RMAIIG AI Ethics SIG

AI tools are rapidly entering classrooms through students, teachers, and school policies. The tough question is not whether AI will be used, but what kinds of use support learning versus cross the line into misrepresentation.

Join us for an inviting, big-picture conversation where we’ll explore:

• What counts as cheating now?: Where’s the line between tutoring, editing, collaboration, and submitting AI work as your own?
• Evidence of mastery: What does a teacher or professor actually need to see to assess learning fairly?
• Equity and access: Do strict AI bans punish students with fewer resources, or protect those most at risk?
• Privacy and student data: What should schools require from AI tools (data retention, third-party access, transparency)?
• Better assessments: How can we redesign assignments (process notes, oral defense, in-class drafting) to reduce incentives to cheat without turning to surveillance?

Whether you’re a student, educator, parent, technologist, administrator, or simply curious, bring your questions and a scenario or assignment you’d like to stress-test together.

AI is changing how stories are written, illustrated, voiced, translated, and distributed. It can lower barriers for new creators and help people communicate across language and ability. It can also flood the world with low-effort content, blur the line between authentic voice and synthetic output, and scale persuasion in ways that are hard to see in the moment.

Join us for a friendly, thoughtful discussion where we’ll explore:

• Authorship and voice: When AI helps write, who is the author and what counts as genuine creative intent?
• AI slop and the attention economy: What happens when platforms are saturated with cheap, optimized content, and how does that change culture and discovery?
• Disclosure and trust: When should AI assistance be disclosed in fiction, nonfiction, journalism, or marketing?
• Cultural impact: Does AI widen participation in storytelling, or amplify stereotypes and appropriation at scale?
• Persuasion and manipulation: How do we distinguish art, entertainment, and education from targeted influence?
• Practical norms: What policies or creator guidelines would you actually adopt for schools, studios, platforms, or communities?

Whether you are a writer, artist, educator, technologist, student, or simply curious, bring a story example you love and a scenario you want to stress-test with the group.

Generative AI can accelerate creative workflows and lower barriers for new artists. It can also remix styles without consent, shift income away from creators, and blur what audiences mean by authentic or original work. Join us for a practical, welcoming discussion where we’ll explore:

• Authorship and originality: When humans and models co-create, what does authorship mean and what should be credited?
• Training data and consent: What is fair for models trained on public art, paid libraries, or scraped work, and what should be opt-in or opt-out?
• Compensation and markets: If AI tools substitute for human labor, what does fair payment look like for artists, performers, and freelancers?
• Disclosure and provenance: When should AI involvement be labeled, and what does “proof” of origin look like for audiences and clients?
• Culture and appropriation: How do we preserve cultural heritage and avoid extraction when models imitate marginalized or sacred styles?

Whether you’re an artist, designer, musician, writer, technologist, student, hobbyist, or simply curious, bring one example of AI art you admire and one concern you want to unpack with the group.

AI’s growth is tightly linked to energy and infrastructure. Data centers can bring jobs and investment, but they also raise hard questions about grid reliability, electricity prices, water use, and emissions. Communities and policymakers are actively debating who should bear the costs of upgrades and how to ensure the benefits are shared fairly. Join us for a practical, welcoming discussion where we’ll explore:

• Demand and reliability: What happens to grid planning when large new “AI loads” arrive quickly and unpredictably?
• Rates and fairness: If grid upgrades drive rate increases, who should pay, and how do we protect households and small businesses?
• Boom and bust risk: What happens if we build out the grid for AI demand and then the bubble bursts, leaving everyone to pay for stranded infrastructure?
• Climate and clean energy claims: How should we evaluate “100% renewable” or “net zero” commitments tied to data center growth?
• Transparency and disclosure: What should companies and utilities publish about energy use, emissions, and water impacts?
• Transparency and local governance: What should companies and utilities publish about energy use, emissions, and water impacts, and what questions should cities and counties ask when approving data centers?

Whether you work in energy, tech, policy, research, or you’re simply curious, bring your questions and a local scenario you’d like to stress-test with the group.