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Why do some states abide by international law to counter terrorism, while others use broad definitions of terrorism to violate human rights? The lack of data differentiating human rights abuses in the name of counter-terrorism from other settings have impeded comparative investigations on this topic. I address this gap by creating the first annual global dataset of counter-terrorism human rights violations using a supervised machine learning method. I develop a new measure of counter-terrorism human rights abuses using a latent variable model to correct for bias in human rights reporting sources and use an instrumental variable analysis to control for endogeneity. I illustrate that countries are more likely to violate human rights in the context of counter-terrorism when there is greater political exclusion of ethnic groups. Framing out-groups as terrorists provides political cover for governments to stifle political dissent, provide legality for abuses, and avoid public backlash for violations.