In this episode of In-Ear Insights, Katie and Chris discuss some of the silly questions interviewers had asked female guests on podcasts, especially questions related to gender and race. Learn what not to ask female guests, how to ask better questions of guests, and as a guest on someone else’s podcast, how to set appropriate boundaries and even provide guidance to the interviewer in advance.
Women in data science, AI, and marketing to follow in this episode include:
- Ayodele Odubela
- Timnit Gebru
- Hilary Mason
- Gini Dietrich
- Rehgan Avon
- and of course, Katie Robbert
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher Penn 0:02
This is In-Ear Insights, the Trust Insights podcast.
In this week’s In-Ear Insights, it is International Women’s Day today, as we record this, of course, news celebrations as as should be happening for more than just a day.
And I thought today, Katie, we would talk about a blog post that you and john wrote this morning, actually on things to not do with a podcast guest who identifies as female.
So, Katie, What’s the story? And where did this come from? And and what’s your take?
Katie Robbert 0:36
Well, so as a female, I have been interviewed on a few podcasts, you know, different articles.
And the thing that always strikes me and you know, john, and I joke about this a lot, is there’s questions that are asked of women that are not also asked of men.
And I think that a lot of this was really, you know, separately highlighted by a lot of what’s going on in the media these days.
And so there was the saving Britney Spears documentary that came out about a month ago, and a lot of what was a lot of the focus, were the interviews that she were she was participating in, and specifically the questions that she was asked that are not also asked of, you know, male celebrities, and there have been other examples of this.
You know, as we’re recording this, the Megan Markel interview with Oprah just came out last night.
And again, there was a lot of examples of questions and criticism that happen with women that don’t also happen with men.
Now, that’s not to say that men don’t face their own level of scrutiny.
But in this particular instance, women are often asked questions, gender first, and then skill set experience, you know, capabilities competency, later, like, a lot.
So a really good example that john and i were just sort of talking about is, you know, what is it like to be a female CEO? Well, when you flip that on its head.
When was the last time you heard someone ask the question? What’s it like to be a male CEO? You don’t hear that question.
So why are you asking a woman? Now granted, there’s a lot that goes behind that question, because women historically have had a more challenging time getting ahead, and you know, getting to positions of power.
So I understand why the question, but it’s a tired question.
Like, there’s other questions that you can be asking someone about their journey, their career path that don’t lead with, so you’re a woman that must suck.
Hmm.
Christopher Penn 2:47
Okay, is it? Is it the novelty of it? Because women are so rarely in positions of power compared to their male counterparts that makes people ask these questions, is it just flat out ignorance? What do you think is the reason why particularly interviewers have this, you know, sort of slant? Because you’re right, it is unusual, and it’s not as common in other minorities, it isn’t some and others.
But like, for example, I can’t say no one’s ever asked me what it’s like being a Korean data scientist is I ethnicity doesn’t come into it.
Yeah, no one has ever asked me what it’s like to be a male data scientists, you know, we’re gender wise and the vast majority.
So what do you think of the motivations behind some of these questions?
Katie Robbert 3:40
I definitely think that it’s, you know, it’s a mixed bag, I think some of it is the novelty of it.
You know, which is such an unfortunate way to think about it.
Like, I don’t want to be thought of as a novelty or a token or, you know, anything like that.
Like, I’m literally just me doing a job, regardless of my gender or ethnicity.
But, you know, I feel like that’s how people who don’t understand the experience, try to understand the experience.
So, you know, a lot of the podcasts that I’ve done have been with male interviewers, and so they don’t, they can’t put themselves in my position or they can’t understand my journey.
So they try to ask questions.
Unfortunately, the way that it comes across is it feels like to me, they’re trying to get that tragic story.
They’re trying to get that soundbite that they can it to me, it feels like a little bit of like that exploitation of like, well, she told me her horrific story.
I got the interview, you know, and I don’t know that that’s necessarily fair, because that’s not true of all interviews, but that is how it feels a lot of times is that they are trying to dig to get that salacious tidbit of information because a lot of times you know, the questions are Around, you know, so it must have been really difficult.
