Rezultati pretraživanja
  1. 31. srp 2019.

    just launched! This is an ongoing effort from to formalize documentation practice for transparency in machine learning. We want EVERYONE - from policy to engineering - to comment on what stakeholders should be documenting throughout the ML dev lifecycle.

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  2. 1. kol 2019.

    Excited to serve on the steering committee of the 's new project which is tackling one of the hardest organizational/logistical challenges in responsible AI: how to encourage and support internal and external transparency.

  3. 28. sij

    If you’re , come to our interactive happy hour 1/29 at 7pm in the plenary room to learn about FAT* research from , tell us what documentation questions are missing from the question bank & meet our staff. All are welcome!

  4. 27. sij

    Triumphant post tutorial selfie with - thanks to everyone who packed the room for ! So excited to see this work resonate with so many people & pumped for what comes next (starting a how to guide for !) Stay tuned!

  5. I honestly respect more “The I tried this and it didn’t work out as planned” way over “The I want to or I’ve been thinking of doing”

  6. 19. srp 2019.

    I'm in SF until the end of summer, helping with and other things. This group is in such a good position to make tangible change across a range of diverse stakeholders. Grateful for the opp. to push their work towards actualizing corporate accountability!

  7. 31. srp 2019.

    Proud to be a Steering Committee member of - the 's ongoing work to establish best practices for transparency in . Join me in submitting comments on the "version 0" draft to include the diversity of your voices:

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  8. 31. srp 2019.

    The is conducting groundbreaking work convening industry, civil society, and the research community to create new documentation standards for transparency in machine learning. Check out here, and contribute yourself:

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  9. 7. pro 2018.
  10. 1. kol 2019.

    Extremely pleased to announce that our Chief Scientist has joined the steering committee of the ABOUT ML initiative with the Partnership on AI group, furthering our commitment to robust and transparent AI.

  11. 23. sij

    I’ve been lucky enough to work with new fellow , who has been leading this awesome research that we’ll be sharing at a tutorial during . We will then translate these insights a how to guide for org change in AI, starting with

  12. 1. kol 2019.

    There's definitely need for greater transparency

  13. 13. sij

    One way to close the gap between principles and practice in is through documentation for machine learning systems at scale. Read about why supports our ongoing initiative, ABOUT ML, to build this bridge:

  14. 28. sij

    On Wednesday, PAI will host an evening satellite event to bring together members of the FAT* community to learn about PAI's research and contribute to the project:

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  15. 1. kol 2019.

    Important initiative that we are proud to be connected to.

  16. 31. srp 2019.

    *SO* proud to be supporting work at to establish documentation best practices for transparency in . We are seeking to include diverse voices across industry, civil society, academia and policy in submitting comments on the "version 0"

  17. 7. kol 2019.
  18. 10. sij

    In , standardizing reporting requirements is a challenge for the industry. Great to see that efforts like project by integrate lessons from social sciences and humanities to translate key concepts like fairness or transparency into practical guidelines

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  19. 29. sij

    Great turnout for our happy hour - I’m amazed and humbled at how many people turned up after a full conference day to hear from on our FTA research agenda & contribute to adapting questions for ML to different contexts

  20. 24. sij

    Proud of this upcoming tutorial presented by & at . We look forward to the insights generated during this session to advance our ongoing initiative.

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