GRACE: Global Review of AI Community Ethics
GRACE: Global Review of AI Community Ethics is a new peer-reviewed, international journal at Stanford University. An open-access journal, indexed in Google Scholar, GRACE offers a unique intellectual forum for AI Ethics practitioners to share their work. OUR FOURTH issue is now LIVE Vol. 4 No. 1 (2026): Governing the Generative Age: Student Policy Proposals on Artificial Intelligence This volume of GRACE Journal presents our first installment of original student-authored policy proposals addressing one of the defining governance challenges of our time: how to responsibly regulate artificial intelligence across domains that directly shape human life. Stanford student contributors examine AI’s implications for autonomous transportation, healthcare, education, military applications, creative labor, and copyright law. Written for policymakers, technologists, and the public alike, these memos demonstrate how emerging leaders translate ethical principles into concrete regulatory frameworks. Collectively, the volume models how careful, evidence-driven student work can meaningfully inform real-world AI governance debates at local, national, and global levels. Future volumes include global perspectives from Stanford and beyond.
Copyright (c) 2026 Muhammad Khattak, Maryam Khalil, Sayo Stefflbauer, Alex Nguyen
Artificial intelligence is now deeply embedded in academic life, yet university governance frameworks have struggled to keep pace with rapid changes in AI capabilities and student adoption. This policy memo examines how generative AI is currently used by students, faculty, parents, industry representatives, and administrators at Stanford University and evaluates whether existing institutional policies adequately reflect these practices. Drawing on a review of Stanford’s 2023 Generative AI Policy Guidance, comparative analysis of peer institutions, and twenty semi-structured stakeholder interviews, the memo identifies persistent gaps related to disclosure, academic integrity, accuracy, bias, data protection, and faculty autonomy. The findings show that while stakeholders view AI as a valuable efficiency and learning tool, they consistently report risks related to weakened critical thinking, hallucinations, privacy leakage, bias amplification, and academic misconduct. Current policy relies heavily on course-level discretion and informal guidance, producing uncertainty and uneven enforcement across departments. To address these gaps, the memo recommends the creation of a Stanford “Code of Practice for the Use of AI” centered on transparency, data protection, assessment design, and iterative oversight. It further proposes faculty-specific and student-specific governance measures, proportional enforcement mechanisms, and the establishment of a standing AI governance framework to support continuous policy updating. These recommendations aim to preserve faculty autonomy while establishing clear, institution-wide standards for responsible, transparent, and effective use of generative AI in higher education.
Copyright (c) 2026 Sergio Escobar, Elena Kim, Silvia Lambardo, Jenin Al Shalabi
A National Framework for Regulating AI in the Health Insurance Space
*]:pointer-events-auto scroll-mt-(--header-height)" dir="auto" tabindex="-1" data-turn-id="b21723db-afdf-4990-81ad-edc65142576e" data-testid="conversation-turn-23" data-scroll-anchor="false" data-turn="user"> This memo proposes a national regulatory framework to govern the use of artificial intelligence in health insurance coverage determinations, with a focus on utilization management and prior authorization. Insurers increasingly deploy predictive tools to estimate recovery time, length of stay, and medical necessity, yet the logic, criteria, and limitations of these systems are often opaque to patients and clinicians. Drawing on evidence of high-volume prior authorization use, documented error and reversal rates, and appeal timelines that frequently outlast clinically relevant windows, the memo argues that existing oversight is fragmented across plan types and agencies and is not calibrated to the speed and scale of AI-influenced denials. Building on California’s SB 1120 as a model, the paper advances a care-centered “AI in Healthcare Bill of Rights” organized around three enforceable pillars: (1) transparent disclosure whenever AI influences an adverse determination; (2) documented accountability by a licensed clinician for any denial, delay, reduction, or termination of coverage relative to the treating clinician’s request; and (3) independent audit and validation of both tools and decision workflows, including audit-ready logging and disparity monitoring. The memo recommends an ERISA amendment to establish a uniform federal floor for these requirements, complemented by coordinated federal and state implementation and enforcement. It concludes that targeted guardrails can preserve the legitimate administrative benefits of predictive tools while protecting patient autonomy, reducing inappropriate denials, and restoring trust in coverage decision-making. This paper was submitted to Dr. Cynthia Bailey's CS121 Equity and Governance for Artificial Intelligence.
