AI Snake Oil

AI Snake Oil scrutinizes AI's effectiveness and ethical concerns, exploring the gap between theoretical benchmarks and real-world application, copyright issues with generative AI, societal risks, and the potential for technology misuse. It also evaluates AI's impact on professional sectors, model biases, and proposes standards for responsible development.

AI Effectiveness and Application Copyright and Generative AI AI and Society Professional Sector Impact AI Model Bias and Ethics Responsible AI Development

The hottest Substack posts of AI Snake Oil

And their main takeaways

Is AI progress slowing down?

1297 implied HN points • 18 Dec 24
🕹 Technology AI Development
  1. The idea that AI progress is surely slowing down might be too hasty. We may not have explored all the ways to improve AI through model scaling just yet.
  2. Industry experts often change their predictions about AI, showing that they might not know as much as we assume. Their interests can influence their views, so take their forecasts with a grain of salt.
  3. While new methods like inference scaling can boost AI capabilities quickly, the actual impact on real-world applications may take time due to product development lags and varying reliability.

We Looked at 78 Election Deepfakes. Political Misinformation is not an AI Problem.

1171 implied HN points • 13 Dec 24
🇺🇸 U.S. Politics Political Science Misinformation Media Studies AI Ethics
  1. Many uses of AI in political contexts aren't trying to deceive. In fact, about half of the deepfakes created in elections were used for legitimate purposes like enhancing campaigns or providing satire.
  2. Creating deceptive misinformation doesn't need AI. It can be done cheaply and easily with regular editing tools or even just by hiring people, meaning AI isn't the sole cause of these issues.
  3. The bigger problem isn’t the technology itself but the demand for misinformation. People’s preferences and existing beliefs drive them to seek out and accept false information, making structural changes more critical than just focusing on AI.

Does the UK’s liver transplant matching algorithm systematically exclude younger patients?

864 implied HN points • 11 Nov 24
🏥 Health Politics Medical Ethics Healthcare policy Predictive Modeling
  1. The liver transplant matching algorithm in the UK might favor older patients over younger ones, which raises serious ethical concerns. This can lead to younger patients, even if they are very sick, being overlooked for transplants.
  2. Using predictive algorithms in healthcare can be risky. They can have biases that might not be obvious, like wrongly estimating how long patients will live after a transplant based on a five-year cap.
  3. It's important for the public to have a voice in how medical algorithms are created and used. Better understanding and participation can help ensure fair and just treatment for all patients.

AI safety is not a model property

796 implied HN points • 12 Mar 24
🕹 Technology AI Machine Learning Cybersecurity Ethics
  1. AI safety is not a property of AI models, but depends heavily on the context and environment in which the AI system is deployed.
  2. Efforts to fix AI safety solely at the model level are limited, as misuses can still occur since models lack necessary context for decision-making.
  3. Defenses against AI model misuse should focus primarily outside models, on attack surfaces like email scanners and URL blacklists, and red teaming should shift towards early warning of adversary capabilities.
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Will AI transform law?

648 implied HN points • 24 Jan 24
🕹 Technology AI Legal Predictions Research Evaluation
  1. The idea of AI replacing lawyers is plausible but not well-supported by current evidence.
  2. Applications of AI in law can be categorized into information processing, creativity/judgment tasks, and predicting the future.
  3. Evaluation of AI in law needs to advance beyond static benchmarks to real-world deployment scenarios.

On the Societal Impact of Open Foundation Models

398 implied HN points • 27 Feb 24
🕹 Technology AI Societal Impact Risk Assessment Regulation
  1. The paper on the societal impact of open foundation models clarifies the discrepancy in claims about openness's societal effects, examines the benefits like transparency and empowering research, and proposes a risk evaluation framework for comparing risks of open vs. closed foundation models and existing technologies.
  2. The framework for risk assessment in the paper outlines steps like threat identification, evaluating existing risks and defenses, and determining the marginal risk of open foundation models. It aims to provide a structured approach to analyzing risks associated with open foundation models.
  3. By analyzing benefits, such as distribution of decision-making power, innovation, scientific research facilitation, and transparency, the paper sheds light on the advantages of open foundation models and offers recommendations for developers, researchers, regulators, and policymakers to navigate the landscape effectively.

A misleading open letter about sci-fi AI dangers ignores the real risks

1171 implied HN points • 29 Mar 23
🕹 Technology AI Ethics Security Policy Risk
  1. Misinformation, labor impact, and safety are key AI risks raised in an open letter.
  2. Speculative risks like malicious disinformation campaigns overlook real harm caused by over-reliance on AI tools.
  3. Addressing near-term security risks from AI integration into real-world applications is crucial, and the containment mindset may not be effective.

A safe harbor for AI evaluation and red teaming

307 implied HN points • 05 Mar 24
🕹 Technology AI Research Safety Evaluation Models
  1. Independent evaluation of AI models is crucial for uncovering vulnerabilities and ensuring safety, security, and trust
  2. Terms of service can discourage community-led evaluations of AI models, hindering essential research
  3. A legal and technical safe harbor is proposed to protect and encourage public interest research into AI safety, removing barriers and improving ecosystem norms

What the executive order means for openness in AI

489 implied HN points • 31 Oct 23
🕹 Technology AI Policy Compliance Research
  1. The executive order on AI strives to address various benefits and risks, impacting openness in the AI landscape.
  2. The EO does not include licensing or liability provisions, which could limit openness in AI development.
  3. The EO emphasizes defense against malicious AI uses, registration and reporting requirements, and transparency audits to ensure security and accountability.

Introducing the REFORMS checklist for ML-based science

432 implied HN points • 16 Aug 23
🔬 Science Machine Learning
  1. ML-based science often has errors like data leakage that skew results.
  2. Errors in ML-based science can also stem from how study findings are interpreted and presented.
  3. The REFORMS checklist can help improve reporting standards in ML-based science, minimizing errors and enhancing clarity.