Unraveling TechnoSolutionism: How I Fell Out Of Love With “Ethical” Machine Learning

Unraveling TechnoSolutionism: How I Fell Out Of Love With “Ethical” Machine Learning

At the recent QCon in San Francisco, Kathryn Jarmul, privacy activist and chief data scientist at Thoughtworks, gave a talk on demystifying technological determinism, where she explored the bias inherent in AI training datasets, the tendency to believe it exists. technical solution.almost every problem and these technical solutions will benefit humanity. He discussed ways to define technical solutions and asked technologists questions to consider when creating products.

He began by discussing how training datasets used in AI systems are biased based on the labels assigned to them by human markers. Many taggers are among the lowest paid in the tech industry. To illustrate this point, he showed a photo of a man and woman talking, labeled as a factory worker being scolded by his boss during a loud conference call and a hot blonde being rudely cut off by her boss...image doesn't match. reality, but the tag is included in the database for developing artificial intelligence systems.

He defined technological solution as the naïve belief that any problem can be solved by applying a magic box of technology and that the application of technology will change society for the better. Techno-solution takes technological progress for granted. While researching the elixir of life, he took a cue from the first written formula for gunpowder found in 9th century China. Is technology good, neutral or bad?

The reality is that almost all technological advances have their advantages and disadvantages. Often, benefits and harms are unevenly distributed: one group may receive most or all of the benefits, while others receive all or most of the harms.

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Noting that the computer industry is dominated by technological solutions, he traced the idea back to the ancient mythology of Silicon Valley and even the California mentality of early settlers who believed we could overcome problems. , upgrade yourself and change the terrain . It started as a belief in Silicon Valley that a good idea can change the world and make you rich .

He quoted Joseph Weizenbaum, who is credited with creating the first artificial intelligence system, saying that computer technology was like this from the beginning:

a fundamentally conservative force that reinforces existing hierarchies and power dynamics that otherwise need to change.

This conservatism meant that social change was difficult and that the benefits of technological progress accrued to a disproportionately small portion of humanity.

He gave some advice on how to recognize techno-decisionism in action. If you make any of these statements, think carefully about the impact of what you're working on:

  • I'm optimizing a metric that someone invented
  • Everyone agrees that everything will be fine
  • It would solve everything if we _______
  • Mythology says: revolution, change, progress
  • People who raise potential issues are excluded
  • I have not tested a non-technical solution to the problem

He then offered five specific lessons for technologists to keep in mind when developing products:

1) Contextualize the technology

Ask yourself what existed before this technology, what would have happened if it had never been discovered, and what we would do without this technology.

2) Look for impact, not just technology

Explore the potential impact of technology in the short, medium and long term. Look broadly to determine who and what may be affected and learn the consequences.

3) Make room and learn from those who know

Identify and listen to affected people, communities and groups. Remember to broadcast their voices, and if you are in a privileged position, use that privilege to hear other voices.

4) Confirm and explain the system change

Use your language wisely and carefully. He used the example of "revolutionary" e-commerce to describe small changes in how people interact online. Exaggeration and hyperbole are often used to obscure the impact of change on disadvantaged communities.

5) Fight for justice, not just architecture

He talked about researchers who were fired from Google for exposing bias in their algorithms. Give your voice to the silenced.

He then talked about his decision to focus on data privacy as an area where he is committed to change and where he can make a difference.

It ended with a series of questions for the audience to ponder:

  • What would you be doing if you hadn't built who you are now?
  • If you focus on change rather than technology, what can you change?
  • What if we collectively took responsibility not for the future of technology, but for the future of the world?

[Referring to fact sheet] Part One: What is the public interest?

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