Copyright (c) 2026 Anna Patrick, Dwija Adamala
Standardized Autonomous Vehicle Evaluation and Deployment (SAVED) Framework
Autonomous vehicles are rapidly expanding on U.S. roads, but oversight has not kept pace. Today’s patchwork of federal guidance and state-by-state rules leaves uneven safety expectations, inconsistent reporting, and public trust problems. This memo proposes the Standardized Autonomous Vehicle Evaluation and Deployment (SAVED) framework: a national safety baseline with state flexibility. SAVED establishes enforceable federal performance standards for safety and cybersecurity, requires independent pre-deployment evaluation (including simulation and controlled on-road testing within the vehicle’s operating domain), and creates standardized incident reporting to a national database. It also authorizes time-limited state “sandbox zones” for tightly monitored pilots and builds in equity and accessibility expectations so benefits and risks are not concentrated in only a few communities. This policy memo was originally submitted to Dr. Cynthia Bailey's CS 121: Equity and Governance for Artificial Intelligence
Health Professional Shortage Areas (HPSAs)
Medically underserved and rural communities face persistent provider shortages, infrastructure constraints, and longstanding gaps in access to care. Artificial intelligence and large language models are increasingly proposed as tools to expand capacity through triage support, telemedicine, and workflow augmentation. Yet these settings also face heightened risk from biased model performance, limited clinical oversight, and fragile institutional trust. This policy memorandum distills findings from a policy-minded research review and identifies the conditions under which AI systems can be deployed to address access gaps without compounding inequities. It argues that ethical deployment in Health Professional Shortage Areas requires three linked safeguards. First, equity must be treated as a prerequisite for scale, including subgroup performance reporting, bias risk assessment, and ongoing monitoring in the intended population. Second, adoption depends on trust-building measures that preserve relational care, provide patient-facing explanations, and establish credible privacy protections and community feedback channels. Third, accountability requires clear governance rules for consent, permitted uses, and data retention, alongside liability clarity that distinguishes attended from unattended systems and specifies responsibility across clinicians, health systems, and vendors. The memo concludes that AI can support care access in underserved settings only when technical validation, culturally responsive implementation, and enforceable regulatory protections are aligned.
Regulating LLMs in Warfare: A U.S. Strategy for Military AI Accountability
Large language models (LLMs) are rapidly entering military workflows that shape intelligence synthesis, operational planning, logistics, cyber operations, and information activities, yet U.S. governance has not kept pace with their distinct risk profile. This memo argues that existing frameworks remain ill-suited to LLM-enabled decision-support: international efforts under the UN Convention on Certain Conventional Weapons focus primarily on lethal autonomous weapons, while U.S. policy relies on high-level ethical principles that have not been operationalized into enforceable requirements for evaluation, monitoring, logging, and lifecycle control. The paper identifies four core risks arising from LLM deployment in high-consequence contexts: inadvertent escalation driven by overconfident or brittle recommendations under uncertainty; scalable information operations and disinformation; expanded security vulnerabilities including data poisoning, prompt-injection, and sensitive-data leakage; and accountability gaps when human actors defer responsibility to opaque model outputs. In response, the memo proposes a U.S. regulatory framework organized around four pillars: (1) human decision rights and escalation controls, including documented authorization for crisis-sensitive uses; (2) mandatory human review and traceability for information-operations content; (3) baseline security, data governance, and continuous adversarial testing for training and deployment pipelines; and (4) accountability mechanisms, including auditable logs and incident reporting overseen by an independent Military AI Oversight Committee. The memo concludes that LLM-specific guardrails complement, rather than displace, existing weapons autonomy policy and would strengthen U.S. credibility in shaping international norms for responsible military AI. This paper was submitted to Dr. Cynthia Bailey's course CS121 Equity and Governance for Artificial Intelligence, Stanford University
A Transparency-Based Approach to Regulating the Resource Footprint of U.S. Data Centers
