The hottest Data Bias Substack posts right now

And their main takeaways
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Top Business Topics

Maybe we should make ChatGPT less “WEIRD”

Sex and the State • 23 implied HN points • 19 Feb 26
🕹 Technology Data Bias
  1. Large language models learn mainly from online content produced by Western, educated, industrialized, rich, democratic (WEIRD) populations, so their outputs reflect those perspectives more than the global population.
  2. WEIRD modes of thinking — more individualistic, analytical, and universalist — differ from many non-WEIRD, more holistic and group-focused cultures, which makes models less accurate or relevant for those other groups.
  3. That WEIRD bias can shape real-world effects: by reinforcing individualistic and commercial norms, LLMs may worsen loneliness and reduce real-world socializing with heavy use and advertising, so we should consider making models less WEIRD and study these downstream impacts.

Training and Testing AI Systems

Rod’s Blog • 59 implied HN points • 28 Feb 24
🕹 Technology Data Bias
  1. Representative data is crucial for training AI systems to ensure they can handle various real-life scenarios and avoid biases.
  2. Challenges in collecting representative data include potential biases and incomplete datasets, which can impact the effectiveness of AI systems.
  3. Techniques like data augmentation can help address challenges in ensuring data representativeness by artificially diversifying and increasing the size of training datasets.

GPT-4 is "WEIRD"—what should we do about it?

The Counterfactual • 59 implied HN points • 12 Feb 24
🕹 Technology Data Bias
  1. Large Language Models (LLMs) like GPT-4 often reflect the views of people from Western, educated, industrialized, rich, and democratic (WEIRD) cultures. This means they may not accurately represent other cultures or perspectives.
  2. When using LLMs for research, it's important to consider who they are modeling. We should check if the data they were trained on includes a variety of cultures, not just a narrow subset.
  3. To improve LLMs and make them more representative, researchers should focus on creating models that include diverse languages and cultural contexts, and be clear about their limitations.

What I’ve Been Reading This Year

Cybernetic Forests • 139 implied HN points • 18 Dec 22
🎨 Art & Illustration Data Bias
  1. Reflection on the problems and implications of AI-based image generation in art
  2. Consideration of the origin and context of AI training data, highlighting issues like exploitation and biases
  3. Exploration of rethinking AI images as material for artistic expression, and the importance of artists reclaiming agency over these tools and the images they create

What things like stress, Childish behaviour and brain rotting mean to marketers

The Social Juice • 9 implied HN points • 02 Feb 24
💼 Business Data Bias
  1. Stressed consumers value experience over material possessions; focus on humanizing buying experiences.
  2. Consumers with low self-esteem may buy inferior products and need reassurance through marketing efforts such as email and social media.
  3. Creatives need boredom to resonate with consumers; give consumers time to explore passions and discover new products.
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