Activism Future Building

Police Scorecard

Bookmarked Police Scorecard (Police Scorecard)

The Police Scorecard evaluates police departments based on quantitative data on arrests, use of force, accountability and other policing issues to make progress towards more just and equitable policing outcomes.

Disappointed to see how poorly my city’s police department scores on data. In 2018 (?) a blatantly racist interaction with police got a lot of publicity here (fortunately no one was harmed, and the racism was more on the people who called the police but the police could have declined to respond). After that, the PD made a lot of nice words and gestures, including investigating body cams. But the overall rating is on par with Seattle’s, which is notoriously bad — I’ll never forget the Native American woodcarver the Seattle police murdered for having a knife in 2010 — and the per capita funding is higher than most communities (🤔 though do they adjust for cost of living differences? I wouldn’t expect an officer’s salary in WA to be the same as in KS for example).

The “incident” certainly raised the public’s awareness and interest in dealing with racism (at least on a superficial level — still plenty of NIMBYism over housing), and spurred changes in the administrative side of the government, so something positive did come out of it even if the police didn’t actually change much. The entire city government also received training on racism and bias. I think the police got more than City Hall folks like me, though I wasn’t super impressed by the half-day training I got, which focused on the metaphor that the US isn’t a melting pot (homogeneous) but a stew (lots of ingredients). That feels like barely enough to even start talking about the issue.

See also: An interactive advocacy website about police

Culture History Political Commentary

Reason 5379 we need better statistics education

Replied to Gun Violence Is Actually Worse in Red States. It’s Not Even Close. (POLITICO)

America’s regions are poles apart when it comes to gun deaths and the cultural and ideological forces that drive them.

Using raw numbers / absolute values instead of per capita data is so misleading. I know that data don’t actually change minds, but some skepticism around statistics couldn’t hurt 🤷‍♀️

Culture Featured Technology Writing

Mining intellectual value

Liked Television writer on fight with studios, networks: “We’re looking at the extinction of writing as a profession” (World Socialist Web Site)

The executives, the management and their attorneys have taken the Writers Guild minimum basic agreement [MBA] and they’ve gone through it with a fine-tooth comb looking for every conceivable loophole, and exploiting those to the hilt. Basically, these companies would like to view us as Uber drivers.

But when you go into this mini-room and commit to this time, you don’t have any guarantee that if the show does go ahead, you’re going to be on the show, because those kinds of commitments are part of the “old model.”

The companies are saying: we’re not going to do that anymore; we’re not committing to you. We’re not promising you anything. We’re just saying, come in, we’ll pay you like piece workers, give us your best ideas and then get the hell out.

This is all part of the same business perspective that rejects artistry, rejects art, rejects the value of teamwork, rejects originality. This is the mindset that cancels once-flagship shows before their final season because the profit margin’s not high enough (Westworld) and writes off completed movies for tax reasons (Batgirl). There is no respect for the human creators who contributed to the show; they got their money, isn’t that enough? As Doctorow calls it, this is the enshittification of the entertainment industry, and in this case of culture itself, all for short-term shareholder value.

Future Building Technology The Internet

Read On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜

Read On the Dangers of Stochastic Parrots | Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.

LLMs reinforce existing structures and values, and overrepresent certain (privileged) viewpoints.

The net result is that a limited set of subpopulations can continue to easily add data, sharing their thoughts and developing platforms that are inclusive of their worldviews; this systemic pattern in turn worsens diversity and inclusion within Internet-based communication, creating a feedback loop that lessens the impact of data from underrepresented populations.

Even if populations who feel unwelcome in mainstream sites set up different fora for communication, these may be less likely to be included in training data for language models.

