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Opening up RRI to values and ways of knowing and doing beyond its European and Anglophone origins has become a focal area for scholars and practitioners. This article addresses the role of RRI pedagogy within the broader scope of this transformation, an under-examined topic in the literature. Drawing on the theoretical framework of critical resistance, we explore how RRI pedagogy might offer engaged scholars and educators opportunities to ‘risk themselves’ by intentionally destabilizing their authority as knowers. We offer a case study of a multinational, multilingual, multi-institutional learning initiative drawing from decolonial thinking to resist Anglophone epistemic hegemony in responsible research education. Our case study points to tactics for unsettling pedagogical habits by working across language differences, centering learners’ contexts, attending to the labor of teaching itself, and ‘searching for decoloniality’. 

This essay joins recent scholarship in arguing that FAccT's fundamental framing of the potential to achieve the normative conditions for justice through bettering the design of algorithmic systems is counterproductive to achieving said justice in practice. Insofar as the FAccT community's research tends to prioritize design-stage interventions, it ignores the fact that the majority of the contextual factors that practically determine FAccT outcomes happen in the implementation and impact stages of AI/ML lifecycles. We analyze an emergent and widely-cited movement within the FAccT community for attempting to honor the centrality of contextual factors in shaping social outcomes, a set of strategies we term ‘metadata maximalism’. Symptomatic of design-centered approaches, metadata maximalism abstracts away its reliance on institutions and structures of justice that are, by every observable metric, already struggling (where not failing) to provide accessible, enforceable rights. These justice infrastructures, moreover, are currently wildly under-equipped to manage the disputes arising from digital transformation and machine learning. The political economy of AI/ML implementation provides further obstructions to realizing rights. Data and software supply chains, in tandem with intellectual property protections, introduce structural sources of opacity. Where duties of care to vulnerable persons should reign, profit incentives are given legal and regulatory primacy. Errors are inevitable and inextricable from the development of machine learning systems. In the face of these realities, FAccT programs, including metadata maximalism, tend to project their efforts in a fundamentally counter-factual universe: one in which functioning institutions and processes for due diligence in implementation and for redress of harms are working and ready to interoperate with. Unfortunately, in our world, these institutions and processes have been captured by the interests they are meant to hold accountable, intentionally hollowed-out, and/or were never designed to function in today's sociotechnical landscape. Continuing to produce (fair! accountable! transparent!) data-enabled systems that operate in high-impact areas, irrespective of this landscape's radically insufficient paths to justice, given the unavoidability of errors and/or intentional misuse in implementation, and the exhaustively-demonstrated disproportionate distribution of resulting harms onto already-marginalized communities, is a choice - a choice to be CounterFAccTual.