The deployment of machine learning models in organizations requires executives and managers to critically think about the ethical implications that those models have on their stakeholders. Colleges and Universities who teach machine learning without also teaching the ethics of machine learning are doing a disservice to societies.

Machine learning ethics refers to the moral implications of using algorithms and their associated data. There may be ethical issues surrounding how data are captured and used along with ethical issues surrounding the developers of the machine models and algorithms. For instance, certain models may knowingly or unknowingly discriminate against certain customers or employees. As a result, all employees involved in the development and deployment of machine learning models have a moral responsibility to ensure that their trained models are aligned with the values of the organization and society more broadly.

It is not difficult to find examples of organizations across all industries who have scrapped models due to unintended consequences. Microsoft had to remove a facial recognition model and Amazon had to remove a hiring model due to ethical issues. Most of these examples are not due to deliberate attempts to do harm. Instead, they were due to the black box nature of the algorithms.

The following articles provide thoughtful discussions of machine learning ethics and the potential unintended consequences of machine learning: 1) World Bank, 2) Techopedia, and 3) Packtpub.

In this project, you will complete a short MIT course and read a book chapter to help you think critically about the importance of ethics in the development and deployment of machine learning. The instructions may be downloaded here.

“This generation will witness social and economic changes in our societies, that will be irreversible, thanks to AI.” ― Anthony Merrydew