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Fairness in speech processing systems is a critical challenge, especially regarding performance disparities based on speakers' backgrounds. To help combat this problem, we are introducing FaiST (Fairness in Speech Technology), a novel speech dataset from American English speakers of various racial, ethnic, and national origin groups. The goal is to evaluate and mitigate possible biases across speech technologies using conversational speech from podcasts and interviews online. In FaiST's current version, speakers self-identified as Asian American and African American, and future iterations will include other groups. White American speakers' speech was extracted from VoxCeleb, and their demographic labels were obtained online. In addition to identifiers of race, ethnicity, and national origins, FaiST is also marked for the exact instances in the conversation where self-identifications occurred. We experimented with FaiST and found racial bias in eighteen Automatic Speech Recognition systems.