gender bias

The notion of boards, you know, I'm sure all of you have been in front of different boards.

It's kind of this very high room, potentially high gender bias to some of the topics that have come up.

Any takeaway notes from there or experiences from there that would be very interesting to the audience?

I agree with what both of them said, right? The work is so much internal.

Okay.

So gender bias is real, and I'm sure you don't need me to tell you that.

And I'm gonna say furthermore, consistent with what we're talking about here today, gender bias makes it a lot tougher for us as women to establish executive presence.

So I'm gonna share some slides with you here.

Hang on.

Unlike other gangs, The Elephants rarely turned violent against each other.

They understood that intra-gang conflict would expose them to police scrutiny.

In 1910, a case went to court where a woman was sentenced for hoisting jewelry from an upscale store, but the judge remarked she had no criminal character, highlighting gender biases.

That ruling reflected a wider judicial tendency at the time to view women as morally upright and thus less likely to engage in serious theft.

However, evidence from police logs shows that some of The Elephants' operations were meticulously documented, including schedules for safe house meetings and coded communication.

It's so good.

And then it sets up like this theme of the movie, right, which is sort of like this gender bias and how hard she's going to have it compared to him, because they both have dreams of playing basketball professionally.

But she's going to face some barriers that he's not.

And that that opening scene like really sets that up.

And there was a review paper last year that basically described this as one of the most persistent problems in machine translation.

I had perhaps naively assumed that removing humans from the equation would mean greater objectivity and less gender bias in applications like this, but I'm starting to think that I was wrong.

[laughs] So the question now is, how far does gender bias in AI go? Katie, are you happy to go away and do a bit more research on this?

Absolutely.

It's fascinating.

Hi, Ella.

What have you got for me?

So I've talked with a few experts who have explained not only where there is gender bias in AI, but how and why.

Der AMS-Algorithmus, ähm, war das erste so medienwirksam wahrnehmbare große Ding, sozusagen.

Äh, der Berufsinformat war auch, äh, im, im, im, in der Kommunikation eine, eine Berg- und Talfahrt, also erst, ähm, flächendeckend in allen Medien ganz, ganz groß nach der Vorstellung, dann, äh, ein blaues Aug bekommen, äh, viel Kritik, ähm, sozusagen als unreif und wirklich wehgetan hat uns nur eine Kritik, nämlich die des Gender Bias, äh, mit dem wir uns schon auch beschäftigt haben davor, äh, damit die Beratungen eben möglichst nicht sich unterscheiden zwischen Mädel und Burschen, ja, grad im Bereich, äh, äh, Berufsinformation am Anfang von jungen Leuten.

Ähm, womit wir aber nicht gerechnet haben, ist, dass jemand überhaupt keine fachliches Interesse eingibt, sondern nur schreibt: „Ich bin ein Mädchen, was redest du mir? Ich bin ein Bub, was redest du mir?"

Ach so.

Da probiert einer was aus und sofort wird er von allen gehaut und wird gesagt, das ist schlecht.

Ähm, äh, jetzt, äh, mit der Weisheit der Rückschau sage ich, das ist ein sehr, sehr nützliches Ding, das wir erzogen haben.

Äh, wenn man, wenn man es ausprobiert und, äh, bewusste Fragen einbaut, die sozusagen Gender Bias nahelegen, dann merkt man, dass das Ding ein bisschen länger dauert, äh, länger denkt, weil wir dann noch einmal bei solchen Fragen eine Feedbackschleife, wo dann also noch einmal und jetzt ist das Ding also super korrekt, fast überkorrekt in der Antwort sozusagen, ähm, eingebaut haben.

Also wir haben's auch erzogen und haben da auch viel gelernt.