What were some of your challenges, it must have been really hard.
Can you tell me, you know, some of those like struggles that you went through.
And, you know, it’s no secret that a lot of us have been through, you know, different forms of abuse and harassment, as we’re trying to get ahead.
And I personally, and I don’t represent all women, I only represent myself, I don’t want to talk about it.
It’s not, to me, the most important part of my personal story, I’ve talked about it, where appropriate, but I don’t want to continue to talk about it to make that the headline that people lead with, I want to talk about the things that I’ve accomplished, you know, whether they’ve been, you know, becoming a CEO, or running a successful business or, you know, things that I’ve done historically, where I’ve worked in, you know, risk management in pharmacology, I would rather talk about that, because to me, that’s more interesting.
And if someone can learn from those, you know, those experiences, I would rather that then, you know, focus on, you know, the things that weren’t so great, it almost feels like interviewers are continually trying to put us in this victim box.
And I’m not interested in that.
I just, I want no part of it.
So ask me, you know, what it’s like to be a CEO.
Don’t ask me what it’s like to be a female CEO.
Christopher Penn 6:26
Do you find that you ever get these questions from other women when you’re being interviewed by them? compared to your male counterparts?
Katie Robbert 6:34
Absolutely not.
I can, I can say with confidence, if I go back, you know, to the past interviews, it’s definitely a different experience.
I’m actually working on some interview questions with a female interviewer.
And at no point was my gender brought up, it’s, you know, what’s the future of artificial intelligence? How should organizations be prepared for artificial intelligence? You know, no point is that.
So as a female, how should you be part, you know, prepared for artificial intelligence? It’s, that’s a silly question.
And you wouldn’t ask, you know, nobody would ask you, Chris.
So as a man, how should you be prepared for artificial intelligence? Like, that’s just goofy?
Christopher Penn 7:20
Where do you think the role is of gender in things like AI? Because obviously, with people like Dr.
timnit, gebru, formerly of Google, raising issues about, you know, biases and stuff, is that is that the context where it is appropriate in your, from your point of view?
Katie Robbert 7:36
Absolutely.
When it comes down to ethics and bias, and those training data sets, that’s where gender ethnicity, those things, you know, even age, age discrimination is still a big problem, that that’s where that belongs, is in making sure that the AI is doing better.
And so you need to have that diversity in creating the artificial intelligence.
But you don’t have to say, okay, so you’re a woman, you’re a man, you are African American, you’re Asian, great.
That’s our group, you guys go create something like that’s the wrong way to be thinking about it.
You know, I guess it’s, there’s a role for gender in artificial intelligence, but it’s not the way that people are thinking about it.
It’s not, oh, you’re a woman, and you can do math, or you’re a woman and you can lead a team, that’s the absolute wrong way to be thinking about it.
I was recently in an interview where, you know, the person interviewing me, I think, had really good intentions, and was trying to be complimentary, but it started to come out as women are really good, you know, taskmasters and caretakers, therefore, they should only be project managers.
And I had, I started to like, rein this interviewer back in and be like, I don’t think that’s what you’re trying to say, because it’s completely coming out wrong.
So let’s, let’s just try again, let’s let’s see if what you’re trying to say is people who, you know, have the aptitude to take in a lot of information and synthesize it and you know, turn it back out and communicate it as a single thought or people, regardless of gender, who have the aptitude to multitask, are going to be good at these kinds of roles, regardless of you know, male or female.
Christopher Penn 9:29
talk a bit more about that.
The redirection process what are some of your favorite techniques for redirecting somebody who is starting to go off the rails? You know, you’re in an interview, whether it’s on a podcast in a blog post at a job interview, even how do you skillfully put that person
Katie Robbert 9:48
you know, it’s, there’s no one single answer to that because it It all depends on the energy of the interview the energy of the interviewer Some of them are aware enough to realize, Oh, I’m starting to go down the wrong road, let’s walk it back.
And some of them don’t feel like there’s anything wrong with the questions that they’re asking.
And that, you know, is problematic.
