What do we mean when we say that machines learn? What is the difference that makes a difference between human learning and machine learning? In my talk I will discuss the nature of machine learning (ML), including a series of design decisions that inform ML research designs and the trade-offs they incorporate. I will argue that these trade-offs have real world implications that require the participation of those who will suffer or enjoy the consequences of real world ML applications. Building on Mouffe’s democratic theory and Rip’s constructive technology assessment, I will argue for agonistic or adversarial ML as the only viable way to ensure that ML contributes to human and societal flourishing.

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