Artificial intelligence systems increasingly rely on large-scale data center infrastructure that consumes substantial electricity and water, with impacts concentrated in rural and suburban regions where facilities cluster near land and transmission corridors. Yet in the United States, resource-consumption disclosures remain fragmented across voluntary corporate reporting and limited, uneven state requirements, leaving policymakers and communities without consistent facility-level data to evaluate cumulative grid stress, water scarcity risk, and emissions trajectories. This paper proposes a transparency-first federal policy that mandates standardized annual reporting of key resource metrics, including electricity consumption by source, total water use, renewable energy share, and reuse of waste heat and water, for covered data centers with capacity of at least 500 kilowatts operated by U.S.-listed firms and U.S.-based private corporations. Reports would be submitted through a joint Department of Energy and Securities and Exchange Commission portal and aggregated into a public database to enable third-party verification and reduce greenwashing. Because hyperscale facilities drive disproportionate demand, the proposal requires annual accredited third-party audits for hyperscale data centers, with unredacted audit copies retained by regulators and redacted versions published publicly. Enforcement relies on civil penalties calibrated to severity and firm size, leveraging existing SEC authority for public companies and Department of Energy enforcement for covered private firms, while minimizing disclosure of proprietary information. By establishing a uniform national reporting floor, the proposal enables evidence-based future regulation, improves investor and community oversight, and supports sustainable growth in U.S. AI infrastructure. This paper was submitted Dr. Cynthia Bailey's CS121 course: Equity and Governance for Artificial Intelligence and it won the 2025 Eric Roberts Prize for Tech Ethics and Policy.
Copyright (c) 2026 Anshika Agarwal, Emma Beharry
Facial Recognition Used in Suspect Identification
Facial recognition is increasingly used by U.S. law enforcement agencies to generate investigative leads and identify criminal suspects. While proponents emphasize efficiency and public safety benefits, federal reviews and empirical testing show that these systems exhibit uneven performance across demographic groups and operational conditions, and that weak procedural controls can allow probabilistic matches to function as de facto evidence. A growing number of wrongful arrests linked to facial recognition illustrate a recurring governance failure: low-quality or misinterpreted matches are treated as determinative without adequate corroboration, disclosure, or accountability. Despite these risks, the United States lacks a uniform federal baseline governing how facial recognition may be used for suspect identification, how such use must be documented and disclosed, and how agencies should be audited for compliance and performance. This paper proposes a mechanism-grounded federal framework for regulating facial recognition used in suspect identification, built around three pillars: disclosure, accountability, and independent auditing. It recommends conditioning key Department of Justice law-enforcement grants on agency compliance with standardized requirements for case-level documentation, court disclosure, corroboration of facial recognition leads, supervisory sign-off, and operator training. It further proposes a national audit protocol aligned with NIST testing concepts and Government Accountability Office findings, coupled with a public reporting database maintained by the Department of Justice. By grounding regulatory authority in existing grant mechanisms and standardized reporting and audit procedures, the framework establishes a realistic and enforceable federal floor of safeguards. The proposed approach preserves facial recognition as an investigative lead tool while reducing the risk of wrongful arrest, civil-rights violations, and unaccountable reliance on probabilistic identification technologies.
Protecting Student Data in the Age of AI
Universities increasingly rely on learning platforms, analytics tools, and AI-enabled services that generate student activity telemetry and derived inferences like engagement metrics and risk flags. Because much of this platform-derived and AI-related data is not consistently treated as part of the formal “education record,” it can fall into a regulatory gray zone that weakens transparency, limits on secondary use, retention controls, and accountability as data move through vendors, APIs, and integrated tools. This memo proposes a federal governance framework that shifts from record-based compliance to lifecycle oversight of student-data pipelines, including standardized definitions, consolidated disclosures, enforceable purpose limits (covering de-identified and derived data), mandatory logging and retention rules, and independent audits of high-risk vendor and institutional systems. The framework builds on a CS182W ethical critique of consent- and anonymization-based data-sale proposals by translating autonomy and intellectual-freedom concerns into auditable, implementable requirements.
