Per Axbom Twitter

My kid sister is today becoming a Doctor of Technology, focusing on the field of feminst technoscience and ethics and her presentation is fabulous and did I mention she is my sister 😭😍

#designethics

"I would suggest that care is deeply embedded in technology development, and how we choose to care, or not to care, when we develop apps, games and technological platforms."

Can I also mention how she is being totally badass about criticising traditional embedded research, acknowledging how the researcher is really part of the research and influencing the environment. The researcher is a co-creator in this sense, just as the designer is a co-creator.

"When I revisit my previous, older papers I can see them in a different light - notice new things - after having done further research in orher areas."

🔗 Originally posted on Twitter

Per Axbom reply Twitter

↪ Replying to @unknown

@Eduardo_Rich @DougCollinsUX @Carilall @ux_adam @krayker @ojmqd @whatusersdo @AngelList @ixdconf @gillesdemarty Thanks for the shoutout Eddie! Very timely. I today embarked on teaching a two-week course in ethics for UX students at a trade school here in Sweden. This will be an experience to blog about and share. I’d likely want to include an interview with some students in that.

🔗 Originally posted on Twitter

Per Axbom reply Twitter

↪ Replying to @RealSaavedra

@RealSaavedra Here we go, one of many examples:

propublica.org/article/machin…

@AOC is 100% correct and there are many organizations across the world working on ways to mitigate bias in machine learning.

@RealSaavedra @AOC Algorithms are driven by computation, not by math.

joanna-bryson.blogspot.com/2017/07/three-…

@RealSaavedra @AOC A video based on @mathbabedotorg's talk may help out here: vimeo.com/thersa/thetrut…

She wrote a book aptly named Weapons of Math Destruction.

@RealSaavedra Hmmm, it seems even Amazon can't get it right... reuters.com/article/us-ama…

@RealSaavedra Here's one where Google apologized: theguardian.com/technology/201…

@RealSaavedra Google says: "Machine learning models are not inherently objective. Engineers train models by feeding them data sets of training examples, and human involvement in the provision and curation of this data can make a model's predictions susceptible to bias." developers.google.com/machine-learni…

🔗 Originally posted on Twitter