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Participatory design initiatives, especially within the realm of digital civics, are often integrated and co-developed with the very citizens and communities they intend to assist. Digital civics research aims to create positive social change using a variety of digital technologies. These research projects commonly adopt various embedded processes, such as commissioning models \cite{dcitizensproj22}. Despite the adoption of this process within a range of domains, there isn't currently a framework for best practices and accountability procedures to ensure we engage with citizens ethically and ensure the sustainability of our projects. This workshop aims to provide a space to start collaboratively constructing a dynamic framework of best practices, laying the groundwork for the future of sustainable embedded research processes. The overarching goal is to foster discussions and share insights that contribute to developing effective practices, ensuring the longevity and impact of participatory digital civics projects.

The DCitizens SIG aims to navigate ethical dimensions in forthcoming Digital Civics projects, ensuring enduring benefits and community resilience. Additionally, it seeks to shape the future landscape of digital civics for ethical and sustainable interventions. As we dive into these interactive processes, a challenge arises of discerning authentic intentions and validating perspectives. This exploration extends to evaluating the sustainability of future interactions and scrutinising biases impacting engaged communities. The commitment is to ensure future outcomes align with genuine community needs and address the ethical imperative of a considerate departure strategy. This dialogue encourages future researchers and practitioners to integrate ethical considerations and community-centric principles, fostering a more sustainable and responsible approach to technology-driven interventions in future urban regeneration and beyond.

Recent advancements in general-purpose AI have highlighted the importance of guiding AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept broadly recognized as alignment. However, the lack of clarified definitions and scopes of human-AI alignment poses a significant obstacle, hampering collaborative efforts across research domains to achieve this alignment. In particular, ML- and philosophy-oriented alignment research often views AI alignment as a static, unidirectional process (i.e., aiming to ensure that AI systems' objectives match humans) rather than an ongoing, mutual alignment problem. This perspective largely neglects the long-term interaction and dynamic changes of alignment. To understand these gaps, we introduce a systematic review of over 400 papers published between 2019 and January 2024, spanning multiple domains such as Human-Computer Interaction (HCI), Natural Language Processing (NLP), Machine Learning (ML). We characterize, define and scope human-AI alignment. From this, we present a conceptual framework of "Bidirectional Human-AI Alignment" to organize the literature from a human-centered perspective. This framework encompasses both 1) conventional studies of aligning AI to humans that ensures AI produces the intended outcomes determined by humans, and 2) a proposed concept of aligning humans to AI, which aims to help individuals and society adjust to AI advancements both cognitively and behaviorally. Additionally, we articulate the key findings derived from literature analysis, including literature gaps and trends, human values, and interaction techniques. To pave the way for future studies, we envision three key challenges and give recommendations for future research., Comment: proposing "bidirectional human-AI alignment" framework after a systematic review of over 400 alignment papers