Whether in education, business, the military, healthcare, or other domains, certain ethical concerns occur again and again: Privacy, Bias, Transparency, and Accountability are among the most common ethical concerns in AI.
Privacy: Given that AI is trained on Big Data, privacy concerns are nearly ubiquitous. This is a concern at the design stage of AI. Here attempts are made to anonymize data, in order to prevent it from being traced back to particular individuals. However, in various areas, this can be difficult. In medical care, for example, DNA markers might make data traceable. The concerns, here, though, is not just for the developers of AI. It is also a concern for the administration of AI. What rules are needed in the field of medicine to keep records confidential?
Bias: There has been much focus on the bias of AI systems. AI is trained on human information, and if human systems training AI are biased, then AI will duplicate that bias, unless great care is taken to root it out. AI is trained on particular data sets. If the data that trains AI for facial recognition, for example, consists of Caucasian data sets, then the AI will get better at recognizing Caucasians than non-Caucasians. This has been one of the major concerns with the early development of facial recognition software. AI, trained on largely white male data sets, has been more prone to false positives with individuals who are not in that demographic. This appears to be something that can be addressed in the development phase of the AI. Just train it with more representative data and it should improve. The concern of bias, though, isn't just a concern at the developmental phase. There are questions about how it is implemented and administered. If facial recognition is used for policing for example, then what neighborhoods will it be used in? Will it be employed in neighborhoods that might reinforce existent inequities in policing?
Transparency and Accountability: Transparency and accountability are further concerns. Gone are the days of symbolic AI or Good Old-Fashioned AI (GOFAI). In an earlier period of AI development, human-made algorithms were provided to AI and it made decisions according to our predefined rules. But current developments in AI are based on neural networks. These have provided for unparalleled human-level performance in multiple areas. They, however, typically make decisions, like humans, in what is known as a Black Box. The decisions are made according to criteria that are not readily visible or understandable.
Transparency: The lack of transparency in black box models means that users and even developers cannot see how inputs are transformed into outputs. This opacity can lead to a lack of trust in AI systems, as stakeholders cannot verify or understand the reasoning behind decisions.
Accountability: Without transparency, it becomes challenging to hold AI systems accountable for their actions. If an AI system makes a mistake or a biased decision, it is difficult to trace back and understand why it happened, making it hard to correct or improve the system
(Generative AI was used in the writing of some of this text)