Our Spreadsheet Overlords

Weatherby argues that the current discourse around AI, especially the buzz around “artificial general intelligence,” is a distraction from its actual impact: the expansion of bureaucracy through massive data systems…

Experts Argue Whether Computers Could Reason, and if They Should (Published 1977)

Computer world is in midst of fundamental dispute over question of computer intelligence since MIT Prof Joseph Weizenbaum wrote book arguing that machines can never be made to reason like people and should not be; Weizenbaum por (M)

Pluralistic: Big Tech’s “attention rents” (03 Nov 2023) – Pluralistic: Daily links from Cory Doctorow

The thing is, any feed or search result is "algorithmic." "Just show me the things posted by people I follow in reverse-chronological order" is an algorithm. "Just show me products that have this SKU" is an algorithm. "Alphabetical sort" is an algorithm. "Random sort" is an algorithm.

We're not taking the fact-checking powers of AI seriously enough. It's past time to start.

Some notes on the Nobel Prize hallucination that wasn't

Why opinion on AI is so divided

AI power users are pulling away from everyone else.

Don't Write Like AI (1 of 101): "It's Not X, it's Y"

The #1 AI writing tell and a daily annoyance of millions.

AI Has Ruined the Job Market

Maybe flawed people were better than brute algorithms.

Knowledge Collapse

AI companies are racing to mechanize mathematics. Where does that leave human understanding?

Silicon Valley Doesn't Get Why You Hate AI

Technology leaders don’t seem to understand society’s gripes about AI, but boy, are they posting through it.

Algorithmic Bias · Open Encyclopedia of Cognitive Science

Algorithmic bias refers to prejudicial, discriminatory, unjust, inaccurate, or otherwise disparate performance or outcomes from algorithmic systems based on racial, gender, or other attributes of an individual or a group. The concept of algorithmic bias emerged at the intersection of computer science, artificial intelligence (AI) research, critical data studies, human–computer interaction, law, philosophy, and similar disciplines. Although problems and discrepancies at the model level denote the most commonly studied form of bias, the term algorithmic bias is also used as a shorthand to describe a multitude of problems and challenges at various steps of the AI pipeline from ideation, problem framing, training data curation and processing, model training and validation, and deployment as well as emergent issues that arise from interaction with the real world. Potential sources of bias, appropriate metrics to define, measure, and mitigate bias, and the utility and merit of technical approaches to bias mitigation are fiercely debated in the current AI landscape.

AI, peer review and the human activity of science

When researchers cede their scientific judgement to machines, we lose something important.

Trump and Musk's history obsession

The frenemies' strange obsession with historical "accuracy" has disturbing connections to big AI

Why embracing complexity is the real challenge in software today

In the midst of industry discussions about productivity and automation, it’s all too easy to overlook the importance of properly reckoning with complexity.

Algorithm appreciation: People prefer algorithmic to human judgment

Even though computational algorithms often outperform human judgment, received wisdom suggests that people may be skeptical of relying on them (Dawes, 1979). Counter to this notion, results from six experiments show that lay people adhere more to advice when they think it comes from an algorithm than from a person. People showed this effect, what we call algorithm appreciation, when making numeric estimates about a visual stimulus (Experiment 1A) and forecasts about the popularity of songs and romantic attraction (Experiments 1B and 1C). Yet, researchers predicted the opposite result (Experiment 1D). Algorithm appreciation persisted when advice appeared jointly or separately (Experiment 2). However, algorithm appreciation waned when: people chose between an algorithm’s estimate and their own (versus an external advisor’s; Experiment 3) and they had expertise in forecasting (Experiment 4). Paradoxically, experienced professionals, who make forecasts on a regular basis, relied less on algorithmic advice than lay people did, which hurt their accuracy. These results shed light on the important question of when people rely on algorithmic advice over advice from people and have implications for the use of “big data” and algorithmic advice it generates.