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cairocode
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MSP_POD_NLP

Dataset card Data Studio Files
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communications law that in the case of an emergency, they can just shut down telecommunication services. and illegally allows shutdown that would be so against-
N
4.6
4.4
4.6
1718
Male
... the state allows itself to enact and-
N
4.8
3.8
3.8
1718
Male
regulations by states also want to allow themselves some degree of leeway...
N
5.4
4.2
3.8
1718
Male
special cases like combating organized crime, drug trafficking-
X
4.6
3
4.2
283
Male
and so on, and terrorism, also allow for extra powers by the states.
N
4.2
3.8
5
283
Male
we also can identify things that we can-
X
3.6
4.4
3.8
1718
Male
advocate against, when those abuses are allowed.
N
4.2
3.6
4.2
1718
Male
i'm not even talking about self-regulation, because in the absence of regulation..
O
4
3.6
4.2
1718
Male
we see that the rule could be some level of abuse by private actors as well.
X
3.8
3
4.2
1718
Male
the founding director of the digital freedom fund and has been an advisor to the clinic since 2016.
N
4.8
4.2
4.4
1718
Male
there will be time at the end as well.
X
4.8
4.8
4.4
1718
Male
this is great. thank you. yeah. thank you so much for having-
H
4.4
5.4
4.4
1718
Male
things like that should not happen.
H
4.4
4.2
4.2
1718
Male
pleasure to be back at harvard.
H
3.2
5
4.4
1718
Male
you, [yan 00:00:49]. i think that's a great point to think about the competencies and the variety in the case law between courts.
X
4.4
4
4
1718
Male
... delighted to be back in my old stomping grounds. the first classroom that i taught in.
H
4.6
5.6
4.2
1718
Male
influential, and i think the european court of human rights is one of those policies.
N
3
4.2
4
1718
Male
[inaudible 00:06:24] i wonder if we might turn to a different way that we can engage with governments around issues-
N
4
3.8
4.2
Unknown
Unknown
is doing completely the wrong thing in designing this...
X
4.8
2.8
5.4
1718
Male
to have us and european lawyers more closely in dialogue, more closely coordinated with relation to your brooklyn project.
H
3.8
4.6
4.6
1718
Male
what i teach a class called product management and a society over there.
N
3.6
3.4
3.4
1718
Male
look, to folks who come up afterward to talk a little bit more about that if we don't get into it today and...
N
4.2
4.2
3.6
283
Male
one of the most disabling environments on the planet. i mean, that's just a fact, because-
N
4
3.4
4
283
Male
swat team, the tech swat team of the united states government.
N
2.2
3.6
3
1401
Female
quo which doesn't feel like it lines up very well with an environment that's ripe for technical innovation...
N
4
3.4
4.2
1616
Male
i mean, there was a case for saving taxpayer dollars in federal projects.
S
3.2
4
3.2
1616
Male
... and then once you buy it. so as a government we're bad at-
N
3.4
4
3.8
Unknown
Unknown
but it actually really doesn't. it's going to cause a lot more problems. or you're planning on shipping this in two months and it's going to fail and background checks are just still not going to happen.
X
4.6
3.4
4.4
1616
Male
this is very troubling. this is criminal behavior.
X
4.2
2.6
4.6
Unknown
Unknown
illustrious panel. we've got ruben playing the phil donahue role.
N
3.4
4
3.8
1617
Male
me, i could tell you about the system i wrote in the 1970s for the state government that never saw the light of day because of-
X
3.6
3.2
4.2
1617
Male
well that is, we bring in people who are federal employees, employed by the federal government who can go around and work on different projects.
N
4.4
4.2
4.4
1401
Female
period. i think that would be a horrible metric, and i would love to actually chat more about your thoughts on-
H
3.8
5
4.6
1618
Male
... piggyback on that too and i'd say that i think that saving money-
H
3.4
4.4
3.8
Unknown
Unknown
be able to generate some sort of return on that investment. that doesn't necessarily have to be in the form of hard cash and taxpayer dollars but it can be some...
