Review of Algorithmic Rights and Protections for Children Narongsaksakul, W. (2024, December 11). Review of Algorithmic rights and protections for children by M. Ito, R. Cross, R., K. Dinakar & C. Odgers. Education Review, 31. https://doi.org/10.14507/er.v31.3799 December 11, 2024 ISSN 1094-5296 Ito, M., Cross, R., Dinakar, K., & Odgers, C. (2023). Algorithmic rights and protections for children. MIT Press. Pp. 292 ISBN: 978-0-262-54548-8 Reviewed by Watsachol Narongsaksakul Illinois State University United States Algorithmic Rights and Protections for Children is part of a series of publications written by Connected Learning Alliance scholars (2020) addressing algorithms and platforms for children through a power, justice, and equity lens. In a data-driven society, predictive systems and artificial intelligence (AI) functions performing human-like tasks are embedded in digital platforms such as learning management systems, website third-party cookies, and social media. Data including numbers, characters, symbols, images, electromagnetic recorded, sorted, or commodified is not equally represented (e.g., gender, race, power, identity) by governments and private companies, causing discrimination and preferential treatment by identified technologies. The book’s authors, Ito, Cross, Dinakar, and Odger, address a range of audiences: children, educators, caregivers, researchers, policymakers, social workers, and instructional designers who shape evolving relationships in the connected learning ecology with productive algorithms. An understanding of children’s algorithmic rights mitigates ethical dilemmas, including conflicts between the educator autonomy and data-driven decisions on account of systemic biases; children’s guided play, data surveillance, and well-being; policymakers seeking to be proactive about how data is used, considering data privacy and personally identifiable information impacting children’s lives; and researchers and instructional designers wanting to balance engagement with social sectors in educational systems. This book contains 11 chapters, divided into two main sections: section I covers perspectives on balancing protection and children’s rights in K–12 settings and unequal childhoods of children's relationships with algorithms, digital data, and platforms, and section II presents empirical research reporting humanistic design, experience, and discourse of educational technologies. In the first section, significant Education Review /Reseñas Educativas/Resenhas Educativas 2 concerns related to children's protection and rights (Chapter 1) are addressed: (1) relationships between algorithms, culture, and society (students reflect on digital algorithmic structures and biases); (2) the needs and positionality of children (i.e., age-appropriate data and digital privacy); and (3) inequality in children's risks and opportunities (social media, digital games, and learning technologies reinforce unequal childhoods, called “algorithms of oppression” [Noble, 2018]). One interesting point raised in this volume is media mentorship (Chapter 3) and how to navigate online settings and cultivate positive and productive lifelong learning skills in safe spaces, where algorithms, privacy, and security become pivotal agents in global internet discourse about history, worldviews, and policy. Despite their assumed duties for “responsibilization” (p. 35), parents feel unqualified to operate and keep students informed about making critical decisions in the digital world. Thus, parents are obliged for protectionism and assistance for students in activating knowledge construction and open lines of communication, evolving children, media, and technology, and advocating for their rights. Some emerging media regulations in the data-rich environment include the Children's Online Privacy Protection Act (COPPA) and the Children's Television Act. Moving away from traditional media, digitized terms, such as user metadata with cultural depictions, are considered nonlinear with the more personalized tools and demands for transparency and accountability (Chapter 4). Aside from parental engagement, the authors bring attention to professional ethics in human services. Ito et al. illustrate examples of how AI—acting as a psychological tool—can transform into a violence prevention system with the potential for identifying and predicting online content called “decipher context” (p. 43; Chapter 5). Nevertheless, AI is still required to understand language, cultural nuances, and social context, and the digital path reinforces an AI algorithmic system of marginalization and oppression in data science within the context of social work. Sociocultural impacts of AI include leveraging domain expertise and promoting social cohesion, but there are risks. In India, for example, biometric IDs are linked to social media and data-driven digital marketing according to caste, gender, sexuality, and religion. In addition, the Automated Facial Recognition System (AFRS) software to screen crowds at political rallies and protests will be extended to identify criminals. In Israel, unrestricted surveillance by the central law enforcement agency violates fundamental human rights in the prevention of cyberbullying, shaming, and sexual predation by monitoring negative social media posts. In the US, misuse of intelligence analysis in the form of racial threats and conservations is identified aggressively with peers. Several organizations, for instance, the Association of Social Work Boards, the Council on Social Work Education, and the Clinical Social Work Association developed standards to consider technology's role in social work practice and education. The standards mentioned cover four main areas: (1) providing information for the public, (2) designing and delivering services, (3) gathering and managing information about a client, and (4) educating and supervising students. In the second section, qualitative insights and expertise are used