Mel Andrews on Twitter / X

On the person-based predictive policing of AI

Ethics and Information Technology -

On the person-based predictive policing of AI

Ethics and Information Technology -

Recommender systems and their ethical challenges

This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system.

Cas (Stephen Casper) on Twitter / X

It is hard to overstate how disappointing I think this new paper from Oxford, OpenAI, Anthropic, and Google (et al) is. I can't take it seriously as academic work, just as propaganda. It also has some very bad scholarship and questionable adherence to research ethics. Having… pic.twitter.com/Z5fBx360ya— Cas (Stephen Casper) (@StephenLCasper) May 13, 2026

Predictive policing AI is on the rise − making it accountable to the public could curb its harmful effects

AI that anticipates where crimes are likely to occur and who might commit them has a troubling track record. Democratic accountability could shine a light on the technology and how it’s used.

Predictive policing AI is on the rise − making it accountable to the public could curb its harmful effects

AI that anticipates where crimes are likely to occur and who might commit them has a troubling track record. Democratic accountability could shine a light on the technology and how it’s used.

🌸 ellie 🌸 on Twitter / X

One benefit of a “proof of fidelity” system where you aren’t trying to solve for such a narrow question of “is this a human”, is that you don’t need to rely on means of validation that can be biased or inaccessible for many actual humans, such as gov IDs or irl events.2/x— 🌸 ellie 🌸 (@heyellieday) July 29, 2021

People Defer to Ai Moral Advice, but Not Blindly

As AI large language models (LLMs) become increasingly embedded in everyday technologies, should we be concerned about their capacity to influence human beliefs - particularly in the moral domain? Being persuaded ...

Feminist Data Manifest-No

1. We refuse to operate under the assumption that risk and harm associated with data practices can be bounded to mean the same thing for everyone, everywhere, at every time. We commit to acknowledging how historical and systemic patterns of violence and exploitation produce differential vulnerabilities for communities.

Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial

Racial bias in predictive policing algorithms has been the focus of a number of recent news articles, statements of concern by several national organizations (e.g., the ACLU and NAACP), and simulat...

Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial

Racial bias in predictive policing algorithms has been the focus of a number of recent news articles, statements of concern by several national organizations (e.g., the ACLU and NAACP), and simulat...

Taking AI Welfare Seriously

In this report, we argue that there is a realistic possibility that some AI systems will be conscious and/or robustly agentic in the near future. That means that the prospect of AI welfare and moral patienthood, i.e. of AI systems with their own interests and moral significance, is no longer an issue only for sci-fi or the distant future. It is an issue for the near future, and AI companies and other actors have a responsibility to start taking it seriously. We also recommend three early steps that AI companies and other actors can take: They can (1) acknowledge that AI welfare is an important and difficult issue (and ensure that language model outputs do the same), (2) start assessing AI systems for evidence of consciousness and robust agency, and (3) prepare policies and procedures for treating AI systems with an appropriate level of moral concern. To be clear, our argument in this report is not that AI systems definitely are, or will be, conscious, robustly agentic, or otherwise morally significant. Instead, our argument is that there is substantial uncertainty about these possibilities, and so we need to improve our understanding of AI welfare and our ability to make wise decisions about this issue. Otherwise there is a significant risk that we will mishandle decisions about AI welfare, mistakenly harming AI systems that matter morally and/or mistakenly caring for AI systems that do not.

Practical Data Ethics

Free, online course from fast.ai and USF Data Institute covering disinformation, bias & fairness, ethical foundations, practical tools, privacy & surveillance, the silicon valley ecosystem, and algorithmic colonialism

Privacy Policy

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

AI language model rivals expert ethicist in perceived moral expertise

People view AI as possessing expertise across various fields, but the perceived quality of AI-generated moral expertise remains uncertain. Recent work suggests that large language models (LLMs) perform well on tasks designed to assess moral alignment, reflecting moral judgments with relatively high accuracy. As LLMs are increasingly employed in decision-making roles, there is a growing expectation for them to offer not just aligned judgments but also demonstrate sound moral reasoning. Here, we advance work on the Moral Turing Test and find that Americans rate ethical advice from GPT-4o as slightly more moral, trustworthy, thoughtful, and correct than that of the popular New York Times advice column, The Ethicist. Participants perceived GPT models as surpassing both a representative sample of Americans and a renowned ethicist in delivering moral justifications and advice, suggesting that people may increasingly view LLM outputs as viable sources of moral expertise. This work suggests that people might see LLMs as valuable complements to human expertise in moral guidance and decision-making. It also underscores the importance of carefully programming ethical guidelines in LLMs, considering their potential to influence users’ moral reasoning.