MIT researchers show how to detect and address AI bias without loss in accuracy

The key…is often to get more data from underrepresented groups. For example…an AI model was twice as likely to label women as low-income and men as high-income. By increasing the representation of women in the dataset by a factor of 10, the number of inaccurate results was reduced by 40 percent. Source: MIT researchers show […]

The AI Threat to Democracy

With the advent of strong reinforcement learning…, goal-oriented strategic AI is now very much a reality. The difference is one of categories, not increments. While a supervised learning system relies upon the metrics fed to it by humans to come up with meaningful predictions and lacks all capacity for goal-oriented strategic thinking, reinforcement learning systems […]

When AI Misjudgment Is Not an Accident

The conversation about unconscious bias in artificial intelligence often focuses on algorithms that unintentionally cause disproportionate harm to entire swaths of society…But the problem could run much deeper than that. Society should be on guard for another twist: the possibility that nefarious actors could seek to attack artificial intelligence systems by deliberately introducing bias into […]

Mozilla’s Common Voice project

Any high-quality speech-to-text engines require thousands of hours of voice data to train them, but publicly available voice data is very limited and the cost of commercial datasets is exorbitant. This prompted the question, how might we collect large quantities of voice data for Open Source machine learning? Source: Branson, M. (2018). We’re intentionally designing […]

The Future of Artificial Intelligence Depends on Trust

To open up the AI black box and facilitate trust, companies must develop AI systems that perform reliably — that is, make correct decisions — time after time. The machine-learning models on which the systems are based must also be transparent, explainable, and able to achieve repeatable results. Source: Rao, A. & Cameron, E. (2018). […]

Want Less-Biased Decisions? Use Algorithms.

At the heart of this work is the concern that algorithms are often opaque, biased, and unaccountable tools being wielded in the interests of institutional power. So how worried should we be about the modern ascendance of algorithms? These critiques and investigations are often insightful and illuminating, and they have done a good job in […]

Defensive Diagnostics: the legal implications of AI in radiology

Doctors are human. And humans make mistakes. And while scientific advancements have dramatically improved our ability to detect and treat illness, they have also engendered a perception of precision, exactness and infallibility. When patient expectations collide with human error, malpractice lawsuits are born. And it’s a very expensive problem. Source: Defensive Diagnostics: the legal implications […]

Fairness matters: Promoting pride and respect with AI

We’re creating an open dataset that collects diverse statements from the LGBTIQ+ community, such as “I’m gay and I’m proud to be out” or “I’m a fit, happy lesbian that has just retired from a wonderful career” to help reclaim positive identity labels. These statements from the LGBTIQ+ community and their supporters will be made […]

An introduction to artificial intelligence in clinical practice and education

Two weeks ago I presented some of my thoughts on the implications of AI and machine learning in clinical practice and health professions education at the 2018 SAAHE conference. Here are the slides I used (20 slides for 20 seconds each) with a very brief description of each slide. This presentation is based on a […]

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