X
4.8
4.2
4.4
1618
Male
your concern. there are so many of them, and i think comes-
X
4.2
4.4
4.2
1618
Male
as part of the technology development process the whole time. and another one of the key findings is...
N
4.2
5
4.2
277
Female
if you went and worked for the federal government, you're like, "hey guys, i have a great idea. we're just going to-"
H
4.6
4.6
5.4
277
Female
yeah. actually this is not quite the same but there...
H
4.8
4.8
4.6
1619
Male
people not coming into the space-
H
4.8
4.6
4.4
1619
Male
professors, i think a lot of professors and chris you can...
N
3.4
4.2
3.8
1619
Male
we are thrilled to have virginia eubank...
H
4.4
5.6
4.8
1619
Male
generally just how many of these issues are playing out across society, and how high tech tools are affecting and impacting the poor.
S
4.333333
4.166667
4.333333
1619
Male
please do make a purchase and stick around afterwards, so that she will-
N
3.2
3.8
3.8
1619
Male
guessing. so please do ask questions and you can do that in...
N
3.8
3.4
3.8
277
Female
and her writing about technology and social justice has appeared in the american prospect, the nation-
N
4
4.6
3.6
1619
Male
to all the folks who have worked so hard to get me here on time and in one piece, to have this conversation with-
X
4.4
4.8
4
1619
Male
... two things that are important to me about how automating and equality's a bit different. so one is that-
N
4.6
4
3.6
Unknown
Unknown
ladies in communities who feel like they are being targeted by these systems, and that really shaped the way i was able to tell the-
S
5.2
4.4
5.2
Unknown
Unknown
... what an incredible generous act it was for people to share their experience with me.
H
4
5.4
4.2
Unknown
Unknown
... since they're unhoused or homeless or their family is involved in a child welfare investigation.
N
4.6
3.4
4.8
Unknown
Unknown
so i just want to make sure i start by acknowledging that the book wouldn't exist without people who took that kind of risk and made themselves really vulnerable.
H
4.2
5
5
Unknown
Unknown
start by acknowledging that incredible contribution to the work. and the other thing that's a bit different about the way i tell this story, it...
N
5
4.6
4.8
277
Female
... specific work, which is to talk about what i think of as the deep social programming of the tools that we're now using in public services across the united states.
N
4.2
4.2
4.6
Unknown
Unknown
which is an invisible institution that profile polices and punishes the poor when they come into contact with public services. in the-
X
4.6
2.4
4
1620
Female
i talk about a statistical model that's supposed to be able to predict which children might be victims of abuse or neglect in the future in allegheny county, which is the-
X
4
3
4.4
1620
Female
this is also always when i thank my editor, because the book originally started with a 90 page history chapter.
H
4.4
5.6
5
1620
Female
in 1819. so the reason i start in 1819 is, this is the moment where there's a really big economic dislocation in the united states...
N
5.2
3.4
4.6
Unknown
Unknown
poor children, and by interaction, they generally meant sort of leasing children for agricultural or domestic labor under apprenticeship.
S
4
3.6
4.4
Unknown
Unknown
year annually. so it's like a third of folks who entered them every year died. and the reason i-
U
4
3.6
5
1621
Male
... within these systems that continues to produce bad outcomes for poor-
O
5
2.8
5.2
Unknown
Unknown
and there's the administrators and other folks involved in the process of creating the systems are really good.
H
4.8
5.4
4.4
1622
Male
advertised call centers, there's several of them across the state. they encouraged folks who were applying for public assistance to do so over-
H
4.6
4.2
4.6
1624
Female
the next in the queue. from the point of view of applicants and recipients of public assistance in indiana-
H
4.4
3.6
5
1623
Female
four page application, or the document processing center had scanned in a piece of documentation upside down, or dropped it behind a-
X
4.4
3.8
4.2
1624
Female
it also meant that the burden of figuring out what had gone wrong and solving it fell almost entirely on the shoulders of poor working families in-
S
4.6
3
5.2
1623
Female
of the experiments. a 54...
