[This blog post is a continuation of our series about research ethics in computer science.]
The widespread deployment of artificial intelligence and specifically machine learning algorithms causes concern for some fundamental values in society, such as employment, privacy, and discrimination. While these algorithms promise to optimize social and economic processes, research in this area has exposed some major deficiencies in the social consequences of their operation. Some consequences may be invisible or intangible, such as erecting computational barriers to social mobility through a variety of unintended biases, while others may be directly life threatening. At the CITP’s recent conference on computer science ethics, Joanna Bryson, Barbara Engelhardt, and Matt Salganik discussed how their research led them to work on machine learning ethics.
Joanna Bryson has made a career researching artificial intelligence, machine learning, and understanding their consequences on society. She has found that people tend to identify with the perceived consciousness of artificially intelligent artifacts, such as robots, which then complicates meaningful conversations about the ethics of their development and use. By equating artificially intelligent systems to humans or animals, people deduce its moral status and can ignore their engineered nature.
While the cognitive power of AI systems can be impressive, Bryson argues they do not equate to humans and should not be regulated as such. On the one hand, she demonstrates the power of an AI system to replicate societal biases in a recent paper (co-authored with CITP’s Aylin Caliskan and Arvind Narayanan) by letting systems trained on a corpus of text from the World Wide Web learn the implicit biases around the gender of certain professions. On the other hand, she argues that machines cannot ‘suffer’ in the same way as humans do, which is one of the main deterrents for humans in current legal systems. Bryson proposes we understand both AI and ethics as human-made artifacts. It is therefore appropriate to rely ethics – rather than science – to determine the moral status of artificially intelligent systems.
Barbara Engelhardt’s work focuses on machine learning in computational biology, specifically genomics and medicine. Her main area of concern is the reliance on recommendation systems, such as we encounter on Amazon and Netflix, to make decisions in other domains such as healthcare, financial planning, and career decisions. These machine learning systems rely on data as well as social networks to make inferences.
Engelhardt describes examples where using patient records to inform medical decisions can lead to erroneous recommendation systems for diagnosis as well as harmful medical interventions. For example, the symptoms of heart disease differ substantially between men and women, and so do their appropriate treatments. Most data collected about this condition was from men, leaving a blind spot for the diagnosis of heart disease in women. Bias, in this case, is useful and should be maintained for correct medical interventions. In another example, however, data was collected from a variety of hospitals in somewhat segregated poor and wealthy areas. The data appear to show that cancers in children from hispanic and caucasian races develop differently. However, inferences based on this data fail to take into account the biasing effect of economic status in determining at which stage of symptoms different families decide seek medical help. In turn, this determines the stage of development at which the oncological data is collected. The recommendation system with this type of bias confuses race with economic barriers to medical help, which will lead to harmful diagnosis and treatments.
Matt Salganik proposes that the machine learning community draws some lessons from ethics procedures in social science. Machine learning is a powerful tool the can be used responsibly or inappropriately. He proposes that it can be the task of ethics to guide researchers, engineers, and developers to think carefully about the consequences of their artificially intelligent inventions. To this end, Salganik proposes a hope-based and principle-based approach to research ethics in machine learning. This is opposed to a fear-based and rule-based approach in social science, or the more ad hoc ethics culture that we encounter in data and computer science. For example, machine learning ethics should include pre-research review through forms that are reviewed by third parties to avoid groupthink and encourage researchers’ reflexivity. Given the fast pace of development, though, the field should avoid a compliance mentality typically found at institutional review boards of univeristies. Any rules to be complied with are unlikely to stand the test of time in the fast-moving world of machine learning, which would result in burdensome and uninformed ethics scrutiny. Salganik develops these themes in his new book Bit By Bit: Social Research in the Digital Age, which has an entire chapter about ethics.”
See a video of the panel here.
