On work-life balance; writing simply; ethical issues with recommender systems; and trust and safety in products.

It’s no surprise that AI can be harmful, but discussions of AI harm often miss historical context. This paper explores the role of decolonial theories in AI to address this gap. Through ethical foresight, the authors argue, we can rethink the role of AI in helping and harming people of different societies.

Self-reported measures of time spent online are known to be inaccurate compared to objective, server-log-informed measures. And while they’re each individually useful in predicting problematic effects online, little is known about how these two measures can be used alongside each other. This CSCW paper uses the open-source, open-data platform Lichess to study this.

On rules engines and machine learning, graph theory for COVID vaccinations, MLflow and PyTorch, and grad school.

Another CSCW paper for this week’s reading group; this one on digital archives. What does data preservation mean in a digital world? How do adults see their legacy? This paper draws on archival theory to study these questions.

When algorithms make decisions, humans should be able to appeal them. Right? This CSCW paper studies users’ experience with contesting algorithmic content moderation decisions.

The “hardware lottery” is when a research idea succeeds because it is suitable with available hardware. What does this mean for the future of research?

“The world is awash with bullshit,” Carl Bergstrom and Jevin West write. What is it? How do we recognize it? How do we call it out when we see it? Calling Bullshit: The Art of Skepticism in a Data-Driven World, informs us on these questions.