critical work that advances frameworks and theories of change that underpin algorithmic audit, model evaluation, and meaningful accountability.
We recognize that AI accountability research is most impactful when it can inform the public, impacted groups, and policy makers. Thus, we aim for active policy translation of our (as well as field wide) research.