This talk explores the potentials of participatory design approaches within machine learning (ML) research and design, toward developing more responsible, equitable, and sustainable experiences among underrepresented user communities. ML scholars and technologists are expressing emerging interest in the domain of participatory ML, seeking to extend collaborative research traditions in human-computer interaction, health equity, and community development. It is a firm position that participatory approaches that treat ML and AI systems developers and their stakeholders more equally in a democratic, iterative design process, presents opportunities for a more fair and equitable future of intelligent systems. This talk will urge more MI/AL research that employs participatory techniques and research on those techniques themselves, while providing background, scenarios, and impacts of such approaches on vulnerable and underrepresented users. We end by discussing existing frameworks for community participation that promote collective decision making in problem solving, selecting data for modeling, defining solution success criteria, and ensuring solutions have sustainably mutual benefits for all stakeholders.