Digital Possibilities: Capitalism, Tech, and the Fight for a Feminist Internet

The genie is out of the bottle. We need to move forward on artificial intelligence development but we also need to be mindful of its very real dangers.”

— Stephen Hawking, 2017, Wired interview

The virtual world once felt like it could shape a different future — one more democratic and equitable. But capitalism — as well as its siblings, including racism and sexism — are turning digital spaces into the same corporatized, white- and male-dominated ones we’ve known for centuries. How have lopsided power structures shaped our digital experiences? What can virtual communities and digital movements reveal about the potential, still, for a reclamation of the democratic possibilities of technology? And what can we do now to build an intersectional, feminist future online?

Find out by tuning in to our most recent Zoom of Our Own conversation with Communication and Science and Technology Studies scholar Breigha Adeyemo, journalist and DIGITAL SUFFRAGISTS author Marie Tessier, social systems scientist Riane Eisler, and AEOO’s Digital Director Carmen Rios! We explored how lopsided power structures shape our digital experiences… and what virtual communities and digital movements reveal about the potential, still, for a reclamation of the democratic possibilities of technology.

DEFINITIONS

Artificial Intelligence or AI: the theory and development of computer systems to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI techniques have transformed businesses worldwide, automating time-consuming tasks to win insights into collected data via quick pattern recognition.

Narrow AI: AI now operates in its first stage, called “narrow,” meaning it looks intelligent but functions under a limited set of rules and factors to simulate humans. Its programs use NLP (Natural Language Processing) to perform tasks and communicate. Examples include virtual assistants like Rankbrain by Google, manufacturing and drone robots, IBM’s Watson, Siri by Apple, Alexa by Amazon, and Cortana by Microsoft. It includes disease mapping, prediction tools, image/facial recognition software, and marketing insights based on humans’ listen/watch/purchase histories. Computer theorists envision two more stages still unrealized: Strong or Deep AI, including emotions and beliefs, and ASI, or Artificial Super Intelligence, a new independent life form outperforming humans.

Machine Learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Digital Advertising uses technologies to deliver advertisements to consumers. This allows advertisers and marketers to reach more specific target audiences than traditional print ads, static billboards, or cable TV. Digital Marketing provides businesses with new strategies for interpreting data and making AI decisions based on insights that data provides.

Click: A user’s interaction with an ad, such as a mouse click on a laptop or tap of a finger on a mobile device. A Click Through Rate (CTR) is a metric used in digital marketing with a calculation of clicks divided by impressions, represented as a percentage.

Audience Targeting Data: a data set used for making ad and marketing decisions beyond age and gender. It may include the use of first-, second- and third-party audience data mined for buying and targeting.

Audience Buying is the process of directly buying audience segments based on data. Businesses can now target segments precisely and learn the most effective combinations of creativity and context. Data insights and machine learnings are then applied to grow that audience segment.

Advertising Auctions: Online advertising prices aren’t fixed, but are based on instant machine-run PPC (Price Per Click) Auctions. Advertisers compete for audiences and pay more for desired outcomes, thus favoring larger, richer patrons. Your attention is the product delivered to them.

Big Tech is a term that can refer to prosperous, influential, and otherwise powerful tech companies. “The Big Five” include Alphabet (Google), Apple, Meta, Amazon and Microsoft.

Articles and Videos

During a series of talks in 2020 presented as part of a symposium by the Northeastern Women’s, Gender, and Sexuality Studies Program, three academics and activists — Katherine Grainger, Catherine Knight Steele, and Carmen Rios — ”dove deeply into the way #MakingFeminisms can expand networks, educate communities on important issues, and shape our democracy both on and offline.” You can watch the recording here.

“It seems obvious that if the Internet is really reviving American democracy, as its celebrants claim, it’s taking a roundabout route,” Robert McChesney asserted in a 2013 In These Times piece on How Capitalism Conquered the Internet. “The hand of capital seems heavier and heavier on the steering wheel, taking us to places way off the democratic grid…” In OpenDemocracy: an exploration of how we can take it back.

John Hermann asked an obvious question in the NY Times: Have big tech companies become too powerful? “As these companies grew, they did more than just vanquish their competition,” he answered. “Their growth and free-service benevolence succeeded at making the very idea of competitors’ challenging their efforts — the industry’s traditional way to solve the problems they’ve created — seem unnecessary or even counterproductive. They’ve ducked the easy questions for so long that it’s reasonable to suspect that they doubt we will like the answers.”

Diversity in tech is slow-growing. Sara Wachter-Boettcher, web consultant and author of “Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech,” explained in the Washington Post that “Tech’s Sexism Doesn’t Stay in Silicon Valley. It’s in the Products You Use.” In her column for Ms. magazine, AEOO founder Rickey Gard Diamond in February wrote about “How Algorithms Enforce Women’s Silence — and How to Stop It.”

“Ensuring that this next iteration of the internet is inclusive and works for everyone will require that people from marginalized communities take the lead in shaping it,” Breigha Adeyemo wrote when the Metaverse opened. “It will also require regulation with teeth to keep Big Tech accountable to the public interest.”

Riane Eisler last year participated in a webinar for the Radical AI Measurementality podcast, related to making Artificial Intelligence Systems more “transparent, responsible and trustworthy.” She spoke about the caring economy in the context of prioritizing people’s mental health.

Lucina Di Meco and Kristina Wilfore explored why and how big tech must be accountable for online violence in a piece for Ms. magazine. In the New Yorker, Sheelah Kolhatkar wrote about The Fight to Hold Pornhub Accountable.

Books and Films

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