The Colossal Clean Crawled Corpus, used to train a trillion parameter LM in, is cleaned, inter alia, by discarding any page containing one of a list of about 400 “Dirty, Naughty, Obscene or Otherwise Bad Words”. This list is overwhelmingly words related to sex, with a handful of racial slurs and words related to white supremacy (e.g. swastika, white power) included. While possibly effective at removing documents containing pornography (and the associated problematic stereotypes encoded in the language of such sites) and certain kinds of hate speech, this approach will also undoubtedly attenuate, by suppressing such words as twink, the influence of online spaces built by and for LGBTQ people. If we filter out the discourse of marginalized populations, we fail to provide training data that reclaims slurs and otherwise describes marginalized identities in a positive light. Thus at each step, from initial participation in Internet fora, to continued presence there, to the collection and finally the filtering of training data, current practice privileges the hegemonic viewpoint. In accepting large amounts of web text as ‘representative’ of ‘all’ of humanity we risk perpetuating dominant viewpoints, increasing power imbalances, and further reifying inequality.

Featured Learning Technology The Internet

Internet era life skills

I recently encountered somewhat shocking — though not necessarily surprising — data about the average person’s computer skills. The vast majority of people are not able to complete complex tasks on a computer. Only five percent of Americans had high level computer skills that allowed them to do things like troubleshoot or analyze data using multiple tools.

These data are from 2011-2015, so the numbers have certainly changed. I would definitely guess there are fewer people who are unable to use a computer at all. But, I was discussing with a friend that we doubted there’s been a substantial increase in the number of people able to complete complicated, multi-step, multi-program tasks. Over the past ten years, technology and user interfaces have trended towards simplification and single-task software (there’s an app for that!). Reducing friction for common tasks removes challenges people might have needed to troubleshoot in the past — and if you don’t ever face problems accomplishing what you need to, you never get to practice or even develop troubleshooting skills.

And basic computer literacy isn’t enough to get by in the internet age. Someone learning how to use the internet today needs to also learn a broad range of skills to protect themselves, communicate effectively, and obtain trustworthy information. Too many people are credulous and uncritical in what they believe. There are so many dark design patterns (or are we not calling it that anymore?) and bad actors attempting to manipulate you that it requires a bulwark of skills to defend against having your time and money stolen, or even worse, indoctrination.

Many of these skills are personal responses to systemic problems that some regulation might assist with. Not that regulation is easy: GDPR wound up giving us all obnoxious popup cookie banners instead of reducing the cookies websites use or data corporations collect — but at least some websites do now allow you to reject non-essential cookies.


Article pairing: wealth disparity

WHY THE SUPER RICH ARE INEVITABLE by Alvin Chang | January 2023

Why do super rich people exist in a society?


Many of us assume it’s because some people make better financial decisions. But what if this isn’t true? What if the economy – our economy – is designed to create a few super rich people?


That’s what mathematicians argue in something called the Yard-sale model…


Who Benefits from Income and Wealth Growth in the United States? by Blanchet et al

Realtime Inequality provides the first timely statistics on how economic growth is distributed across groups. When new growth numbers come out each quarter, we show how each income and wealth group benefits.


Controlling for price inflation, average national income per adult in the United States decreased at an annualized rate of -2% in the third quarter of 2022, and average income for the bottom 50% shrunk by -2.4%.

Meta Personal Growth Resources and Reference

Template spreadsheet for tracking reading diversity

Bookmarked Introducing the 2023 Reading Log! (

The 2023 reading log is here! It can help you track your reading stats and generate infographics to help you achieve your reading goals.

👀 This could be handy for tracking my reading.

Environment Resources and Reference

Carbon emissions by sector

Bookmarked Emissions by sector (Our World in Data)

How much of CO2 emissions come from electricity, transport, or land use? What activities do our greenhouse gases comes from?

Art and Design Resources and Reference

Color palettes for data visualization

Bookmarked Data Color Picker (

Creating visually equidistant palettes is basically impossible to do by hand, yet hugely important for data visualizations. Why? When colors are not visually equidistant, it’s harder to (a) tell them apart in the chart, and (b) compare the chart to the key.


Valuing outliers


Investigate outliers to see if they tell a different story from the average.