Tag: AI ethics

• Teaching AI Ethics: Truth and Academic Integrity

  

  This is the third post in a series exploring the nine areas of AI ethics outlined in this original post. Each post goes into detail on the ethical concern and provides practical ways to discuss these issues in a variety of subject areas. For the previous post on Environmental concerns, click here. The concept of…

  Leon Furze

  March 21, 2023

  AI, Ethics

  AI development., AI ethics, AI outputs, AI training, algorithmic bias, biased datasets, community needs, data bias, discrimination, environmental impact, ethical concerns, ethical guardrails, facial recognition, Fairness, Justice, predictive policing, responsible AI use, social services, societal biases

• Teaching AI Ethics: Environment

  

  This is the second post in a series exploring the nine areas of AI ethics outlined in this original post. Each post goes into detail on the ethical concern and provides practical ways to discuss these issues in a variety of subject areas. For the first post on bias and discrimination, click here. The original…

  Leon Furze

  March 13, 2023

  AI, Ethics

  AI development., AI ethics, AI outputs, AI training, algorithmic bias, biased datasets, climate, climate change, climate crisis, community needs, data bias, data center, data centre, discrimination, electricity, energy, environment, environmental impact, ethical concerns, ethical guardrails, facial recognition, Fairness, Justice, predictive policing, responsible AI use, social services, societal biases

• Teaching AI Ethics: Bias and Discrimination

  

  This is the first post in a series exploring the nine areas of AI ethics outlined in this original post. Each post will go into detail on the ethical concern as well as providing practical ways to discuss these issues in a variety of subject areas. UPDATE: Here’s a pre-post-script to this post which raises…

  Leon Furze

  March 6, 2023

  AI, Ethics

  AI development., AI ethics, AI outputs, AI training, algorithmic bias, biased datasets, community needs, data bias, discrimination, environmental impact, ethical concerns, ethical guardrails, facial recognition, Fairness, Justice, predictive policing, responsible AI use, social services, societal biases