# data[].attributes.body
AI4G Ethical Dilemma: Footprint · Climate Action

The chatbot that came with its own power plant

Training and running large AI chatbots takes enormous amounts of electricity. To power its Grok chatbot fast, Elon Musk's company xAI installed dozens of gas-burning turbines next to its data centre in Memphis, Tennessee, without the air permits the law requires. They can release over 1,700 tons of smog-forming gas a year, in a neighbourhood that is mostly Black and already struggles with air quality; the NAACP sued in 2026. xAI is not alone: Google's own reports show its emissions rose 48% in five years, and Microsoft's rose 25% in one year, both blaming AI. At the same time, AI tools like Google DeepMind's GraphCast now forecast hurricanes and floods earlier and more accurately than older systems, helping communities prepare.

https://naacp.org/articles/naacp-sues-xai-illegal-pollution-data-center-power-plant

Discussion questions:

Big AI companies promise their emissions will come down as chips get more efficient and clean energy scales up. What would need to be true for that promise to actually happen?

Is it fair that the pollution from a data centre falls on one nearby neighbourhood, while the chatbot it powers is used by people all over the world?

AI can raise emissions by training and running huge models, and also lower emissions by forecasting weather and disasters. How would you decide whether a specific AI use is worth its climate cost?

# data[].attributes.attachment_link.title
Data centers for AI use huge amounts of electricity, water, driving up costs and climate concerns

# data[].attributes.body
AI4G Ethical Dilemma: Accessibility · Climate Action

The flood warning that never arrived

AI has made flood forecasting genuinely more accurate, giving communities more warning before disasters strike. But researchers keep finding what they call the last mile problem: a perfect forecast is useless if the warning never reaches the people who need it. In many disaster-prone regions, whole communities are missing from digital maps, do not have smartphones or signal, or do not receive warnings in a language they speak. Despite better predictions, the people most at risk are often the last to know.

https://www.un.org/en/climatechange/early-warnings-for-all

Discussion Points:

1. What is the point of a highly accurate AI flood prediction if the warning cannot reach the people at risk?

2. Who is most likely to be left out when a warning system assumes everyone has a smartphone and signal?

3. What would a genuinely accessible warning system need to include, beyond just better predictions?

# data[].attributes.attachment_link.authors[].name
United Nations Office for Disaster Risk Reduction

# data[].attributes.body
AI4G Ethical Dilemma: Misinformation · Climate Action

The flood videos that were never real

During real hurricanes, including Hurricane Helene in 2024 and Hurricane Melissa in 2025, AI-generated videos went viral online: sharks swimming in hotel pools, airports supposedly destroyed, animals stranded on rooftops. None of it had actually happened. The content racked up millions of views before being debunked, and Jamaican authorities had to publicly warn people not to trust what they were seeing during a real, live emergency.

https://www.cbsnews.com/news/hurricane-viral-video-how-to-spot-old-fabricated-ai-footage/

Discussion Points:

1. Why might people share a fake AI disaster video, even if they are not trying to cause harm?

2. What harm can fake disaster content cause while an emergency is actually happening?

3. How could you tell whether a dramatic flood or storm video you see online is real?

# data[].attributes.attachment_link.title
AI-generated images are spreading on social media, compounding hurricane misinformation

# data[].attributes.body
AI4G Ethical Dilemma: Privacy · Life on Land

The anti-poaching cameras that watch everyone

Conservation groups use AI-powered camera traps, drones and acoustic sensors, including real tools like TrailGuard AI and Air Shepherd, to detect poachers in national parks in Africa and elsewhere, and it has led to real arrests and convictions. But the same sensors also pick up images, and sometimes voices, of local and Indigenous communities who live in or pass through these areas for entirely legitimate reasons: herding animals, gathering food, or following traditional routes. Some communities have reported feeling watched, and in a few cases have damaged the equipment in protest.

https://www.smithsonianmag.com/smart-news/new-ai-camera-helps-conservationists-spot-elephant-poachers-180971180/

Discussion Points:

1. Is it fair for a system built to catch poachers to also record people who are not doing anything wrong?

2. Who should be consulted before AI surveillance technology is installed on land people live on or use?

3. How could a conservation project get the benefits of AI monitoring without treating local communities as suspects?

# data[].attributes.attachment_link.title
Safeguarding Wildlife: Ai Cameras Defend Endangered Animals Against Poachers

# data[].attributes.body
AI4G Ethical Dilemma: Bias · Life Below Water

The illegal fishing tracker with a blind spot

Global Fishing Watch, a real tool built with Google, Oceana and SkyTruth, uses AI and satellite data to catch illegal fishing by tracking ships' GPS signals from space. It has genuinely helped catch large vessels breaking the law. But nearly a third of the world's seafood is caught by small-scale fishing boats, and fewer than one in a hundred of these smaller boats carry the GPS trackers the AI relies on. The system can see big trawlers clearly, but is almost blind to a huge share of the world's fishing fleet.

https://www.nationalfisherman.com/ai-and-satellite-data-to-help-end-illegal-fishing

Discussion Points:

1. Why might an AI trained on GPS and satellite data end up almost blind to millions of small fishing boats?

2. Who benefits, and who is left out, when a monitoring system only sees part of the picture?

3. What would need to change for a tool like this to fairly cover both large and small fishing operations?

# data[].attributes.attachment_link.title
The Outlaws of the Ocean — and How We're Reeling Them In | Tony Long | TED

# data[].attributes.body
Ethical Dilemma 6: A medical diagnosis algorithm assigns risk scores to patients to help allocate healthcare resources, but it systematically underestimates the risk for certain racial groups.

Discussion Points:

1. What are some potential sources of bias in medical diagnosis algorithms?

2. How can biased medical diagnosis algorithms affect patient care and outcomes?

3. How might bias in medical diagnosis algorithms contribute to health disparities?

4. What role should regulations and oversight play in mitigating bias in medical diagnosis algorithms?

# data[].attributes.body
Ethical Dilemma 5: A social media platform's algorithm curates news feeds based on user behaviour, leading to echo chambers where users are only exposed to viewpoints similar to their own.

Discussion Points:

1. How do personalised news feeds contribute to the creation of echo chambers?

2. What are the potential consequences of echo chambers for individuals and society?

3. How can social media platforms introduce diverse viewpoints without alienating users?

4. What role should regulations and oversight play in mitigating the effects of echo chambers?

# data[].attributes.body
Ethical Dilemma 4: A self-driving car has to make a decision in a split-second to avoid an accident, but the decision could lead to different outcomes depending on the algorithms programmed.

Discussion Points:

1. What ethical dilemmas arise in scenarios where an autonomous vehicle must make a decision to avoid an accident?

2. What role should public opinion play in determining the ethical guidelines for autonomous vehicles?

3. How can ethical considerations be balanced with legal and technical constraints?

4. What role should regulations and oversight play in guiding the decision-making of autonomous vehicles?

# data[].attributes.body
Ethical Dilemma 3: A company uses an AI system to sift through resumes, but the algorithm tends to favour male candidates over female ones because of historic hiring data.

Discussion Points:

1. How might the data used to train this algorithm be inherently biased?

2. How can companies ensure fair representation in their hiring processes?

3. What potential consequences can biased hiring algorithms have on individuals and society?

4. How can hiring algorithms be designed to be more transparent and accountable?

# data[].attributes.body
Ethical Dilemma 2: A facial recognition system is used for security, but it has difficulty identifying individuals with darker skin tones because it was trained on a dataset mostly consisting of lighter-skinned individuals.

Discussion Points:

1. What are some reasons that facial recognition algorithms might be less accurate for certain racial backgrounds?

2. How can bias in facial recognition technology impact individuals and communities?

3. How can facial recognition technology be more transparent and accountable?

4. What role should regulations play in mitigating bias in facial recognition systems?

# data[].attributes.body
Ethical Dilemma 1: An algorithm used in the justice system to predict the likelihood of a defendant reoffending exhibits racial bias, leading to harsher sentences for certain ethnic groups.

Discussion Points:

1. How might sentencing algorithms contribute to systemic bias in the criminal justice system?

2. What potential consequences can biased sentencing algorithms have on individuals and communities?

3. How can sentencing algorithms be designed to avoid perpetuating systemic bias?

4. What role should regulations and oversight play in mitigating bias in sentencing algorithms?