A couple years ago, I did an interview, where the person kept asking question after question about the me to movement.
So the me to movement, for those who might not be aware is when, you know, women have been standing up and speaking out about some of the harassment and abuse that they’ve experienced in the workplace.
And just in general, for that matter.
And so this interview, were really wanted to capitalize on that conversation, and kept asking question after question after question about my experiences with harassment and abuse, and it just, it wasn’t the interview that I signed up for.
And so, you know, I had to keep saying, like, I’m not interested in answering that question, can we talk about something else? Or, you know, these aren’t questions, you know, that I’m feel comfortable asking, and what happens when you have to say, I don’t feel comfortable answering those questions is, sometimes the interviewer will be like, oh, I’ve really hit on a, you know, sensitive spot.
And it’s like, No, not necessarily.
I just don’t want to talk about that particular topic.
And so it’s, unfortunately, it’s a 5050, like, give and take with the interviewer.
Sometimes the interviewer thinks that they’re really digging into something important, when really, they’re just being annoying.
The best way to redirect Chris to your question is to say, you know, that’s not a topic that I think is appropriate for this podcast, can we move on to these other things, and it’s okay to actually stop the interview and say, This is not, you know, appropriate, this is not what I signed up for, if you can’t respect that these are not topics that I feel comfortable talking about, I’m going to have to end this and that’s perfectly acceptable.
Other side of that coin, is that a lot of times we be like, Oh, well, don’t be so aggressive, or Don’t be so sensitive.
And that’s a whole other, you know, issue is that a lot of times women, regardless of their behavior just can’t win.
Christopher Penn 12:19
Do you think that the blog post that you wrote, or a distilled version of that is something that you’re going to add to, like your press kit and things, you know, in advance of an interview saying, like, these questions are not acceptable for the show? Just, you know, we’re not saying that you’re going to ask them.
But, you know, here’s, here’s a list of what not to do.
Is that something that belongs in the process, particularly for people who are going into interviews who happen to be women?
Katie Robbert 12:43
I do, I think so I think that it’s important for us, you know, anyone male or female to set our boundaries and to hold those boundaries, because it’s the only way, you know, that we can feel like we’re entering into, you know, a safe space or, you know, a, an interview where you going to be respected.
If the person can’t respect those boundaries, it’s probably not the right interview, you know, for you to be participating in, because it means that you are continually going to be put in a position that you feel very uncomfortable in.
And that’s just not fair.
Like, we’re, I would like to believe that we’ve evolved past the point where, you know, we have to continually put people back in these tiny little boxes that they don’t belong in.
And so, you know, Chris, to your initial point, nobody’s ever asked you, what is it like to be an Asian data marketer? What is it like to be a male data marketer? You know, what would you do if somebody said, So, Chris, you’re Asian, tell me about that.
Like, what would you be?
Christopher Penn 13:42
I mean, like, I don’t know, any different.
Literally, I couldn’t tell you what it’s like, to not be bad.
In the same way, I can’t tell you what it’s like to not be male, because I have no frame of reference.
So to me, it’s, it’s sort of a silly questions like, why would you ask that? I can’t answer.
Like, I can’t crawl inside your head yet.
I will someday, when we all have circuitry implanted there, but right now, I can’t and understand your reality.
So I can’t say like, how my reality different than yours cuz I can’t I have no idea.
You know, there are lots of stereotypes, you know, for gender and race and things like that.
But those stereotypes really don’t inform.
You know, the experience like do I eat a lot of rice? Probably, I don’t know, I don’t know what other people do in their homes.
I do know that.
You know, nutritionally, people are having a disaster.
But culturally, I can’t tell you how it’s different.
Can I give examples of like discrimination? Sure.
And crazy assumptions people make like people make assumptions.
Like, if you’re Asian, you must play piano, be good at math, and do the martial arts.
And only one of those three things is true of me.
So I can’t just like I would imagine this A whole laundry list of assumptions that people make about women like, Oh, you know, you must have a hard time balancing, you know, family and work life and things like that.
