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https://github.com/apparebit/penal-colony

Letters from the Stochastic Penal Colony šŸ
https://github.com/apparebit/penal-colony

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Letters from the Stochastic Penal Colony šŸ

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# Letters from the Stochastic Penal Colony šŸ



[Paper](https://github.com/apparebit/penal-colony/raw/boss/penal-colony-v1.1.pdf)

(v1.1, PDF) by Robert Grimm, Independent Investigator, Brooklyn, NY, USA.



## Abstract



This paper serves as pointed critique of algorithmic practice outside the

criminal injustice system. Far too many interventions including social media's

content moderation are excessively punitive, often resulting in the figurative

death of users through permanent account suspension. First, based on my own

experiences and grounded in procedural justice, this paper starts by exploring

the many ways policy and automated enforcement turn punitive on the example of

OpenAI's DALLā€¢E 2. Second, it illustrates how even best-practices policy turns

punitive performance on the example of pre-Musk Twitter. Third, a comprehensive

survey of non-Chinese social media demonstrates the pervasiveness of excessively

punitive content moderation. It also tests the limits of their accountability,

notably by projecting the likely impact of the European Union's Digital Services

Act and by correlating data released by Facebook, Google, and the National

Center for Missing and Exploited Children. Fourth, to illustrate the limits of

algorithmic content moderation, this paper presents a successful strategy for

subverting DALLā€¢E's aggressive automated censor, which inadvertently also

unleashed grotesquely racist imagery. Fifth, this paper proposes a new

intellectual property regime specifically for AI. It re-combines proven

elements from copyright and patent law, resulting in a framework that balances

the interests of those who invest in state-of-the-art AI and everyone else.

Finally, this paper concludes by pointing towards harm reduction as a mindset

for, possibly maybe, making life in this digital penal colony at least somewhat

bearableā€”because, I fear, we are stuck in it.



## Findings



Highlights of the paperā€™s findings include:



  * Content moderation by all surveyed social media is punitive and excessively

    so. Social media are on the best way to creating a new underclass of people

    without a voice on these platforms.

  * Content moderation by all surveyed social media runs against the public

    interest. Particularly prohibitions against misinformation are extremely

    chilling given pervasive failures by medical experts during the pandemic.

  * Transparency reports by all surveyed social media besides Reddit and YouTube

    suffer from significant data quality issues.

  * Transparency disclosures by Meta are so ridden by data quality issues to be

    wholly untrustworthy and meaningless. Unfortunately, that is the case for

    Metaā€™s data disclosures to researchers and customers as well.

  * As demonstrated on OpenAIā€™s DALLā€¢E 2, algorithmic censors based on large

    language models are vulnerable to a new kind of attack strategy that is hard

    if not impossible to mitigate.

  * As demonstrated on ChatGPT, large language models can significantly simplify

    and shorten the experiments necessary for that attack, raising significant

    doubts about the efficacy of AI-based content moderation.

  * OpenAIā€™s DALLā€¢E 2 produces deeply racist images without being prompted to do

    so, most likely due to a naive diversity mitigation.



The paper explores regulatory responses to this sorry state of content

moderation and transparency reporting but rejects them as too punitive. Instead

it points towards more subversive, harm-reducing approaches to dismantling the

stochastic penal colony. It also proposes a new intellectual property regime for

AI that remixes existing, proven copyright and patent provisions to ensure that

all of society benefits from this amazing new technology.



## Source Code and Supplements



Source code and supplements for the paper ā€œLetters from the Stochastic Penal

Colony šŸā€ by Robert Grimm.



  * A [__custom build script__](build.sh) in the repository root takes care of

    repetitive tasks. The one optional argument is the name of the task to

    execute.

  * By default, i.e., when invoked without argument, the build script runs

    `pdflatex` and `bibtex` to __create the PDF document__ from the LaTeX files

    in the [source](source) directory.

  * Since LaTeX and BibTex are incredibly noisy in their output, the build

    script contains custom logic to __detect actionable warnings__ and then

    error out.

  * To work with the ACM's new (but arguably not improved) publishing flow, the

    paper uses only approved LaTeX packages and __compiles with `pdflatex`__. To

    produce my own copies, it also compiles with `lualatex` when the build

    script is given the `lua` argument.

  * Unfortunately, that leaves only one subpar option for __color emoji__,

    namely simulating them by including graphics files. I wrote my own LaTeX

    package, emo, to take care of that and then some. Emo is included with the

    paper sources, but may be outdated. Check out [its

    repository](https://github.com/apparebit/emo) or

    [CTAN](https://ctan.org/pkg/emo).

  * Transparency data and Jupyter notebooks with the code for __analyzing the

    data__ are inside the [supplements](supplements) directory.

  * The build script assumes that the __virtual environment__ with Python

    packages necessary for running the notebooks is contained in the `.venv`

    directory. When invoked with the `venv` argument, it checks whether that

    directory exists, creating the virtual environment and installing packages

    otherwise, and then activates the virtual environment.



## (C) Copyright 2023 by Robert Grimm



The shell script and Jupyter notebooks included in this repository have been

released as open source under the Apache 2.0 license. Otherwise, all rights are

reserved, including for the paper itself.