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1. Imagine a more ethical AI: Using stories to develop teens’ awareness and understanding of artificial intelligence and its societal impacts

   Forsyth, S. ; Walsh, B. ; Dalton, B. ; Haberl, E. ; Smilack, J ; Yeh, T. ( January 2021 , RESPECT 2021 virtual conference; Annual Conference on Research in Equity and Sustained Participation in Engineering, Computing, and Technology)

   null (Ed.)

   Artificial intelligence (AI) tools and technologies are increasingly prevalent in society. Many teens interact with AI devices on a daily basis but often have a limited understanding of how AI works, as well as how it impacts society more broadly. It is critical to develop youths’ understanding of AI, cultivate ethical awareness, and support diverse youth in pursuing computer science to help ensure future development of more equitable AI technologies. Here, we share our experiences developing and remotely facilitating an interdisciplinary AI ethics program for secondary students designed to increase teens’ awareness and understanding of AI and its societal impacts. Students discussed stories with embedded ethical dilemmas, engaged with AI media and simulations, and created digital products to express their stance on an AI ethics issue. Across four iterations in formal and informal settings, we found students to be engaged in AI stories and invested in learning about AI and its societal impacts. Short stories were effective in raising awareness, focusing discussion and supporting students in developing a more nuanced understanding of AI ethics issues, such as fairness, bias and privacy. 

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2. BUILDING BLOCKS FOR UNDERSTANDING ARTIFICIAL INTELLIGENCE: DESIGNING INTERACTIVE AI LEARNING EXPERIENCES FOR YOUNG CHILDREN AND THEIR FAMILIES IN A MUSEUM SETTING

   Greenwald, E ; Cavero, D ; Grindstaff, K ; Krakowski, A ; Hurt, T ; Wang, N ( July 2024 , 16th annual International Conference on Education and New Learning Technologies)

   As artificial intelligence (AI) profoundly reshapes our personal and professional lives, there are growing calls to support pre-college aged youth as they develop capacity to engage critically and productively with AI. While efforts to introduce AI concepts to pre-college aged youth have largely focused on older teens, there is growing recognition of the importance of developing AI literacy among younger children. Today’s youth already encounter and use AI regularly, but they might not yet be aware of its role, limitations, risks, or purpose in a particular encounter, and may not be positioned to question whether it should be doing what it’s doing. In response to this critical moment to develop AI learning experiences that can support children at this age, researchers and learning designers at the University of California’s Lawrence Hall of Science, in collaboration with AI developers at the University of Southern California’s Institute for Creative Technologies, have been iteratively developing and studying a series of interactive learning experiences for public science centers and similar out-of-school settings. The project is funded through a grant by the National Science Foundation and the resulting exhibit, The Virtually Human Experience (VHX), represents one of the first interactive museum exhibits in the United States designed explicitly to support young children and their families in developing understanding of AI. The coordinated experiences in VHX include both digital (computer-based) and non-digital (“unplugged”) activities designed to engage children (ages 7-12) and their families in learning about AI. In this paper, we describe emerging insights from a series of case studies that track small groups of museum visitors (e.g. a parent and two children) as they interact with the exhibit. The case studies reveal opportunities and challenges associated with designing AI learning experiences for young children in a free-choice environment like a public science center. In particular, we focus on three themes emerging from our analyses of case data: 1) relationships between design elements and collaborative discourse within intergenerational groups (i.e., families and other adult-child pairings); 2) relationships between design elements and impromptu visitor experimentation within the exhibit space; and 3) challenges in designing activities with a low threshold for initial engagement such that even the youngest visitors can engage meaningfully with the activity. Findings from this study are directly relevant to support researchers and learning designers engaged in rapidly expanding efforts to develop AI learning opportunities for youth, and are likely to be of interest to a broad range of researchers, designers, and practitioners as society encounters this transformative technology and its applications become increasingly integral to how we live and work. 

