Digital Ethics

One Michigan community has been experiencing the reality of a data ...

Many white-collar professionals worry about the tech inhibiting creativity and making errors

AI companies play a cat-and-mouse game, trying to boost the capabilities of their creations while scrambling to block answers to dangerous queries

Who is ed-tech for? The marketing copy -- whether on websites or in PowerPoint or in TED Talks and the like -- will all insist that ed-tech is, at the end of the day, for "the students." Even the products that are pitched to make teachers' and principals' and professors'

Age-verification systems are increasingly sophisticated. They're lucrative for the businesses creating them, too.

After the earthquakes, developers and citizens used AI to build websites and apps to help locate missing persons and coordinate relief efforts amid a slow government response.

Protein structure prediction is a hard problem, but even harder ones remain

The reality of how much damage AI mania is doing to our ability to run institutions effectively.

A shockingly inefficient trillion-dollar project

Is it ethical to use hyper-scaled GenAI?

Being anti-AI feels like being anti-war

Ontario jails are using a program that claims it can predict prisoners’ behaviour, disproportionately putting Black prisoners in higher-security facilities

The government’s favourite provider of child protection software, SafeToNet – a perpetually loss-making firm run by recently born-again Christians – has finally filed its long-delayed accounts. The…

For the richest men on Earth, everything is free and nothing matters.

Age verification (or age-gating) laws generally require online services to check, estimate, or verify all users’ ages—often through invasive tools like ID checks, biometric scans, or other dubious

The SFPD’s exposure of hours of videos from drone platform Skydio reveals how broadly it’s watching the city from above—and how the results can spill online.

So this is hell. I'd never have believed it. You remember all we were told about the torture-chambers, the fire and brimstone, the 'burning marl.' Old wives' tales! There's no need for red-hot pokers. HELL IS – OTHER PEOPLE! –Jean Paul Sartre, No Exit Sartre was (arguably) an optimist. It's not that other people are unpleasant: quite the contrary! There's nothing that makes the day (or, pointedly, the night) …Read More

Trying to understand how LLMs represent meaning by way of a discussion of Cleanth Brooks's account of poetic paradox.

Big Tech has looted humanity’s creative output to create artificial intelligence. We don’t just deserve a share of AI’s profits — we deserve control over it.

In 2022, Google sued to keep its water use in The Dalles, Oregon secret. When the numbers finally came out, the company's data centers were drinking 29 percent of the town's water.

Tech Won't Save Us · Episode

Video lecture for Rurality, Livelihoods, and Gender at the Swedish ...

Critically analysing the Turing test.

Demand for AI is expanding, but sustainability solutions are “not scaling fast enough,” Microsoft reported.

As demand for cloud computing and artificial intelligence grows, data centers are expanding across the country, and in order to keep these centers running, they rely on massive amounts of electricity ...

Discover which data center cooling method and technology delivers the best balance of cost, efficiency, and sustainability for your facility.

This paper examines the paradoxical decline in engagement with philosophy of science among engineers precisely when machine learning (ML) systems are increasingly performing complex epistemological functions in engineering practice. We identify how philosophical naivety, characterized by the uncritical adoption of reductive frameworks regarding consciousness, intelligence, and ethics, creates tangible organizational and technical liabilities. We then demonstrate how conceptual limitations in engineers’ philosophical foundations lead to three primary flaws: 1) ontological misclassification of system capabilities, 2) ethical blind spots in ML system design and application, and 3) inadequate epistemological approaches and hidden philosophical commitments for interpreting model outputs. Thus, we argue that renewed engagement with the philosophy of science is not merely academic but necessary for engineers to maintain epistemic authority and responsibility in an era where engineering judgment is increasingly delegated to or mediated by ML systems. In response, we propose a technical-philosophical framework integrating perspectives from philosophy of mind, ethics, epistemology, and engineering to address these shortcomings systematically.