In this work, we reflect on the data-driven modeling paradigm that is gaining ground in AI-driven automation of patient care. We argue that the repurposing of existing real-world patient datasets for machine learning may not always represent an optimal approach to model development as it could lead to undesirable outcomes in patient care. We reflect on the history of data analysis to explain how the data-driven paradigm rose to popularity, and we envision ways in which systems thinking and clinical domain theory could complement the existing model development approaches in reaching human-centric outcomes. We call for a purpose-driven machine learning paradigm that is grounded in clinical theory and the sociotechnical realities of real-world operational contexts. We argue that understanding the utility of existing patient datasets requires looking in two directions: upstream towards the data generation, and downstream towards the automation objectives. This purpose-driven perspective to AI system development opens up new methodological opportunities and holds promise for AI automation of patient care.

This paper presents SYMBIOSIS, an AI-powered framework and platform designed to make Systems Thinking accessible for addressing societal challenges and unlock paths for leveraging systems thinking frameworks to improve AI systems. The platform establishes a centralized, open-source repository of systems thinking/system dynamics models categorized by Sustainable Development Goals (SDGs) and societal topics using topic modeling and classification techniques. Systems Thinking resources, though critical for articulating causal theories in complex problem spaces, are often locked behind specialized tools and intricate notations, creating high barriers to entry. To address this, we developed a generative co-pilot that translates complex systems representations - such as causal loop and stock-flow diagrams - into natural language (and vice-versa), allowing users to explore and build models without extensive technical training. Rooted in community-based system dynamics (CBSD) and informed by community-driven insights on societal context, we aim to bridge the problem understanding chasm. This gap, driven by epistemic uncertainty, often limits ML developers who lack the community-specific knowledge essential for problem understanding and formulation, often leading to ill informed causal assumptions, reduced intervention effectiveness and harmful biases. Recent research identifies causal and abductive reasoning as crucial frontiers for AI, and Systems Thinking provides a naturally compatible framework for both. By making Systems Thinking frameworks more accessible and user-friendly, SYMBIOSIS aims to serve as a foundational step to unlock future research into responsible and society-centered AI that better integrates societal context by leveraging systems thinking frameworks and causal modeling methods. Our work underscores the need for ongoing research into AI's capacity to understand essential characteristics of complex adaptive systems - such as feedback processes and time delays - paving the way for more socially attuned, effective AI systems.

The Trolley Problem has inspired scores of psychology experiments, including MIT's Moral Machine,1 an online survey where people had to decide what a self-driving car should do in case of an impending accident. Participants were given a series of pairs of scenarios, presented as map-like diagrams, with various numbers and types of pedestrians and passengers. For each pair of scenarios, they had to choose between options such as driving ahead and killing pedestrians, or veering into an obstacle and killing passengers. Based on 40 million responses from more than 200 countries, they found general preferences, such as sparing humans over animals. They also found differences between cultures. People from countries with collectivistic cultures prefer sparing lives of older people instead of the lives of younger people.

The present study explored managerial and instructor perceptions of their freshly employed cybersecurity workers' or students' preparedness to work effectively in a changing cybersecurity environment that includes AI tools. Specifically, we related perceptions of technical preparedness to ethical, systems thinking, and communication skills. We found that managers and professors perceive preparedness to use AI tools in cybersecurity to be significantly associated with all three non-technical skill sets. Most important, ethics is a clear leader in the network of relationships. Contrary to expectations that ethical concerns are left behind in the rush to adopt the most advanced AI tools in security, both higher education instructors and managers appreciate their role and see them closely associated with technical prowess. Another significant finding is that professors over-estimate students' preparedness for ethical, system thinking, and communication abilities compared to IT managers' perceptions of their newly employed IT workers.

The emergence of AI tools in cybersecurity creates many opportunities and uncertainties. A focus group with advanced graduate students in cybersecurity revealed the potential depth and breadth of the challenges and opportunities. The salient issues are access to open source or free tools, documentation, curricular diversity, and clear articulation of ethical principles for AI cybersecurity education. Confronting the "black box" mentality in AI cybersecurity work is also of the greatest importance, doubled by deeper and prior education in foundational AI work. Systems thinking and effective communication were considered relevant areas of educational improvement. Future AI educators and practitioners need to address these issues by implementing rigorous technical training curricula, clear documentation, and frameworks for ethically monitoring AI combined with critical and system's thinking and communication skills.

