PyData NYC 2022

Algorithms as Policy Workshop: Exploring web3 from a data scientist's perspective

Wednesday, November 9, 2:45-4:15, New Amsterdam board room (5th floor)

Open to all attendees, prior registration not required

This workshop will be broken up into three parts:
1. Algorithms as Policy: A Primer
2. Small Group Discussions and/or Hack Sessions
3. Regroup to share our findings and discuss next steps.

Part 1: Algorithms as Policy: A Primer

We will briefly explore the role of subjective decision making in the design and implementation of algorithms used as part of public platforms. We will briefly consider how designers are and are not held accountable for the systemic impacts of their algorithms, as well as the very really challenges in identifying to what extent designers can or should be capable of identifying harms before (or after) an algorithm is in production. The presentation component will be kept relatively short in order to allow an extended AMA.

Part 2: Small Group Discussions and/or Hack Sessions

Participants will break up into groups 2-5 to work have deeper discussions and/or explore public data sets to further identify similarities and differences between algorithmic policy making practices, and oversight thereof in web2 and web3. Hint, its not going particular well in either but we can still learn something by comparing and contrasting our experiences.

Part 3: Regroup to share our findings and discuss next steps.

Each break out group will be invited to share a summer of what came up in their discussions. Then we will close by identifying what, if anything, the workshop participants would like to do to continue this conversation in the future.

1. Blog on Algorithms as Policy by Michael along with a PhD student writing a dissertation on data/cpmputing infrastructure from an infrastructure studies perspective:
2. Blog on Engineering Ethics in web3: Blog Michael wrote comparing civil engineering ethics to algorithm design in web3
3. A video from “Funding the Commons” on Algorithms as Policy

Prior Knowledge Expected

No previous knowledge expected