Generative artificial intelligence (GenAI) is a type of machine learning. It refers to machine-learning models that use neural networks to train on vast amounts data. After training (i.e. learning) the models then produce novel output based on their training sets, much like advanced auto-complete.
Ethics refers to both moral principles and to the study of people's moral obligations in a society.
AI ethics is an evolving field. As a result, there is considerable debate about what constitutes ethical AI. However, a 2019 analysis (which has been cited over 3,500 times!) of national and international AI policies and guidelines found convergence around five ethical principles: transparency; justice, fairness, and equity; non-maleficence; responsibility and accountability; privacy.
Transparency
AI systems and processes should be open and clear about how decisions are made, including disclosing the data, algorithms, and assumptions behind their functionality, to ensure trust and accountability.
Justice, Fairness, and Equity
AI systems should not perpetuate bias or discrimination, and they should be designed to treat all individuals and groups equitably, addressing and mitigating any unfair outcomes.
Non-maleficence
AI systems should not be harmful to individuals or society, and the systems should avoid causing any unintended negative consequences.
Responsibility and Accountability
Developers, organizations, and users of AI systems should be held accountable for the outcomes of the technology, including ensuring ethical use and addressing any harm caused.
Privacy
AI systems should protect the personal information and data of individuals, ensuring that their data is not misused or exposed without consent.
