Purpose: To explore the value and the case for designing antifragile socio-technical information systems (IS) in an era of big data, moving beyond traditional notions of IS design towards systems that can leverage uncertainty for gains. Design/methodology/approach: A design science research (DSR) approach was adopted, comprising four stages, including problem identification and solutions definition, conceptual artifact or socio-technical system design, preliminary evaluation, and communication and knowledge capture. Findings: A conceptual socio-technical artifact that identifies antecedents to antifragile IS design. When operationalised, the antecedents may produce the desired antifragile outcome. The antecedents are categorised as value propositions, design decisions and system capabilities. Research limitations/implications: This research is conceptual in nature, applied and evaluated in a single big data analytics case study in Facebook-Cambridge Analytica. Future research should empirically validate across a range of real-world big data contexts, beyond the presented case study. Practical implications: Uncertainty generally results in socio-technical system failures, impacting individuals, organisations and communities. Conversely, antifragile IS can respond favourably to the shocks and stressors brought forth by periods of elevated uncertainty. Social implications: Antifragile IS can drive socio-technical systems to respond favourably to uncertainty and stressors. Typically, these socio-technical systems are large, complex structures, with increased connectivity and the requirement to generate, process, analyse and use large datasets. When these systems fail, it affects individuals, organisations and communities. Originality/value: Existing IS design methodologies and frameworks largely ignore antifragility as a possible designable outcome. Extant research is limited to abstract architectural design, and approaches based on the proposition of principles. This research contributes to knowledge of antifragile IS design, by deriving a conceptual artifact or socio-technical system based on antecedent-outcome relationships that leverage uncertainty towards performance gains.