12:35:22	 From  Kevin Moody : I thought the final four is april 3 and 5
12:35:49	 From  Makenna Girard (She/Her) : What time weds evening?
12:48:47	 From  Michael Finch : I don’t like the term Silicon Valley “refugee”. Like I feel like the term refugee makes me think of people who have escaped like terrible lives and war zones and things. What happens in Silicon Valley like “OH noooo I got a six figure salary and got to shop at Whole Foods ooooooo”
12:49:04	 From  Dillon Shipley : ^
12:49:35	 From  Makenna Girard (She/Her) : shows very little global awareness or perspective
12:52:31	 From  Kevin Moody : siri is male by default in the uk
12:52:48	 From  Makenna Girard (She/Her) : Would the women and people of color be listened to?
12:54:50	 From  Tristan Call : Having written a paper on defaults for ethics, I can confidently say people stick with most defaults the vast majority of the time
12:55:04	 From  Tristan Call : So the defaults have effects
13:00:13	 From  Mia Brasil : Apparently Siri only defaults to a male voice in four languages: Arabic, French, Dutch and British English.
13:03:34	 From  Daniel Kar : I found an article that talks about why AI assistant voices are female and it talks about how when Google first made “text to speech” technologies, it was much easier to work with higher pitched female voices.
13:03:36	 From  Tristan Call : According to one source I looked up for my paper there are at least 467 different ways they recorded for people to say 'there isn't a statistically significant difference. But we're going to heavily imply that there is an effect anyways because we want to have an interesting result.'
13:10:44	 From  Tristan Call : There's also the potential for ethical training to be used to excuse immoral behavior they were going to do anyways. For instance, the corporate ethicist that is hired not to police their AI ethics, but to justify their AI policies
13:11:23	 From  Tristan Call : Using whatever ethical loopholes necessary
13:17:17	 From  Tristan Call : One aspect of this is there are 'standard learning libraries' that are really common/widely used because they are free. So if one of those is biased, literally every algorithm that uses it for data will be encoding that bias in itself.
13:18:15	 From  Nate Remcho : Arthur slaps… everybody that you meet has an original point of view
13:24:01	 From  Makenna Girard (She/Her) : They had their hand raised*
13:24:05	 From  Dillon Shipley : mb
13:27:24	 From  Fisher Ng : I feel like there are so many ways to solve a problem beyond technology. In some communities I am connected with around the world like Papua New Guinea or Kiribati, the people of a community have learned to see the act of committing a crime as the worst form of punishment because it is both embarrassing and prevents them from becoming the best person they can be. In socializing concepts through transforming culture, something like facial recognition for surveillance is unnecessary.
13:31:16	 From  Megan Rice (she/her) : & how people judge merit is variable
13:34:09	 From  Paloma Whitworth (she/her) : Systemic issues contribute to apparent ‘merit’ and ‘lack of merit.’ I think evaluating merit should be looked at holistically rather than previously defined metrics.
13:35:24	 From  Makenna Girard (She/Her) : Also interesting how people who have less access to “merit” achievements might not know how to conform their resume or personal statement to get through resume screening algorithms so its another barrier to entry
13:36:07	 From  Sophia Whitworth : Also, workplace culture in STEM is not always very welcoming to women and minorities- this makes it really hard for some people in this groups to stick to a job
13:38:15	 From  Megan Rice (she/her) : Speeding is more ethical than going the speed limit. Other cars need to get out of my way