So there’s those assumptions that people go in with, but they always it always comes out like, what does this even have to do with the thing that we’re talking about?
Katie Robbert 15:19
And that’s exactly it.
And, you know, you know, even as you’re sort of talking it through, Chris, you said, this is a silly question.
That’s exactly it.
You know, I can’t speak to any other ones experience, I can’t speak to a non woman’s experience, I can only speak to my experience, and I get to choose what I want to talk about publicly.
You know, I do think that it’s, you know, to your question about, does this belong in a press kit? I absolutely do.
Now, the, you know, I do belong to women organizations.
And so women in analytics is an organization that I think is really great that I like to talk about, you know, quite a bit.
But what I’ve experienced within that community is that at no point has someone said, so you’re a woman, tell me about that.
It’s the goal of the organization is to boost more women in analytics and have, you know, women led speaker panels, and you know, women led communities.
But there’s not a focus and emphasis on, well, you’re a woman, you must, you know, you must participate.
And you must talk about your experience as a woman, there’s a lot of men who belong to those to that same organization.
Because they’re trying to build something inclusive, what they’re trying to do is just make sure that women have the same type of authority and voice that men, you know, take for granted.
Christopher Penn 16:47
So, on that note, who are some of the women that you look up to in marketing enamel, it’s in the data space, who, maybe your your top your 123 people that you think, have interesting perspectives to share, or that you learn from?
Katie Robbert 17:03
Well, definitely, you know, Reagan, Avon, She’s the founder of women in analytics, and I’m really enjoying following what she’s doing and working on and what it is that she’s trying to build.
And again, it’s the community is called Women analytics.
But at no point, while you’re in it, do you feel like this is like a feminist, you know, estrogen fueled thing, it’s literally just a place that gives women more opportunities that they might not otherwise have.
Gini Dietrich is another one who I very much look up to.
She’s been, you know, teaching me a lot about just business in general.
And we have similar home lives.
And so just being able to learn from her in terms of, you know, when I run into this type of a challenge, you know, what are some examples of how I could navigate it, not because she’s a woman, but because there’s a lot of similar scenarios that we have going on, we have a lot of similar interests.
And so those are two people that I definitely look up to in terms of, you know, people that I would want to aspire to be more like.
Christopher Penn 18:14
Very cool.
Yeah, a couple of folks that I follow that I think are really interesting.
io Dilly o dibella is a data science evangelist.
She is a product person in the black and AI and data science space, and has a lot of really good perspectives, I think on not only, you know, machine learning and data and AI, but also, you know, her perspectives is both someone who is black and is and is female.
timnit gebru, as we mentioned before, somebody who is very much worth following to see, you know, her perspectives on fairness and AI, I think is really a good first of all, Dr.
Hilary Mason has, I think, done more for at least getting people’s heads on straight when it comes to ethics and data science, then probably, you know, half of the guys in the space because her book ethics and data sciences is a fantastic read.
It’s it’s required reading for anybody who’s doing any kind of machine learning projects.
So there will be some folks that if you want to go get some new perspectives, some very, very valuable folks to follow.
So any final parting thoughts Katie, on on International Women’s Day about things that everybody regardless of gender, should be doing to provide better interviews and better interactions with with everyone they work with?
Katie Robbert 19:30
You know, just you know, do your homework, use your brain.
If you haven’t done at least a little bit of research on the person they are interviewing.
Don’t be lazy, don’t just say, Oh, well, they’re a woman so we can talk about that or, you know, they’re a person of color so we can talk about that.
Don’t make that assumption.
And don’t you know, if you’re if you are interviewing a woman and she happens to be okay with, you know, answering some of those questions.
Don’t also assume that it’s Okay to introduce her as a lady marketer, or a female CEO, I am a CEO, period.
I’m a marketer, period, I will answer some questions about my experiences, because I happen to be a woman.
So that will be my point of view.
But don’t assume that my title is you know, Katie robear female CEO that is not what my signature says.
When in doubt, just ask the person you’re interviewing what they’re comfortable with.
Period.
Christopher Penn 20:28
Just ask awesome.
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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.