'"Algorithms are meaningless without good data. The public can exploit that to demand change," argues a new article in MIT's Technology Review (shared by long-time Slashdot reader mspohr): Data is fed into machine-learning algorithms to target you with ads and recommendations. Google cashes your data in for over $120 billion a year of ad revenue. Increasingly, we can no longer opt out of this arrangement... Now researchers at Northwestern University are suggesting new ways to redress this power imbalance by treating our collective data as a bargaining chip... In a new paper being presented at the Association for Computing Machinery's Fairness, Accountability, and Transparency conference next week, researchers including PhD students Nicholas Vincent and Hanlin Li propose three ways the public can exploit this to their advantage: Data strikes, inspired by the idea of labor strikes, which involve withholding or deleting your data so a tech firm cannot use it — leaving a platform or installing privacy tools, for instance. Data poisoning, which involves contributing meaningless or harmful data. AdNauseam, for example, is a browser extension that clicks on every single ad served to you, thus confusing Google's ad-targeting algorithms. Conscious data contribution, which involves giving meaningful data to the competitor of a platform you want to protest, such as by uploading your Facebook photos to Tumblr instead. Will we someday see "white-hat data poisoners" trying to convince tech companies that the best place to advertise is the classified sections of small local newspapers? While the researchers believe sporadic individual actions have little impact, the article takes this to its ultimate conclusion. "What if millions of people were to coordinate to poison a tech giant's data well...? That might just give them some leverage to assert their demands."' -- source: https://yro.slashdot.org/story/21/03/06/0149242 Cheers, Peter -- Peter Reutemann Dept. of Computer Science University of Waikato, NZ +64 (7) 577-5304 http://www.cms.waikato.ac.nz/~fracpete/ http://www.data-mining.co.nz/