Beneficial AI

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Making AI work for humans, now

Beneficial artificial intelligence is a term I came up with to encompass the a broad push to make robotics, machine learning, and related autonomy more useful for humans. This encompasses a broad push to integrate work from AI Safety, Ethical AI, Fair Machine Learning, and Human-in-the-loop autonomy. What started as a blogging direction is now an active area of my research.

Robots interact with humans in the physical world, so any deleterious effects of algorithms will be magnified (and even more concentrated on lower-income groups that engage with companies more likely to automate, those with low margins).

One of my big hopes is that model-based RL proves useful in pure robotics, and in addressing some of these sociotechnical concerns.

Open areas of study:

  • interpretable RL, guiding policy for RL,
  • AI “clinic”
  • Mitigating harms of intelligent consumer drones (e.g. slaughterbots)

(Papers accepted and awaiting publication)

We present a concise set of directions for understanding the societal risks of new directions of AI research.
We study three developing subfields of AI research and their growing relationship with the sociotechnical: AI Safety, Fair Machine Learning, and Human-in-the-loop Autonomy.