Think that discussing the ethics and impacts of data science is important, but also, kind of fun? Our next data science ethics bookclub is on AI and gender and we’d love you to join the discussion. You are welcome to pick from this reading list, depending on your interest and the time you have:
Book : Invisible Women: Data Bias in a World Designed for Men by Caroline Criado Pérez
News article : “The deadly truth about a world built for men – from stab vests to car crashes” by Caroline Criado Pérez
Crash-test dummies based on the ‘average’ male are just one example of design that forgets about women – and puts lives at risk
Journal article : The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition by Os Keyes
Automatic Gender Recognition (AGR) is a subfield of facial recognition that aims to algorithmically identify the gender of individuals from photographs or videos. In wider society, the technology has proposed applications in physical access control, data analytics and advertising. Within academia, it is already used in the field of Human-Computer Interaction (HCI) to analyse social media usage…I show that AGR consistently operationalises gender in a trans-exclusive way, and consequently carries disproportionate risk for trans people subject to it.
Report : I'd blush if I could: closing gender divides in digital skills through education by Unesco - specifically we're looking atThink Piece 2 - The Rise of Gendered AI and its Troubling Repercussions, pages 85-146
News article : “Digital assistants like Siri and Alexa entrench gender biases, says UN” by Kevin Rawlinson
Assigning female genders to digital assistants such as Apple's Siri and Amazon's Alexa is helping entrench harmful gender biases, according to a UN agency.
- Gender and AI in the workplace link
Warming up
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What was your main takeaway from the readings?
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Can you think of an item or product in your life, that was designed for the opposing gender? Tip: Think of your daily routine. Think of how you could redesign this so it becomes more inclusive.
The gender data gap
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Why has the gender data appeared? Is it rooted in misogyny (intended to deliberately harm of ignore women) or an absence of thought?
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What do you feel needs to be done to reduce, even eliminate this gender gap?
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The author, Perez believes we need to start collection sex-disaggregated data. Do you agree? Why, or why not?
Data, AI and gender
- Invisible women places a focus on a binary concept of gender (male and female). How we can ensure inclusive design for all - not just those identifying with binary genders? Discuss the limitations of Invisible Women with regards to this.
- Automatic Gender Recognition - assuming a gender based on features in the data - often ignores trans individuals and others who are non-binary. What can/should be done to avoid this? In what situations does AGR have an overall beneficial use case?
Data science x social activism
- The following statement argues that our awareness of data gaps only goes so far, and that we need more to lead to change. Discuss!
“The power of data to shame people into making society fairer, it seems, goes only so far. Beyond a certain point, it's difficult not to conclude that they don't particularly care. What should worry us more than the data gap, then, is that huge and seemingly intractable don't-give-a-damn gap.” - Invisible Women by Caroline Criado Perez – review, The Observer, 2019
For tweets from the evening see here.
AI and gender: the ‘default male’ and the unspoken gender binary