Copyright (c) 2026 Sherlock Jr Langevine
Artificial intelligence is increasingly used to screen applicants, rank candidates, evaluate performance, and support termination decisions. While these tools promise efficiency, they also scale risks that are difficult for workers and applicants to detect or contest, including discrimination, opaque decision logic, and error at high volume. Existing civil-rights and labor protections remain essential, but their effectiveness depends on transparency and technical capacity that many automated systems do not provide. At the same time, emerging state and local regulations create a fragmented landscape with uneven protections and inconsistent compliance requirements. This memo proposes the creation of a federal regulator, the Automated Employment Bureau (AEB), housed within the Department of Labor to establish baseline national standards for AI systems used in employment decisions. The proposed framework centers on four core functions: standards and certification, independent audits and ongoing monitoring, transparency and notice requirements, and enforceable redress and enforcement mechanisms. By pairing technical oversight with civil-rights enforcement and periodic updating, AEB would reduce discriminatory outcomes, strengthen due process, and provide consistent national expectations while preserving space for responsible innovation.
Copyright (c) 2026 Kwame Nyarkoh-Ocran, Chijioke Mgbahurike
Artificial Intelligence (AI) has become a fundamental part of everyday life, yet most Americans remain unfamiliar with its workings, risks, and ethical implications. This policy memo proposes a federally mandated AI-literacy curriculum for all U.S. K-12 schools to bridge that knowledge gap. The policy requires every public school to deliver a 30- to 45-minute AI-literacy module at the start of each academic year as a condition of continued federal funding. Developed and annually updated by a Department of Education–appointed board of AI researchers, educators, and K-12 teachers, successive modules will scaffold age-appropriate lessons on AI capabilities, limitations (e.g., hallucinations, bias), and responsible use. A brief end-of-module survey will track students’ comprehension and AI usage, informing improvements over each iteration. This memo reviews existing federal and state initiatives and finds them overly broad, voluntary, or piecemeal. By contrast, the proposed policy establishes nationwide, enforceable standards that ensure nearly all Americans receive foundational AI education. The plan leverages federal funding incentives, mirroring historical precedents in education policy, to guarantee adoption. Key beneficiaries include students, parents, educators, higher-education institutions, employers, and technology firms, though opposition may arise from stakeholders that are against federal mandates in the field of education. Ultimately, this memo argues that mandatory, repeatable AI-literacy training is essential for safeguarding the public in this new era of technology, fostering an informed population in an AI-driven future.
This issue of GRACE: Global Review of AI Community Ethics arrives at a critical juncture, as generative AI rapidly reshapes the boundaries of creative labor, authorship, policymaking, and public life. From courtroom debates and copyright disputes to deeply personal accounts of disability, mental health, and surveillance, the contributions in this volume offer grounded, often urgent reflections on how AI-generated content and systems are altering the core of our shared human experience. We received more than 3,000 submissions for this issue, and we were moved by the breadth, originality, and thoughtfulness of the work. The selected pieces reflect our editorial commitment to uplifting community-based, student-led, and ethics-oriented perspectives on AI ethics. Most contributions come from undergraduate students, with the exception of “Who Holds the Camera?” co-authored by former GRACE editors Muhammad Khattak and Samuel Eli Cohen—now in graduate school—and Stanford staff member Kiyoshi Taylor. Their continued work affirms GRACE's mission to remain a platform for emerging thinkers and those who began here and continue to shape the field. In many of our editorial discussions we addressed concerns about censorship and trolling of student work that uses language of design justice and equity. Rather than filter those words to avoid trolls, we opted to affirm the rhetorical choices of our authors.
Copyright (c) 2025 Julia Kwak, Maryam Khalil, Sayo Stefflbauer, Onyothi Nekoto
Teaching Justice: Mitigating Bias in Machine Learning
Edited by Dr. Carlotta A. Berry and Dr. Brandeis Hill Marshall, two leading Black American women in STEM, the book addresses a longstanding gap in the field by offering a comprehensive and socially responsive guide to understanding and mitigating bias in artificial intelligence systems. Most of all, these two technologists offer a clear, technologically sound set of instructions for responsible model design and maintenance, accompanied by helpful charts and figures.