This exploratory study invesBgates the potenBal of the arBficial intelligence tool, ChatGPT, to support systems thinking (ST) in various subjects. Using both general and subject-specific prompts, the study assesses the accuracy, helpfulness, and reliability of ChatGPT's responses across different versions of the tool. The results indicate that ChatGPT can provide largely correct and very helpful responses in various subjects, demonstraBng its potenBal as a tool for enhancing ST skills. However, occasional inaccuracies highlight the need for users to remain criBcal of ChatGPT's responses. Despite some limitaBons, this study suggests that with careful use and aRenBon to its idiosyncrasies, ChatGPT can be a valuable tool for teaching and learning ST. In today's increasingly complex world, systems thinking (ST) emerges as an invaluable skill to equip our students with. It fosters a broader perspecBve, encouraging individuals to recognize the interconnectedness and complexity of various phenomena, thereby enhancing their understanding of the world and enabling more effecBve acBons (Binkley et al., 2012; Yoon et al., 2017). Complex situaBons, ranging from ecological problems to economic issues, social relaBonships, and health concerns, o]en confront even children. Successfully navigaBng these situaBons requires managing various aspects: percepBon, evaluaBon, understanding, consideraBon of alternaBves, decision-making, taking acBon, and reflecBon. Children o]en develop their own explanaBons and build knowledge from real-life experiences, even in the absence of formal educaBon (Arndt & Kopp, 2017).

The way you think about a problem and the conceptual process you go through to find a solution may be guided by your personal skills or the type of problem at hand. Many mental models exist representing a variety of thinking patterns -- and as a Data Scientist, appreciating different…

We propose a vision for directing research and education in the ICT field. Our Smart and Sustainable World vision targets at prosperity for the people and the planet through better awareness and control of both human-made and natural environment. The needs of the society, individuals, and industries are fulfilled with intelligent systems that sense their environment, make proactive decisions on actions advancing their goals, and perform the actions on the environment. We emphasize artificial intelligence, feedback loops, human acceptance and control, intelligent use of basic resources, performance parameters, mission-oriented interdisciplinary research, and a holistic systems view complementing the conventional analytical reductive view as a research paradigm especially for complex problems. To serve a broad audience, we explain these concepts and list the essential literature. We suggest planning research and education by specifying, in a step-wise manner, scenarios, performance criteria, system models, research problems and education content, resulting in common goals and a coherent project portfolio as well as education curricula. Research and education produce feedback to support evolutionary development and encourage creativity in research. Finally, we propose concrete actions for realizing this approach.

In all the Singularity-based angst over whether robots are going to take over, few have considered the human qualities that might allow our silicon cousins to prevail. Specifically, will robots dupe us into doing something dumb or dangerous because we trust them too much? PCMag discussed this recently with robotics expert Dr. Ayanna Howard after her keynote at the first IEEE Multi-Robot Systems (MRS) conference. Howard spent 12 years at NASA JPL as a Senior Robotics Researcher but is now the founder and CTO of Zyrobotics, which creates advanced technology to assist children living with disabilities. Here are edited and condensed excerpts from our conversation.

Ashok conducts research into human-centered computing, artificial intelligence and cognitive science, with a focus on computational design, discovery, and creativity. The goals of his research are to understand human creativity in conceptual design of complex systems as well as scientific problem solving, to develop interactive tools for aiding people in such creative tasks, and to invent computational systems that are themselves creative. His research explores analogical thinking, systems thinking, visual thinking, and meta-thinking as fundamental processes of design, discovery and creativity. His current projects investigate analogical thinking and systems thinking in biologically inspired engineering design, visual thinking on intelligence tests, and meta-thinking in game-playing agents. Related project focuse on systems thinking and meta-thinking in learning about ecological systems, and analogical thinking and systems thinking in learning about biologically inspired design.