Crowdsourcing has transformed conventional methods of problem-solving by gathering human intelligence on a significant scale, although the value of the method is often decreased, based on bias, inconsistency, and scalability. In this paper, we will present a conceptual and technical overview of how Artificial Intelligence (AI) and Machine Learning (ML) can help improve human-AI collaboration on crowdsourcing platforms and improve social computing. In AI's role as a complement to human response in crowdsourcing, by using unified ML algorithms to give out tasks and make decisions about quality, we expect AI will help produce more consistent, fluid, and scalable crowdsourced solutions. Some of the hybrid models of AI and human reasoning discussed in this paper include the following: AI that promotes optimal workflow, eliminates redundancy, and generally helps to improve the solidity of group decision-making. Ethical considerations discussed in this paper include removing bias, transparency, and building trust to name a few, to ensure AI usage in social computing systems will be more just and accountable. The results show that an adaptive AI infrastructure can significantly influence the efficiency and quality of the crowdsourced activities and leads to stronger and more sustainable social computing applications. The paper proposes an agenda for future research on human-AI collaboration, in the area of explanation and safety at the centred focus of AI-driven crowdsourcing contexts

AI Collaboration, Crowdsourcing, Social Computing, Machine Learning, Task Optimization, Bias Mitigation, Trustworthy AI