We illustrate the emergent spectrum of human-AI hybrids in digital platforms and discuss some implications for IS research by using one class of digital platforms: digital labor platforms. Recognizing the service orientation and the expanding role of AI in digital platforms, we define digital labor platforms as online environments where digital services are sourced and delivered in exchange for compensation, with constituent tasks for the services determined, executed, and coordinated by human and AI agents. Work done on these platforms is, by definition, digital and can thus be modularized into tasks which require a range of cognitive skills for execution and coordination, providing a rich context to illustrate human-AI hybrids and some key issues for next-generation digital platforms.

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Deep learning methods to develop artificial intelligence (AI) systems produce black-box models that achieve high levels of prediction accuracy but are inscrutable to human users. Explainable AI (XAI) techniques offer the potential to achieve both prediction accuracy and explainability objectives with AI applications by converting black-box models to glass-box models that can be interpreted. Understanding how to effectively use XAI in marketing opens up exciting research avenues relating to the role of different classes of XAI in redefining the tradeoff between prediction accuracy and explainability, creating trustworthy AI applications, achieving AI fairness, and modifying the privacy calculus of consumers. It also raises important questions related to the levels of explainability and transparency for different users and how explainability can create value for different stakeholders involved in the development and deployment of marketing AI applications.