N
5
4.4
4.4
1623
Female
against it. they pushed back against the state, and they were so successful that the governor actually canceled the contract with i-
U
4.4
4.8
3
1624
Female
because the state had breached the contract. that case stayed-
N
5
3.2
4.8
Unknown
Unknown
so i'm going to talk now about the allegheny county algorithm.
N
4.6
4.8
4.6
1624
Female
but i'll do bits and pieces of this, and we can reengage in conversation if you feel like there's anything i've missed.
X
4.2
3.8
4.6
1624
Female
so, the allegheny family screening tool is a statistical model that's built on top of a data warehouse that was built in 1999 in allegheny...
N
4
4.2
4.4
Unknown
Unknown
county. so the data warehouse receives regular data extracts from 29 different agencies across the county. as-
N
4.8
4.2
4.6
1625
Female
... the limitations of that data set then have become a really important part of the tool-
N
4
4.6
4.6
Unknown
Unknown
... reality of experiencing this tool though from parent's point of view-
N
5.4
3.6
5
1626
Female
may actually exist. now, i also spent a lot of time with-
N
4.6
3.8
4.8
1626
Female
frontline case workers in this system, particularly with intake call center workers. intake call center workers are the folks who receive reports of abuse or neglect.
A
5
3.8
4.6
1626
Female
whether or not they should screen in each case in, for a full investigation. or whether they should screen it out as not rising to the level of abuse and neglect or...
N
5.2
3
4.2
1626
Female
the suburbs or in isolated housing won't show up in the data warehouse because they're not the folks in allegheny county who are getting county help.
X
5
3.4
4.6
1626
Female
... were also really concerned about the limitations of that data set but they were concerned-
N
4
3.4
4.4
Unknown
Unknown
eligibility rules that didn't fall until they were directly challenged by the national welfare rights movement in the late 60s and early 70s. that's created-
N
4.8
3.8
4.4
1626
Female
all sorts of discretionary excesses in the system that are both human and institutional and really important to address.
N
4.4
4.2
4.2
1626
Female
african american children are in foster care at rates that far exceed their actual proportion of the popula-.
N
4.4
3.2
4.2
1626
Female
8% of children in foster care in allegheny county were black or biracial, and-
X
3.2
3.2
4.6
1626
Female
only made up 18% of the youth population. that's more than twice where they should be, given their proportion of the population.
N
4
3.6
4.6
1626
Female
... intake call screening is not actually the point at which discrimination is entering the system. in fact, it's entering much earlier.
N
5
3.6
4.2
Unknown
Unknown
percentage difference, or 350% percentage difference. i think one of the really interesting questions this begs is-
N
4.4
4.6
3.6
1627
Male
bias. is that something we can attack, or address, or confront with automated systems?
X
5.2
4.2
4
1627
Male
issue, not a data issue. although of course, the two are deeply related.
N
3.6
3.4
3.6
1627
Male
angeles county. just for a sense of the scale. and some-
N
4.8
4
4.2
1628
Female
and so this is a absolutely critical humanitarian crisis in...
O
5.6
2.8
5
1629
Female
particularly frontline caseworkers want a little help making the incredibly difficult decision of who among the hundred people they see every week gets access to the two or three resources...
O
4.8
3.8
4.6
1630
Male
completely understand the impulse to try to create a more efficient, and more rational, and more objective system for matching need to-
N
4.2
4
4.2
1630
Male
just think of it as a database, is not quite true, but think of it as a database for now.
H
4.2
5
4.2
1630
Male
absolutely. they have been able to match about 9,000 people with some kind of resource through this system. that doesn't necessarily-
N
4.8
5.2
3.6
1630
Male
and why they believed that is because the [inaudible 00:06:29] actually asked some really intense and borderline invasive questions.
N
4.4
4
4.4
1630
Male
interacting with this process, because coordinated entry has become the front door for pretty much all housing resources in los angeles county. so-
H
5
4
4.6
1630
Male
the system. so though you sign this really sort of intense informed consent that lasts a really long time-
N
4.6
4.2
4
1631
Male
161 agencies who share this information, who share this data across-
H
3.2
3.8
4
1631
Male
because of the federal data standards is the los-
N
4
3.6
4.6
1632
Male