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Hi Josh.<br>
I think this would be cool to add at some point, I'm not sure this
is now.<br>
I'm a bit surprised by their "fairness report". They have 4
different metrics of fairness which are conflicting.<br>
If they are all included in the fairness report then you always fail
the fairness report, right?<br>
<br>
I think it would also be great to provide a tool to change
predictions to be fair according to one of these<br>
criteria.<br>
<br>
I don't think there is consensus yet that these metrics are "good",
in particular since they are conflicting,<br>
and so people are trying to go beyond these, I think.<br>
<br>
Cheers,<br>
Andy<br>
<br>
<div class="moz-cite-prefix">On 10/29/18 1:36 AM, Feldman, Joshua
wrote:<br>
</div>
<blockquote type="cite"
cite="mid:CAPUh_Fodf0O5m+Gmcd=Pa8dQXwv4qDu9qJzM-uiuJHAqCp4rmA@mail.gmail.com">
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<div dir="ltr">
<div dir="ltr">Hi,
<div><br>
</div>
<div>I was wondering if there's any interest in adding
fairness metrics to sklearn. Specifically, I was thinking of
implementing the metrics described here: </div>
<div><br>
</div>
<div><a href="https://dsapp.uchicago.edu/projects/aequitas/"
moz-do-not-send="true">https://dsapp.uchicago.edu/projects/aequitas/</a></div>
<div><br>
</div>
<div>I recognize that these metrics are extremely simple to
calculate, but given that sklearn is the standard machine
learning package in python, I think it would be very
powerful to explicitly include algorithmic fairness - it
would make these methods more accessible and, as a matter of
principle, demonstrate that ethics is part of ML and not an
afterthought. I would love to hear the groups' thoughts and
if there's interest in such a feature.</div>
<div><br>
</div>
<div>Thanks!</div>
<div><br>
</div>
<div>Josh</div>
</div>
</div>
<br>
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