In 2019, I was entrenched in volunteer work at a small free clinic in North Hollywood, California. Actually in pursuit of my California phlebotomy (blood drawing) credential, my goal was to continue work in clinical settings, and to finish my degree to become a doctor. I wanted, like many of us, to help patients — to make a positive difference in the lives of people.
It was not very long after that I discovered computer science, artificial intelligence, and biotechnology. Hooked on the idea and disillusioned with a traditional academic path, I jumped at the opportunity to join a startup-like college in San Francisco, accelerate my degree path, and get into tech. It was all so shiny and promising. Papers on top of papers were being published with titles like “Artificial Intelligence Helping Biotech Get Real”, “AI Breakthrough Could Speed Up Lung Cancer Diagnosis …”, and many, many more. Months into pursuit of my new, shiny degree, I was doing aggressive research into the field, and even had begun working in it.
Pause. The Covid-19 pandemic seemed to accelerate the AI diagnostic space, with poor results. Venture capitalists, large investment firms, academic organizations, and big pharma continued to pour millions into so-called “AI focused biotech” startups. While large conference platforms seem to always accept a single keynote speaker on fairness or ethics in AI (mostly focused on disparate outcomes between demographic groups — a crucial part of the conversation, but a part nonetheless), my LinkedIn feed, industry contacts, and tech-news outlets served crickets on the conversation of ethical standards for the artificial intelligence boom. The biotechnology companies themselves continued to post job openings for subject matter expert “Data Scientists”, leaving no space in the room for anyone without a laser-focused Ph.D. dissertation, anyone who might have less of a stake in innovation for innovation’s sake, anyone willing to stand in the corner and ask the crucial question:
“We can, but should we?”
Building Systems Designed for Failure
Physicians swear an oath to “first do no harm”, but professionals falling under the ubiquitous umbrella of bioinformaticians, data scientists, and machine learning engineers swear no such oath.
Instead, when we’re hired, we market ourselves based on metrics….
Continue reading: https://towardsdatascience.com/lets-talk-the-ethical-ai-conversation-and-how-biotech-is-failing-a1dcfd63aba1?source=rss—-7f60cf5620c9—4