EmoClass stringclasses 10
values | EmoAct float64 1 7 | EmoVal float64 1 7 | EmoDom float64 1 7 | SpkrID int64 1 3.33k | Gender stringclasses 2
values | Split_Set stringclasses 2
values | transcript stringlengths 1 353 ⌀ | emotion2vec_features listlengths 1 1 |
|---|---|---|---|---|---|---|---|---|
N | 4.6 | 3.6 | 4.4 | 1,270 | Female | Train | ... which is that a reasonable person could look at the model and have some sense of why it predicted the way it did for them as opposed to- | [
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X | 4.8 | 2.6 | 5.4 | 499 | Female | Train | ... then we predict false. you are not a proponent of transparency. | [
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N | 4.4 | 4 | 4.8 | 499 | Female | Train | ... there was the second piece i wanted to say, which is, how hard is that... technically, it seems to be, and i won't say that we know this- | [
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N | 4.6 | 3.6 | 4.4 | 1,270 | Female | Train | ... to be that in some cases- | [
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N | 3.2 | 3.8 | 4.6 | 1,269 | Male | Train | ... different kinds of shapes of the head. is it a flat headed? is it a very- | [
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X | 4.6 | 4.2 | 4.2 | 1,270 | Female | Train | ... that they they're making a decision based on this more complicated formula, not a [inaudible 00:06:29]- | [
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N | 5.6 | 4.4 | 5.4 | 499 | Female | Train | ...so, i completely agree what cynthia says is absolutely right. and here's how i think about it, let's imagine, instead of deciding that mushrooms are poisonous, we're deciding why you got turned down- | [
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A | 4.8 | 2.8 | 5.4 | 499 | Female | Train | well, what could i do such that next time that won't happen? [inaudible 00:00:06] | [
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N | 4.2 | 3.8 | 4.4 | 498 | Male | Train | a lot of it can't, at least in the criminal context, can't be boiled down to what could i have done differently at the time of scoring, except for perhaps differential answers. | [
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N | 4.6 | 4.4 | 4.6 | 498 | Male | Train | again, i don't know how much that's going to advance the objectives of the public understanding what's happening | [
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X | 4.6 | 4.4 | 4.6 | 498 | Male | Train | [inaudible 00:00:27], but i'll argue even more importantly, because if you have an ordinal risk scale from one to six, for example, | [
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N | 4.6 | 3.4 | 4.8 | 1,270 | Female | Train | does the decision maker actually know what the difference is between a two and a three or a three and a five, perhaps, perhaps not. | [
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A | 4.666667 | 3.5 | 4.333333 | 1,270 | Female | Train | could you say more about that? i don't understand what you're saying well enough. | [
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N | 5.2 | 3.4 | 5 | 498 | Male | Train | it spits out three outputs, a risk of failing to appear on a one to six scale, a risk of committing a new crime. | [
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N | 4.8 | 4.4 | 5.4 | 498 | Male | Train | it would be one thing to spit out those scores, hand those to the judge initial appearance and say, your honor, let us know what you think about detention or release. | [
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C | 3.2 | 3 | 4 | 1,270 | Female | Train | one thing that chris's remarks illustrate is just the boring aspect of the critical, not the brain remarks. | [
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X | 4.666667 | 3.666667 | 5 | 498 | Male | Train | but really quickly to, to your point, jonathan, one of the things that we've learned in our ongoing and prospective evaluation | [
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A | 2.6 | 3.8 | 4.4 | 498 | Male | Train | [inaudible 00:01:27] psa is that, that data quality issue is one that is highly, highly fraught across the country. not because, | [
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S | 3.6 | 3.4 | 3.6 | 498 | Male | Train | so i believe that the data that are collected in each system are usually collected quite well. | [
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C | 4.4 | 3.6 | 4.6 | 498 | Male | Train | jail databases, prosecutor databases are all housed at the county level and none of them ever talk | [
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N | 4.2 | 4.4 | 4.4 | 498 | Male | Train | [inaudible 00:01:51] without that, algorithms are somewhat useless because you can't track the inputs and the outcomes in a reliable manner. | [
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X | 3.8 | 4.4 | 4 | 1,270 | Female | Train | you could see a lot of problems with those models. and it's almost a recommendation to say, "i'd rather trust a machine learning, getting feedback from the world" [inaudible 00:02:11]. | [
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N | 4.4 | 4.4 | 4.8 | 499 | Female | Train | and there's sort of this implicit assumption here that well, clearly, i mean the, the suspicion around machine algorithms, | [
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N | 4.8 | 4.2 | 4.2 | 499 | Female | Train | i believe misplaced belief that humans are actually fair and unbiased. | [
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A | 4.888889 | 2.777778 | 4.888889 | 1,270 | Female | Train | right now, no, we know that, that's not true. we | [
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A | 6 | 2.8 | 6.6 | 1,270 | Female | Train | just know that, that's not true. is there anyone here who wants to argue that fact? | [
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U | 4.8 | 3.6 | 4.6 | 1,269 | Male | Train | i'm not even sure how you can make any statements about the two of them, but i'm hoping cynthia will at some point, solve that problem. but i guess i would like to live in the [inaudible 00:02:46] | [