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3. Ethics in Artificial Intelligence Education: Preparing Students to Become Responsible Consumers and Developers of AI.

   Zhang, H. ; Lee, I. ; Moore, K. ; Ahmad Shah, S. ( June 2023 , Proceedings of the 2023 ASEE Annual Conference & Exposition.)

   The rapid expansion of Artificial Intelligence (AI) necessitates a need for educating students to become knowledgeable of AI and aware of its interrelated technical, social, and human implications. The latter (ethics) is particularly important to K-12 students because they may have been interacting with AI through everyday technology without realizing it. They may be targeted by AI generated fake content on social media and may have been victims of algorithm bias in AI applications of facial recognition and predictive policing. To empower students to recognize ethics related issues of AI, this paper reports the design and implementation of a suite of ethics activities embedded in the Developing AI Literacy (DAILy) curriculum. These activities engage students in investigating bias of existing technologies, experimenting with ways to mitigate potential bias, and redesigning the YouTube recommendation system in order to understand different aspects of AI-related ethics issues. Our observations of implementing these lessons among adolescents and exit interviews show that students were highly engaged and became aware of potential harms and consequences of AI tools in everyday life after these ethics lessons. 

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4. American public opinion on artificial intelligence in healthcare

   https://doi.org/10.1371/journal.pone.0294028  

   Rojahn, Jessica ; Palu, Andrea ; Skiena, Steven ; Jones, Jason J. ( November 2023 , PLOS ONE)

   Mahmoud, Ali B. (Ed.)

   Billions of dollars are being invested into developing medical artificial intelligence (AI) systems and yet public opinion of AI in the medical field seems to be mixed. Although high expectations for the future of medical AI do exist in the American public, anxiety and uncertainty about what it can do and how it works is widespread. Continuing evaluation of public opinion on AI in healthcare is necessary to ensure alignment between patient attitudes and the technologies adopted. We conducted a representative-sample survey (total N = 203) to measure the trust of the American public towards medical AI. Primarily, we contrasted preferences for AI and human professionals to be medical decision-makers. Additionally, we measured expectations for the impact and use of medical AI in the future. We present four noteworthy results: (1) The general public strongly prefers human medical professionals make medical decisions, while at the same time believing they are more likely to make culturally biased decisions than AI. (2) The general public is more comfortable with a human reading their medical records than an AI, both now and “100 years from now.” (3) The general public is nearly evenly split between those who would trust their own doctor to use AI and those who would not. (4) Respondents expect AI will improve medical treatment but more so in the distant future than immediately.

    

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5. Socially situated artificial intelligence enables learning from human interaction

   https://doi.org/10.1073/pnas.2115730119  

   Krishna, Ranjay ; Lee, Donsuk ; Fei-Fei, Li ; Bernstein, Michael S. ( September 2022 , Proceedings of the National Academy of Sciences)

   Regardless of how much data artificial intelligence agents have available, agents will inevitably encounter previously unseen situations in real-world deployments. Reacting to novel situations by acquiring new information from other people—socially situated learning—is a core faculty of human development. Unfortunately, socially situated learning remains an open challenge for artificial intelligence agents because they must learn how to interact with people to seek out the information that they lack. In this article, we formalize the task of socially situated artificial intelligence—agents that seek out new information through social interactions with people—as a reinforcement learning problem where the agent learns to identify meaningful and informative questions via rewards observed through social interaction. We manifest our framework as an interactive agent that learns how to ask natural language questions about photos as it broadens its visual intelligence on a large photo-sharing social network. Unlike active-learning methods, which implicitly assume that humans are oracles willing to answer any question, our agent adapts its behavior based on observed norms of which questions people are or are not interested to answer. Through an 8-mo deployment where our agent interacted with 236,000 social media users, our agent improved its performance at recognizing new visual information by 112%. A controlled field experiment confirmed that our agent outperformed an active-learning baseline by 25.6%. This work advances opportunities for continuously improving artificial intelligence (AI) agents that better respect norms in open social environments. 

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