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  1. Last 7 days
  2. Jan 2026
    1. So theprisoners are kept in the dark as much as possible and do not learn theirrisk scores

      This is sad, no transparency in the data and the use of it

    2. When we ask Google Mapsfor directions, it models the world as a series of roads, tunnels, andbridges. It ignores the buildings, because they aren’t relevant to the task.When avionics software guides an airplane, it models the wind, the speedof the plane, and the landing strip below, but not the streets, tunnels,buildings, and people.

      Great analogy, wondering where it's going

    3. A model, after all, is nothing more than an abstractrepresentation of some process

      Definition of a model- they tell us what to expect and they guide decisions

    1. 17.7% of patients that the algorithm assigned to receive extra care were black. The researchers calculate that the proportion would have been 46.5% if t

      huge difference, almost 3x as many should have been referred

    2. verage black person was also substantially sicker than the average white person

      Again, interesting on how they were able to pull this data to determine the average black person was sicker

    3. urces and closer medical super-vision for people with mu

      Interesting initial study as well. Clearly they were looking for other trends in the data and keeping an open mind to discover this trend too

    4. less likely to refer black people than white people who were equally sick to programmes that aim to improve care for patients with complex medical needs

      Interested in learning how this was determined and studied

    1. "Feminist analysis these power differentials so that they can change them." really addressing the power and oppression that comes with it for all oppressed/marginalized groups. Privilege and oppression are intersectional (Crenshaw). Definition of oppression-the systematic mistreatment of certain groups of people by other groups. "the work of data feminism is first to tune into how standard practices in data science serve to reinforce these existing inequalities and second to use data science to challenge and change the distribution of power." co-liberation-oppressive systems of power harm all of us. Data feminism- must answer these questions: what info needs to because data before it can be trusted? whose info needs to become data before it can be considered as fact and acted upon? The book by Cathy O'Neil sounds interesting. Love this quote "It takes more than one gender to have gender inequality and more than one gender to work toward justice." data feminism is about power-about who has it and who doesn't. Review the seven principles. Book provides concrete steps

    2. we employ the term feminism as a shorthand for the diverse and wide-ranging projects that name and challenge sexism and other forces of oppression, as well as those which seek to create more just, equitable, and livable futures.

      Definition used for feminism

    3. wealth inequalities and the role that well-educated, well-off people play in maintaining those..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; } Or to believe in the logic of co-liberation. Or to advocate for justice through equity. .d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1nyah beanIndeed, a central aim of this book is to describe a form of intersectional feminism that takes the inequities of the present moment as its starting point

      almost like their declaration of biases or disclaimer.

    4. ngineering positions, where they could be promoted through the ranks of the civil service, while women with the same degrees were sent to the computing pools, where they languished until they retired or quit.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }211..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Joe Masnyy

      Men-engineering past more opportunities. Women-human computers. Glass ceiling

    5. Not only was her contribution vital to the success of the Apollo II mission, her very presence was challenging the sexism and racism within the US.