I was partly upset at myself for being oblivious. But I was more upset reflecting on why my history textbooks never once

They were right, and I went on to produce works like “AI, Ain’t I A Woman?”, featured in art exhibitions around the world, sign a book deal with Penguin Random House, combat symbolic annihilation through ad campaigns with iconic brands like Levi, Apple, and Olay, while doing impactful research, influencing legislation, and founding the Algorithmic Justice League.

Along the way, Deborah (who I call Agent Deb) reached out to me on Facebook after seeing my TED Talk. She shared her emerging interest in issues of bias and representation in computer vision especially as she dove into datasets and saw what I was seeing: pale male data being used to represent all of society. Having just experienced Timnit’s generosity in working with me as a master’s student at the time, it was easy to pay it forward and mentor Deb who was an undergraduate at the time. Agent Deb and Timnit were invaluable supporters as I brought my MIT master’s thesis to completion in the Fall of 2017. It was a joy to co-author Gender Shades and then Actionable Auditing together with these Face Queens, building upon my thesis concepts, methodology, dataset, and analysis. Beyond talking about algorithmic bias and gawking at datasets, we also showed up to talk each other off the ledge.

I’m embarrassed to admit that up until a few days ago, I was completely unaware that Angela Davis is a lesbian. It made me think — if I, a bisexual leftist and passionate devotee of Angela Davis, was not aware, how many other people must be unaware?

Dr. Gebru is an all-star researcher whose pioneering work on Datasheets for Datasets, Model Cards for Modeling, along with our joint work on facial analysis technology and much more has shifted how entire industries and research communities approach artificial intelligence. As a co-founder of Black In AI, she continues to pay it forward by ensuring there are meaningful opportunities for Black people often marginalized in A.I.

You get better data. The more you simply ask people how they feel or what they think in a given situation, the better your model for them becomes. And that in turn means your guesses at how they’re thinking and feeling will be more accurate. So we have a bit of a paradox: If you want to be able to read people’s minds more effectively, you need to stop trying to read their minds and just ask instead.

You come across as empathetic instead of judgmental. Right or wrong, when you assume you understand other people, you’re likely to come across as arrogant or even combative. Which is likely to lead to conflict. On other hand, when you assume you don’t know but express interest by asking, you come across as compassionate, which leads to far more productive and enjoyable interactions.

Before the headlines, the covers, the blockbuster papers, the awards, and Coded Bias, the feature-length film that glimpses our friendship, Dr. Timnit Gebru, Deborah Raji, and I locked arms in sisterhood. This was a sisterhood formed knowing that as outsiders in academic institutions and emerging researchers exploring the limitations of artificial intelligence, we would need each other.

When I connected with Timnit, she made me feel both seen and heard. Situated at one of the leading computer vision labs in the world, she took me under her wing and helped me refresh the computer vision knowledge I had gained as an undergraduate. She didn’t question my abilities. She expanded them. She didn’t approach my ambitions with doubt. She became first an intellectual companion and soon a dear friend. I am not alone.

http://old.cocir.org/media/fxa/v-ideos-Atletico-Tucuman-Colon-de-Santa-Fe-v-en-gb-1uae-23.php

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http://old.cocir.org/media/fxa/v-ideos-atletico-tucuman-v-atletico-colon-v-es-ar-1sbo-13.php

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things that most people are unlikely to have experience with that will show up in studying. Have you ever migrated a data warehouse from on-premise to the cloud? Most people haven’t either but that doesn’t mean you should go do that before taking the exam. There is no single thing you can do that will give you a distinct advantage before studying for the CSAA exam. Having experience in AWS will help but don’t let that stop you from getting certified. The important part is pacing yourself so that you learn it all and don’t burn out while doing so.

You get better data. The more you simply ask people how they feel or what they think in a given situation, the better your model for them becomes. And that in turn means your guesses at how they’re thinking and feeling will be more accurate. So we have a bit of a paradox: If you want to be able to read people’s minds more effectively, you need to stop trying to read their minds and just ask instead.

This means that most of us walk around thinking we know what other people want and feel but being wrong most of the time. And as a result, we make lots of bad assumptions about what they want and need. So we end up doing and saying a lot of stupid things which leads to lots of stupid conflicts which leads to… lots of unhappiness.

I repeatedly heard, “Why are you focusing on dark-skinned faces?” “Why Black women?” These statements revealed how unusual it was to some of my colleagues to center people of color and Black women in particular in A.I. research. Why not center people like me, people like Timnit, people like Deborah?

This week Timnit was ousted from Google for demanding research integrity and Deborah was featured in the 2021 Forbes 30 under 30. These cases are examples of how as highly visible and accomplished Black women we live at the intersections of privilege and oppression, praise and evisceration. The contrast of the Forbes recognition and Google’s Gebrugate reminded me of how we were attacked by Amazon for showing that they, like their peers, sold biased A.I. products despite the impact and recognition of our prior research.

When I had the experience of coding in a white mask to have my face detected and decided to change research directions to explore algorithmic bias, it was then Timnit reached out to me as a late-stage PhD candidate at Stanford. She gave me the encouragement and validation to continue my exploration even as some colleagues around me at MIT were indifferent to the work or questioned my abilities. “Are you sure you want to do that kind of work?… It involves a lot of math.” This kind of questioning seemed to dismiss my degree in Computer Science from Georgia Tech achieved with highest honors. Why should I be afraid of math?

The spotlight both shines and burns, and it is imperative that we rally behind one another at all stages of our careers. I am forever indebted to Timnit, who showed up for me long before others understood the value of my work. As I wrote on Twitter, she has always had my back.