Interview with Maria Fernanda (Mafer) Velasquez
I’m a sophomore studying Electrical Engineering with a dream of developing sustainable technological solutions that truly matter for Peru—and hopefully expanding to other Latin American countries. My passion lies at the intersection of energy-efficient systems and high-performance computing, where I can design hardware and software architectures that optimize energy consumption without compromising performance.
Copyright (c) 2025 Maria Fernanda (Mafer) Velasquez
As large pre-trained language models grow prevalent, efforts in preventing biased and hateful outputs related to race and gender are increasingly critical. Since initiatives are scattered and fragmented, this review outlines the latest methods for measuring safe, ethical AI and discusses their limitations. By spotlighting the proper utilization and challenges of state-of-the-art methods, this review seeks to foster continuing discourse and innovation among both technical developers and non-technical policymakers.
Copyright (c) 2025 Johann Lee, Darynne Lee
In October 2023, 42 attorneys general filed lawsuits against Meta, alleging that its platforms are addictive and harmful to youth. These suits echo legal strategies used against Big Tobacco in the 1990s, particularly the Master Settlement Agreement and subsequent regulatory reforms. This paper examines the viability of applying tobacco regulation as a blueprint for regulating Big Tech, especially in light of mounting concerns about digital addiction and youth vulnerability. Through a comparative analysis of tobacco and social media industries, it identifies key parallels: both target young users, downplay known harms, and engineer user dependence. However, the paper also highlights critical differences—social media is not inherently carcinogenic and offers meaningful societal benefits, complicating direct regulatory transfer. Drawing on emerging literature on digital addiction, platform design, and public health risk, the paper evaluates whether elements of tobacco control—such as warning labels, youth advertising restrictions, and public education—can be adapted for the tech sector. It concludes that while tobacco regulation provides a powerful legal and rhetorical model, effective oversight of social technology requires a new regulatory infrastructure tailored to its unique risks. The “Big” blueprint should inspire, not constrain, the future of digital platform governance.
De-Identified Medical Datasets and the 2025 Readiness Gap:
Foundation models (FMs)—large-scale machine learning models trained on vast, diverse datasets—are reshaping the future of medical AI by powering diagnostic tools, clinical decision systems, and health information summarization. Sometimes referred to as large language models (LLMs) when applied to text, these models are increasingly deployed across clinical contexts. However, the de-identified datasets that form the backbone of FM training are often outdated, demographically limited, and difficult to access. These limitations raise profound concerns about fairness, scientific validity, and the potential for harm—particularly for marginalized populations underrepresented in training data. This paper argues that current de-identified datasets are not adequately representative or accessible for building trustworthy AI in healthcare. It critiques the prevailing assumption that de-identification alone ensures ethical readiness, showing instead how it can obscure structural biases and entrench inequality. Drawing on recent research and emerging technical and policy solutions—including synthetic data generation, automated de-identification, and global benchmarking—this paper explores what it means for datasets to be “2025-ready.” It proposes a new standard for responsible dataset design, grounded in demographic transparency, equity-centered governance, and inclusive participation in medical AI development.
This paper investigates the ethical, legal, and creative implications of generative AI in filmmaking, with a focus on how artists—particularly those from historically excluded communities—are navigating the rapid rise of machine-generated content. Using OpenAI’s GPT-4o "Ghibli-style" image controversy as a launch point, the paper traces the broader cultural and legal tensions surrounding generative AI’s use of copyrighted work without consent. Through historical parallels from photography and cinema to early AI storytelling systems, the paper explores how generative AI differs in its ambition to mechanize creativity itself. The authors examine recent industry flashpoints, including the 2023 WGA and SAG-AFTRA strikes, alongside new legal precedents and copyright lawsuits challenging data scraping and authorship rights. Drawing on emerging scholarship and official reports, including the January 2025 U.S. Copyright Office statement reaffirming the necessity of human authorship, the paper proposes a framework for accountable fair use and ethical AI implementation in filmmaking. The authors advocate for transparency, licensing reform, cultural authenticity audits, and investment in human-AI collaboration that augments rather than replaces human artistry. The paper concludes that while generative AI may enhance efficiency and idea generation, it cannot replicate the depth of lived experience, emotion, and cultural understanding that human creators bring to film. Ultimately, the question of “Who holds the camera?” remains central to the future of cinematic storytelling in the age of machine generated art.
