Managing Risk, Bias, and Algorithmic Uncertainty

The governance of risk has long occupied a central position in management theory and organizational practice. Classical frameworks conceptualize organizational risk as a function of environmental uncertainty, resource dependency, and strategic exposure, with governance mechanisms designed to reduce variance in outcomes and protect the firm from exogenous shocks (March & Shapira, 1987; Miller, 1992). The diffusion of AI into core organizational processes introduces a qualitatively distinct risk profile that these frameworks only partially address. Algorithmic systems generate risks that are endogenous to the organization’s own cognitive architecture: they originate not in the external environment but in the design choices, data structures, and inferential logics embedded in the systems through which the firm perceives and responds to its environment. Managing these risks requires governance approaches that extend classical frameworks in substantive ways. This chapter develops an analytical framework for algorithmic risk governance spanning three levels of analysis: technical, organizational, and societal. It examines the principal categories of algorithmic risk such as bias, opacity, instability, and systemic uncertainty, and argues that effective governance must address their interactions rather than treating them as independent challenges. The chapter draws on research in risk management, organizational governance, and the emerging literature on responsible AI to develop principles for adaptive governance under conditions of algorithmic uncertainty.