An ICTP Virtual Meeting From helping farmers adapt to climate change to predicting disease outbreaks, scientists in developing countries have begun turning to ML for more effective solutions. With this potential, however, comes the possibility for abuse, misuse, and unintended consequences. Embedding Ethics Education in Machine Learning: case studies from various parts of the world that demonstrate the need for a wider perspective on ML ethical challenges. Big data, privacy and democracy: ethical questions linked with big data exploitation, privacy and the dangers for democracy. Machine Learning, bias and fairness: problems of ML amplified bias, and some of the possible solutions.

While artificial intelligence stands to bring rapid improvements to the healthcare sector, director-general of the World Health Organisation Margaret Chan has warned that it must be for the good of everybody – not just the wealthiest countries. "What good does it do to get early diagnoses of skin or breast cancer if a country does not provide the opportunity for treatment or if the price of medicines are not affordable?" "Many developing countries don't have health data to mine. And they don't have functioning systems for registering vital causes of death stats. "Enthusiasms for smart machines reflect the perspectives of well-resourced companies and wealthy countries.