Copyright (c) 2025 Mahammad Khattak, Samuel Eli Cohen, Kiyoshi Taylor
AI mental health chatbots could offer a promising solution to the care gap faced by underserved communities, especially during times of social isolation and health crisis. These tools provide round-the-clock, low-cost, and stigma-free support. Yet, this paper explores how current implementations face challenges that may limit their long-term efficacy and equitable impact. Drawing on recent empirical studies and ethical scholarship, I evaluate the capabilities and constraints of AI chatbots as short-term mental health supports and propose safeguards that promote inclusive, culturally relevant, and ethically responsible deployment. This study presents new empirical insights from a qualitative interview study with 18 low-income, first-generation community college students of color, whose reflections on chatbot use underscore the importance of trust, cultural resonance, and long-term engagement. Rather than comparing or critiquing specific products, this research centers community voices and advocates for collaborative, community-informed improvement. It also draws from an ethically sourced and protected dataset created by Dr. Harriett Jernigan at Stanford University, which provides a compelling counterexample of culturally specific chatbot design.
This review examines Unmasking AI: My Mission to Protect What Is Human in a World of Machines by Dr. Joy Buolamwini, a leading voice in AI ethics and founder of the Algorithmic Justice League. The reviewer positions Buolamwini’s work as both a critique of current AI practices and a call to action for inclusive, ethical, and interdisciplinary approaches to technology.
Unelected Digital Sovereigns? A Review of The Tech Coup
This review of The Tech Coup: How to Save Democracy from Silicon Valley by Marietje Schaake explores the author’s urgent argument that unregulated technology companies pose a grave threat to democratic institutions. Drawing on her experience as a European lawmaker and global tech policy advisor, Schaake critiques the unchecked power of Big Tech firms like Meta, Google, and Amazon, examining how their influence over information, governance, and global markets undermines accountability and democratic norms. The review highlights her call for stronger regulation, public awareness, and civic action, while commending the book’s clarity, authority, and practical roadmap for restoring democratic control in the digital age.
Copyright (c) 2025 Oluwakemisola Omolola Adeusi
Navigating the AI Hype: Why AI Snake Oil is Crucial for Interpreting Advancements and Inherent Limitations
In AI Snake Oil, authors Arvind Narayanan and Sayash Kapoor debunk AI hype with a cautious, informed perspective, equipping readers to do the same. As renowned computer scientists and TIME’s 100 Most Influential People in AI, they bring both expertise and clarity to a wide audience—including general readers, technical professionals, and policymakers. Using examples from spell-check to self-driving cars, they demonstrate how the definition of AI constantly shifts with new advancements. The authors address widespread misconceptions by providing tools to identify inflated claims and understand the limitations of both predictive and generative AI. By demystifying the technology, they offer a grounded and accessible guide to what AI can—and cannot—do.
Mark Coeckelbergh Wants “Democratic AI” to Build a Common World
In Why AI Undermines Democracy and What to Do About It, Mark Coeckelbergh challenges the prevailing notion of AI as politically neutral, asserting that it amplifies threats to democracy. He explores how AI furthers technocracy, authoritarianism, and centralized power in the hands of corporate elites. Moreover, he shows that as AI-driven algorithms increase polarization and misinformation, they ruin the civic culture essential to democracy, including a public sphere of trust, tolerance, and rational deliberation that produces consensus. However, Coeckelbergh envisions a future where AI enhances democracy rather than eroding it. He advocates for "democratic AI" — technologies designed to support public discourse, serve the common good, and foster a common world. Through this principle underlying tech design, in addition to civic education and stronger oversight of Big Tech, he outlines a pathway toward reclaiming AI for democracy. While there are unresolved tensions, such as between liberty and the common good, Coeckelbergh offers a compelling philosophical framework to inspire actionable reforms.
