The hottest Data Ethics Substack posts right now

And their main takeaways
Category
Top Technology Topics

On OpenAI's Model Spec 2.0

Don't Worry About the Vase • 985 implied HN points • 21 Feb 25
🕹 Technology AI Machine Learning Software Development Data Ethics Tech Policy
  1. OpenAI's Model Spec 2.0 introduces a structured command chain that prioritizes platform rules over individual developer and user instructions. This hierarchy helps ensure safety and performance in AI interactions.
  2. The updated rules emphasize the importance of preventing harm while still aiming to assist users in achieving their goals. This means the AI should avoid generating illegal or harmful content.
  3. There are notable improvements in clarity and detail compared to previous versions, like defining what content is prohibited and reinforcing user privacy. However, concerns remain about potential misuse of the system by those with access to higher-level rules.

Hard-forked! Casey Newton’s distorted portrait of Gary Marcus and AI skepticism

Marcus on AI • 6679 implied HN points • 06 Dec 24
🕹 Technology AI Machine Learning Data Ethics Tech Policy Regulation
  1. We need to prepare for AI to become more dangerous than it is now. Even if some experts think its progress might slow, it's important to have safety measures in place just in case.
  2. AI doesn't always perform as promised and can be unreliable or harmful. It's already causing issues like misinformation and bias, which means we should be cautious about its use.
  3. AI skepticism is a valid and important perspective. It's fair for people to question the role of AI in society and to discuss how it can be better managed.

Algorithmic ranking is unfairly maligned

DYNOMIGHT INTERNET NEWSLETTER • 1156 implied HN points • 23 Jan 25
🕹 Technology Algorithms Social media Digital Culture User Experience Data Ethics
  1. Not all algorithmic ranking is bad. Some algorithms can be useful if they align with what you want to see and achieve.
  2. A lot of current algorithms are designed to keep you engaged and make money for the companies, not necessarily to help you find what you like.
  3. We need better control over these algorithms to ensure they serve our interests, possibly through new technology or structures that prevent companies from taking that control away.

It's time to come to grips with AI

Silver Bulletin • 922 implied HN points • 27 Jan 25
🕹 Technology AI Digital innovation Tech Policy Software Development Data Ethics
  1. AI is becoming very powerful and it could change many things in society. We need to talk about its risks and benefits honestly.
  2. The left is not fully engaging in discussions about AI, which is concerning as this technology is rapidly evolving. Everyone should be part of the conversation to shape its future.
  3. Dismissing AI as overhyped is misguided; rather, we should explore its potential impacts and work together to ensure it benefits everyone.

AI's Big Hype

The Biblioracle Recommends • 511 implied HN points • 28 May 23
🕹 Technology AI Artificial Intelligence Tech regulation Data Ethics Future Impacts
  1. People promoting generative AI want us to believe it is inevitable, but that doesn't mean it's without risks.
  2. Humanity often faces catastrophic failures due to a mix of bad structural incentives and human desires.
  3. The push for artificial intelligence might lead to a world where human expression is replaced by algorithms, impacting writing and creativity.
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When in Doubt, Abstain: Why Machine Learning Models Need to Know Their Limits

Mindful Modeler • 139 implied HN points • 18 Apr 23
🕹 Technology Machine Learning Data Ethics Artificial Intelligence Model Training
  1. Machine learning models should not always provide an answer and should learn to abstain if uncertain or lacking information.
  2. Abstaining from making predictions can help in various scenarios like uncertain decisions, out-of-distribution data, and biased outputs.
  3. Implementing methods like outlier detection, input checks, reinforcement learning, and measuring prediction uncertainty can help models in learning when to abstain.

Mechanical Turk is aptly named

The Counterfactual • 79 implied HN points • 16 Jun 23
🕹 Technology AI Automation Gig Economy Human-computer interaction Data Ethics
  1. The Mechanical Turk was a famous hoax in the 18th century that impressed many by pretending to be an intelligent chess-playing machine, but it actually relied on a hidden human operator.
  2. Today, Amazon Mechanical Turk allows people to complete simple tasks that machines struggle with. It's a platform where those who need work can connect with people willing to do it for a small fee.
  3. Recent studies reveal that many tasks on MTurk may not be done by humans at all; a significant portion are actually completed using AI tools, raising questions about the reliability of data collected from such platforms.

Meta’s new AI image generator underwent training using a dataset of 1.1B Instagram and Facebook pics

philsiarri • 44 implied HN points • 07 Dec 23
🕹 Technology AI Data Ethics Image Generation Social media
  1. Meta introduced an AI image generator trained on 1.1 billion Instagram and Facebook images.
  2. The AI creates images from text prompts and aims for aesthetic appeal.
  3. Questions on data ethics arose due to the extensive training dataset, leading Meta to implement filters and a watermarking system.

Open sourced AI is the worst thing imaginable

Theology • 3 implied HN points • 04 Apr 23
🕹 Technology AI Open Source Data Ethics Programming Artificial Intelligence
  1. Open-sourced AI can be dangerous when unregulated and in the hands of individuals who may use it for harmful purposes.
  2. The proliferation of open-source AI projects without proper ethical boundaries makes it challenging for regulators to monitor and control its potential risks.
  3. There is a significant concern over the unintended consequences of developers creating and sharing homebrew versions of AI models, leading to a lack of understanding and control over the technology's impact.

Gradient Flow #16: Conversational Assistants, Model Compression, Cloud Native

Gradient Flow • 0 implied HN points • 27 Aug 20
🕹 Technology AI Machine Learning Infrastructure Data Ethics Virtual Conferences
  1. Best practices for conversational AI applications include using developer tools and software engineering practices.
  2. Model compression is crucial for deploying efficient NLP models due to challenges in deploying large models on servers.
  3. The importance of machine learning, especially deep learning and reinforcement learning, is growing, leading to challenges for developers in terms of model optimization and scaling.

Latent Space Art Academy

Cybernetic Forests • 0 implied HN points • 05 Feb 23
🎨 Art & Illustration Data Ethics Cybernetics Art history Digital Art
  1. The Latent Space Art Academy offers a course on AI images, delving into data ethics and media studies through histories of computation and art.
  2. The course includes guest lectures, exploring topics like cybernetics, art, AI-generated knitting patterns, and the societal impacts of AI technologies.
  3. By focusing on making images with AI, the course aims to help students understand how AI works, its cultural context, and how it can redefine our relationship with technology.