What’s worse is the ones that don’t scatter when you turn on the lights. Just sit there waiting for you to get out of their territory.

Where does the NYPD keep getting these expensive but apparently useless robots?

Right. I care less about 60% less power, and more about will it randomly connect my phone to my car as my partner drives away instead to the speakers I was already using on the desk next to me.

Martha Wells. Finished the Murderbot series, wrapping up the Raksura. All very much of the same cloth, but I enjoy it.

Also recently discovered Naomi Novik’s Scholomance trilogy, I had only read her Temeraire books before.

There’s still a huge racial disparity in tech work forces. For one example, at Google according to their diversity report (page 66), their tech workforce is 4% Black versus 43% White and 50% Asian. Over the past 9 years (since 2014), that’s an increase from 1.5% to 4% for Black tech workers at Google.

There’s also plenty of news and research illuminating bias in trained models, from commercial facial recognition sets trained with >80% White faces to Timnit Gebru being fired from Google’s AI Ethics group for insisting on admitting bias and many more.

I also think it overlooks serious aspects of racial bias to say it’s hard. Certainly, photographic representation of a Black face is going to provide less contrast within the face than for lighter skin. But that’s also ingrained bias. The thing is people (including software engineers) solve tough problems constantly, have to choose which details to focus on, rely on our experiences, and our experience is centered around outselves. Of course racist outcomes and stereotypes are natural, but we can identify the likely harmful outcomes and work to counter them.