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

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.

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 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

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.
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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.

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.

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.