Ethics Checklist for Forest Wildfire Prediction Model

1. Data Collection

Input Variables: Temperature, Humidity, Wind Speed, Rainfall, Fuel Moisture, Vegetation Type, Slope, Region
Output Variables: Fire Size, Fire Duration, Suppression Cost, Fire Occurrence
Are the data sources, such as weather, vegetation, and geographic data, properly licensed and legally available?
Has any sensitive information, such as private property or personal location data, been anonymized?
Have you obtained consent for data collected from private or proprietary sources, such as satellite imagery or drone footage?

How will you ensure that the model’s predictions are fair and do not disproportionately affect specific regions or communities (e.g., indigenous lands, rural areas)?
What biases might exist in the historical data (e.g., underreporting of fires in certain regions), and how will you address these to ensure the model does not unfairly target or neglect specific areas?
How will you balance fairness in handling both false positives (predicting a fire where there is none) and false negatives (failing to predict a fire)?
Have you tested the model across different regions to ensure consistent performance across various forest types (tropical, temperate, etc.)?

How will you ensure transparency about the data sources, algorithms, and decision-making process of the model?
What information will you make available to government agencies, the public, and environmental organizations?
How will you communicate the model’s predictions and limitations to decision-makers so that they understand the risks involved?
How will you explain false positives and false negatives to the affected communities or stakeholders, especially during critical events like evacuations?

How will you ensure the privacy of individuals whose data might be inadvertently captured (e.g., campers, rural residents) through satellite images, drones, or weather stations?
What steps will you take to prevent the misuse of this data, especially in terms of tracking human activities in forest areas without their consent?
If external data sources, such as drones or surveillance tools, are integrated into the model, how will you balance the need for accurate predictions with protecting individual privacy?

Who will be held accountable if the model incorrectly predicts a wildfire, resulting in unnecessary evacuations or failure to prevent a disaster?
What system will you establish to monitor and adjust the model over time, ensuring it adapts to changing environmental and climate conditions?
How will you communicate accountability measures to the public, especially in high-risk areas where wildfire prediction is critical?

How will you ensure the model includes diverse data from different types of forests (e.g., tropical, temperate) and regions, especially those that may be underrepresented in historical data collection?
How will you ensure the model accounts for the needs of different communities, including vulnerable populations such as indigenous groups or rural residents who have unique relationships with the land?
If certain regions or communities lack sufficient data (e.g., underreporting, lack of resources), how will you address this to avoid biased predictions?

How will the model’s predictions affect long-term forestry practices, land management, and firefighting strategies over time?
How will you ensure the model remains sustainable, considering the evolving nature of climate change and its effects on wildfire patterns?
What are the broader social and environmental implications if this model becomes widely adopted (e.g., impacts on land use, deforestation policies, wildlife conservation)?