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N | 3.8 | 4.2 | 4.2 | 499 | Female | Train | where, the person who's ultimately making the decision, so assuming it's a recommendation, | [
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N | 5 | 4.6 | 5 | 499 | Female | Train | [inaudible 00:02:52] and in fact, take a model with some understanding of that it might be biased in certain ways and maybe there are ways | [
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N | 5.6 | 4 | 6 | 1,269 | Male | Train | [inaudible 00:03:00] prompt them to think about it, they actually do a little better and so maybe the model serves as a way to do that. | [
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F | 4.2 | 3.8 | 4.2 | 498 | Male | Train | probably not going to do a lot of work. i'm skipping over a lot of very specifics [inaudible 00:03:15]. | [
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S | 3.2 | 3.2 | 3.4 | 1,270 | Female | Train | i think that they're problematic, but i don't [inaudible 00:03:21]. | [
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S | 5.2 | 4.6 | 4.6 | 1,270 | Female | Train | you were talking about [inaudible 00:03:23] so yeah, so i am totally fine with private companies building these models, but i see no reason that they [inaudible 00:03:32]. | [
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H | 5.2 | 5 | 5 | 1,270 | Female | Train | like when i'm truly bored, i read the insert with the prescription `drug | [
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X | 4.8 | 4.6 | 4.8 | 1,269 | Male | Train | [inaudible 00:03:36], that little diagram of a molecule and like that's pretty and then in the middle, it's like pharmacology, it's like | [
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X | 4.6 | 4.4 | 4.8 | 1,268 | Male | Train | [inaudible 00:03:42]. let me show you the following research papers about people who have a condition exactly like yours and what the results are. this is a doctor [inaudible 00:03:50]. | [
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N | 3.6 | 4 | 5 | 1,268 | Male | Train | but we subject these ideas, whether it be a risk assessment, whether it be some new idea for enhancing civil legal justice, | [
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N | 5.2 | 4.6 | 5.2 | 1,268 | Male | Train | in what cases i'm not aware of specifically around the country, but giving such tools, i mean, think about the | [
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C | 2.888889 | 2.222222 | 4.333333 | 1,268 | Male | Train | [inaudible 00:04:08] lord knows how many officers need, because they're using either improper or just wrong [inaudible 00:04:17]. | [
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X | 4.4 | 4.6 | 4.6 | 1,268 | Male | Train | [inaudible 00:04:19] other point. so, as somebody who builds software for a living, like every piece of software has bugs. and so this was the response to should these models [inaudible 00:04:29]. | [
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X | 4 | 5.2 | 3.8 | 634 | Female | Train | i'm like, i guess nobody wants to hear. i'll just sit here and maybe- | [
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C | 4.4 | 4.4 | 4.4 | 1,222 | Male | Train | -produces inequality, and basically gives the more privileged the power to choose jobs- | [
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S | 3.8 | 3.8 | 4.2 | 1,222 | Male | Train | will you raise that we should just not use these programs? but- | [
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N | 3 | 4.2 | 3.6 | 1,222 | Male | Train | ..just will opt in, no matter what. so, is there something like- | [
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N | 3.4 | 4.4 | 4 | 1,222 | Male | Train | i see several hands, i think you're first. yeah. | [
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X | 4.8 | 4.4 | 4.2 | 1,221 | Male | Train | just use your speaker.it is an enormous pleasure- | [
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X | 3.4 | 4 | 4.2 | 1,222 | Male | Train | -numberyes.why is that? why don't you want people to email or tweet you? can you explain? | [
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N | 3 | 4 | 3.4 | 1,222 | Male | Train | groups in brazil, that also drive to set up help line- | [
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H | 5 | 6 | 4.8 | 1,222 | Male | Train | thank you for these presentation and also, thank you for all the work that you've done, because i think- | [
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X | 3.4 | 3.6 | 3.8 | 1,222 | Male | Train | ..in dealing with online service providers, and without saying names or saying what company, who said- | [
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N | 3.2 | 3.4 | 3.6 | 1,222 | Male | Train | i think you said you had around- | [
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N | 3.8 | 4.2 | 4.8 | 1,222 | Male | Train | what is the context of how many people you're helping, given how much harassment you think actually takes place? the [pak-sady 00:04:13] citizens on, i guess- | [
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H | 3 | 5.2 | 3.4 | 1,221 | Male | Train | efforts to make some progress, and i'm very grateful that [nigath 00:04:22] his come- | [
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N | 4.2 | 5 | 4 | 1,221 | Male | Train | welcome to david kaye, u.n. special rapporteur on freedom of opinion and expression, and you were appointed in this mandate in 2014- | [
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N | 3 | 4.2 | 3.6 | 1,221 | Male | Train | i wanted to bring up the... sorry- | [
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N | 3.6 | 3.6 | 3.2 | 1,221 | Male | Train | yeah, sure. i thought you'd nev- | [
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N | 3.625 | 3.625 | 3.625 | 1,222 | Male | Train | i don't know, i always look at these things also, like what is the alternative, right? if you weren't there to make all these efforts, look [scrim 00:04:51] now, but just imagine how bad- | [
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N | 3.2 | 3 | 2.8 | 1,222 | Male | Train | there are human rights violations or denial of- | [
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