to consider and anticipate potential challenges of AI deployment. Based on the study about Tega, a Review of Algorithmic Rights and Protections for Children 3 relational robot designed to form relationships with humans, there are growing concerns about “deceptive relationships” (p. 99) that are inauthentic and unfamiliar forms in everyday life (Chapter 7). Adolescents reported lower privacy concerns and greater use of protective strategies for personal data, while adults had higher privacy concerns because of their lack of knowledge of online privacy strategies. Dataveillance is associated with exploiting and commercializing children's play or social networking data as a capitalist commodity, which affects youth in terms of their autonomy, exploration, self-expression, and peer connectedness (Chapter 8). Personalized learning technologies can be considered to both reproduce and remedy educational inequities as their development depends on algorithms and automation to replace teachers’ tasks in classrooms (Chapter 9). Access to AI also shapes interactions between parents and young children. Emerging forms of literacy occur from patterns to ignite change in how children spend time in microsystem in their natural or virtual spaces. Algorithmic literacy has influenced perspectives in education, media, and cultural production for transformative education (Chapter 2). With the change and continued concerns of ethical textual experience, a new approach to representing the world and the relationship between humans and robots is reflected in this book. These shifts from analog to digital enable different ways of communicating and sharing through various interfaces of connected learning with algorithms for student repertoires based on their cross-cultural borders to support their use of technology with parental engagement. The ecological perspective informs theories of parental mediation and joint media engagement within the AI literacy framework. Bronfenbrenner's (1994) learning ecology (Chapter 10) details a nested system consisting of various levels: • Macrosystem factors, including social and cultural values. • Exosystem factors: including technology infrastructure and policies. • Mesosystem factors, including community centers, libraries, and schools. • Microsystem factors, including families, peers, siblings, and neighbors. In participatory design (PD), also known as human-centered design and inclusive design, family activities, literacy questions, and design dimensions were explained in the dimensions of ask, adapt, and analyze. The results revealed that human-machine interactions developed racism, sexism, and discrimination. The authors urge parents to help foster their children as active consumers of AI across the proposed nested layers in diverse populations for more inclusive and democratizing access to AI. Several strengths of this book bring critical awareness of technological constraints. To begin with, the authors suggested that underage children are unprotected from the collection and disclosure of personal data. These elements apply a technocentric approach to learning in constructing narratives mediated by youth experiences of visuality and digitality to accommodate student needs in the shape of digital media democracy and justice in the student learning process (Chapter 6). One example is Scratch, where algorithmic literacies are classified as a subset of computational literacies. For this reason, algorithmic discrimination is a new challenge Education Review /Reseñas Educativas/Resenhas Educativas 4 articulated by generative systems and platforms that stand in contrast to the community's values. Overall, the concept of algorithmic rights is viewed as a social and cultural movement related to AI technologies that can inspire critical thoughts and actions in contemporary cultures. This book brings ecological perspectives utilizing real-world data shaping participatory cultures in school settings, after-school programs, community-based organizations, and other youth center spaces. The authors provide a broader context of how technology reflects equity, full participation, and social connection, particularly parental involvement and social work. Consequently, the nature of education molds school learning according to what technology is capable of accomplishing. References Connected Learning Alliance. (2020, February 29). Connected learning guide. https://clalliance.org/resources/connected-learning-guide/ Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism New York University Press. About the Reviewer Watsachol Narongsaksakul is a lecturer in ELT, curriculum developer, and SDG advocate. She is a doctoral student and graduate research assistant in the School of Teaching and Learning, College of Education, Illinois State University. She also serves as a member of Awards Professional Council of the TESOL International Association (2021-2024), Social Media Chair of Illinois Teachers of English to Speakers of Other Languages & Bilingual Education Association (ITBE), and a member of the Global Doctoral Consortium of the International Council for Open and Distance Education (ICDE). Education Review/Reseñas Educativas/Resenhas Educativas is supported by the Scholarly Communications Group at the Mary Lou Fulton Teachers College, Arizona State University. Copyright is retained by the first or sole author, who grants right of first publication to the Education Review. Readers are free to copy, display, distribute, and adapt this article, as long as the work is attributed to the author(s) and Education Review, the changes are identified, and the same license applies to the derivative work. More details of this Creative Commons license are available at https://creativecommons.org/licenses/by-sa/4.0/. Disclaimer: The views or opinions presented in book reviews are solely those of the author(s) and do not necessarily represent those of Education Review. https://creativecommons.org/licenses/by-sa/4.0