Ethics of Care in the Era of Algorithms: Giovanni Rubeis’ Ethical Blueprint for Medical AI
Giovanni Rubeis’ Ethics of Medical AI offers a comprehensive analysis of the ethical challenges posed by integrating artificial intelligence into healthcare. The book examines algorithmic bias, depersonalization, and technological reductionism, while advocating for empathy, trust, and equity in clinical practice. Framing Medical AI within broader social and institutional contexts, Rubeis challenges the belief that technology alone can resolve systemic healthcare disparities. Instead, he emphasizes the importance of humanistic values and narrative medicine. With seven actionable lessons for stakeholders, the book provides practical guidance for ensuring transparency, accountability, and fairness. Rubeis’ interdisciplinary approach makes this a vital resource for healthcare professionals, policymakers, and ethicists shaping the future of ethical healthcare.
California’s 900,000 farmworkers constitute half of the nation's farm labor force, playing a critical role in U.S. agriculture by harvesting one-third of the country’s vegetables and 90 percent of its strawberries. Despite their essential contributions, these workers face significant economic and social challenges. Most farmworkers have over a decade of experience in the U.S. agricultural sector, yet many struggle with job security, low wages, and lack of access to stable employment opportunities. Immigration status further compounds these hardships, as an estimated 50 percent of California’s farmworkers are undocumented, making them particularly vulnerable to labor exploitation, deportation threats, and exclusion from social services. Recent federal immigration enforcement policies, including mass deportation raids and proposed changes to the H-2A visa program, have heightened workforce instability, raising concerns about labor shortages in agriculture. Farmworker advocacy groups are calling for legislative reforms to provide legal pathways to residency or expand guest worker protections. Addressing these challenges is essential for ensuring both the well-being of farmworkers and the long-term sustainability of California’s agricultural economy. This paper examines the threats posed by automation and shifting immigration policies and proposes policy solutions, including public-private partnerships, to support displaced farmworkers in the evolving agricultural landscape.
At the crossroads of artificial intelligence (AI) and creative rights, the music industry is grappling with how to balance innovation and copyright protection. This paper examines Suno AI as a test case for Human-Centered AI (HCAI), investigating how generative music platforms may either support or undermine artistic expression. Drawing on recent court decisions, legal theory, and stakeholder perspectives, the analysis explores key issues such as fair use, the idea/expression distinction, and the evolving role of AI in creative fields. Building on HCAI scholarship by Shneiderman (2022), Capel and Brereton (2023), and Shroff (2024), the paper evaluates tensions between technologists promoting open innovation and artists advocating for stronger rights protections. Cases like Authors Guild v. Google and Andy Warhol Foundation v. Goldsmith highlight the difficulty of applying traditional legal frameworks to AI-generated content. To address these challenges, the paper proposes new policy tools, including standardized "likeness thresholds" to detect AI replication, transparency requirements for generative systems, and metadata tagging for copyright enforcement. As AI continues to reshape creative industries, this analysis offers a path toward regulation that supports both artistic integrity and technological progress. The findings aim to inform policymakers, AI developers, and creative professionals seeking a sustainable future for human and machine-made art.
Breaking the Silence with Direct-Speech Brain Computer Interfaces: Centering Communicative Disability in Ethical Recommendations for Mitigating Algorithmic Bias
Despite growing scholarly attention to the ethical implications of direct-speech brain-computer interfaces (BCIs), there remains a lack of concrete guidance on how to address these concerns—particularly for users with communicative disabilities. As neuroengineering continues to advance alongside developments in artificial intelligence and machine learning, the need for clear ethical frameworks and standardized protocols becomes increasingly urgent. This paper investigates the core ethical challenges of direct-speech BCIs for individuals with communicative disabilities, identifying algorithmic bias as a central and underexamined issue. Through analysis of existing neuroethical standards, policy proposals, and international legislation related to AI and neurotechnology, the paper exposes critical gaps in current guidance. Drawing from disability studies and related fields, it argues that mitigating bias and ensuring equitable BCI development requires a broader, more inclusive understanding of language and a commitment to user-centered design.
This paper asserts that the progression of Artificial Intelligence technology is an incredible feat and holds momentous potential to grant significant efficiency to individuals involved in the study and practice of law. Nonetheless, such benefits do not transpire without subsequent harms; this paper means to present the adverse effects that could befall historically marginalized and disadvantaged groups and promote the motion of constructing infrastructure to regulate the utilization of this revolutionary technology in the legal field. Identifying the promises that the techno-solutionist mindset preaches and contrasting it with the current capabilities of Artificial Intelligence, the aim is to have a clear understanding of how an integration of this magnitude could benefit students of and practitioners in law. By focusing on past incidents regarding A.I. integration in media, business, and finance, this paper will bring credibility to the concerns previously outlined. Brief and preliminary data gathered from individuals involved in the identified discipline will illustrate the imperative for cautionary advancement of another integration and further progression of A.I. technology in the legal field. Contrary to what business moguls and wealthy proprietors would like people to believe, this technology is still in its infancy and has faults that prevent it from executing the extraordinary feats that it is credited with being able to accomplish. Such unmerited promotion can lead to unfavorable outcomes that will not only impact utilizers of this technology, but also the clients they serve, and thus regulation is demanded. By regulation, we mean to incur internal regulation by legal institutions (e.g., law schools and law firms) combined with government policy to hold these agent conglomerates accountable and protect common people, specifically the most vulnerable of society, from harm. This paper concludes with the perspective to think critically regarding the development and integration of advanced technology within all fields, and not solely the legal sector.
Regulatory Measures and Consumer Protection in Electronic Gambling
This paper advances a novel regulatory framework to address the growing cognitive and behavioral harms associated with electronic gambling machines (EGMs) and iGaming (online gambling) platforms. As the gambling industry rapidly expands, it increasingly capitalizes on users’ psychological vulnerabilities and the persuasive design of digital interfaces. Identifying the limitations of AI-driven harm mitigation strategies, which often lack contextual sensitivity and regulatory accountability, this paper argues for an integrated approach that combines targeted AI interventions with enforceable regulatory measures. Drawing on the UK Gambling Commission’s model, the paper proposes a new, region-specific framework for the United States and North America that foregrounds fairness, transparency, and consumer protection. This framework makes a novel contribution by incorporating explainable design standards, structural game modifications, and cross-border regulatory cooperation into a cohesive policy blueprint. Key recommendations include rigorous licensing and audit processes, mandated user protections, and international collaboration to reduce gambling-related harms and promote ethical industry practices.
This paper affirms Israeli technology and its potential to improve the quality of life for both Israelis and Palestinians. It argues the choice lies in the Palestinians’ hands — renounce Hamas, renounce terror, acknowledge Israel’s right to exist, and we can move toward peace. Focusing on the Israel-Hamas war, we examine efforts to delegitimize Israel, its AI-driven tech, and its right to self-defense. Anti-Zionists and tech critics decry Israel as a “racist,” “settler colonialist” project armed with tech that enforces its “apartheid.” Yet, they promote their own ethnic “Arab Palestine” “from the river to the sea,” as though anti-racist justice consists in statehood for Arab Palestinians but not any other inhabitants of the land. Through a close reading of anti-Zionist work and the key influences of Palestinian scholars Edward W. Said and Elia Zureik, we demonstrate that the settler colonialism thesis, which claims to be an “objective, historical, secular” lens, is flawed and antisemitic. This is a long read. We hope to do justice to Said and Zureik’s work and seriously address criticisms of Israel, its technology, and Palestinian loss of life, before offering our positive views moving forward. Contrary to the anti-Zionist critics and the Boycott, Divestment, and Sanctions (BDS) movement, we believe that Palestinian suffering can end without delegitimizing Israel. Most importantly, normalization of Palestinian and Israeli relations can provide Palestinians self-determination, freedom, and equality. By normalization, we mean that neither side will attain the all-or-nothing demands of groups like BDS or far right Israeli organizations, and that instead both will gain acceptance and equality. After October 7, 2023, such a coexistence appears increasingly fraught, but remains possible. We end with our view of Israeli technology as an important component for an Israeli and Palestinian future.
Copyright (c) 2024 STUDENTS FOR ISRAELI TECH Starkman