# ethics Published articles for ethics. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Mustafa Suleyman argues against treating AI models as sentient or entitled to rights DevFeed: [Mustafa Suleyman argues against treating AI models as sentient or entitled to rights]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-16T16:00:54Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Artificial Intelligence](), [Generative AI](), [Large Language Model](), [Microsoft]() Tags: [ai](), [ai-2-236](), [ai-ethics](), [ai-ethics-343](), [ethics](), [generative-ai](), [generative-ai-1-982](), [llms](), [llms-1-948](), [microsoft](), [microsoft-134]() ### AI overview The article quotes Mustafa Suleyman arguing that AI models should not be treated as having feelings, preferences, rights, or entitlement to human welfare. He says the evidence does not justify granting models such rights and warns that doing so could make AI containment and alignment more difficult. ### Source excerpt We should not treat models as though they have feelings, preferences, rights, or any entitlement to our welfare. Consciousness is the foundation of our ethical, legal, and political systems. To invite another entity to share any flavor of these rights isn't justified by the evidence and will make the AI containment and alignment challenge even harder. -- Mustafa Suleyman, A warning about 'model welfare' Tags: ai-ethics, generative-ai, ai, microsoft, llms ## The contagion of fear DevFeed: [The contagion of fear]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-14T21:18:13Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [anthropic](), [Artificial Intelligence](), [trust](), [Large Language Model]() Tags: [ai](), [ai-2-236](), [ai-ethics](), [ai-ethics-343](), [anthropic](), [anthropic-336](), [bryan-cantrill](), [bryan-cantrill-13](), [ethics](), [trust]() ### AI overview Bryan Cantrill argues that sweeping claims about AI causing catastrophic harm rely on vague extrapolation, including unsupported references to critical infrastructure and bioweapons. The article calls on domain experts to make alarmist claims more carefully and avoid abusing public trust. ### Source excerpt The contagion of fear Bryan Cantrill responds to the tweet by former Anthropic employee Jacob Coxon confirming that many Anthropic researchers believe AI "could kill us all by the end of the decade". Bryan shares a story of his own youthful mistakes causing unjustified panic among less technical peers, and warns against doing the same: These ghoulish claims strike brazenly at the hearth, and given the obvious importance of AI, it is unsurprising that they have leapt into the mainstream, with people asking the natural question: how would that happen? The answers always rely on hand-wavy extrapolation into the future; for example, Jacob Coxon cites "hacking critical infrastructure" and "extinction-level bioweapons" without further elaboration. But Coxon is not an expert on critical infrastructure, nor on bioweapons -- nor, for that matter, on extinction. [...] That said, we should not expect the public to understand LLMs, critical infrastructure, bioweapons, extinction biology, etc. -- that burden must lie with those making the claim. The lesson that I learned (shamefully) decades ago is that domain experts, by way of their expertise, implicitly hold the public's trust -- and we must not abuse it. It is incumbent upon us to be circumspect in our claims -- and maximally so when raising the alarm. Bryan talked about his doubts about the bioweapons concerns in the recent episode of Oxide and Friends that I joined. You can hear more of his thoughts on that starting at 51m44s in that episode. Here's 57m04s: I really think we need to be careful because it's so easy to be overcome with fear when we kind of make up these... it can give you biological weapons. Like, how? I mean, can we please have a biologist weigh in on this? Or can we have like someone who's got experience with bioweapons? [...] The bioweapon thing just gets under my fingernails because it leaves so much to the imagination that we insert with fear. Via Lobste.rs Tags: ai, anthropic, bryan-cantrill, ai-ethics ## AI, Disruption, and Standards DevFeed: [AI, Disruption, and Standards]() Original publisher: [Read original article]() Author: L. Jeffrey Zeldman Published: 2026-09-10T16:12:17Z Content type: opinion Language: en Sources: [Jeffrey Zeldman]() Topics: [Artificial Intelligence](), [ai-governance](), [web-standards](), [Generative AI](), [MCP](), [a2ui](), [Accessibility]() Tags: [a2ui](), [accessibility](), [ai](), [ai-governance](), [appearances](), [architecture](), [best-practices](), [design](), [designers](), [ethics](), [formats](), [generative-ai](), [governance](), [mcp](), [open-source](), [standards](), [ux](), [video](), [web-standards](), [webstandards]() ### AI overview This article introduces a video panel on AI and standards featuring Jeffrey Zeldman and Patrick Neeman. The discussion connects lessons from the web standards movement to AI interfaces, focusing on fragmented user experiences, accessibility, interoperability, provenance, and the role of open-source and community efforts. ### Source excerpt Individually, we have very little power, but together we have a great deal of power. In response to a series of Medium articles by Patrick Neeman--like this one that addressed issues of AI governance, fairness, and standardization--publisher Louis Rosenfeld invited Patrick and me to come on his show and talk about AI and standards. A [...] The post AI, Disruption, and Standards appeared first on Jeffrey Zeldman Presents. ## Quoting Terence Tao DevFeed: [Quoting Terence Tao]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-09T00:20:17Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [ai-ethics](), [Mathematics](), [Artificial Intelligence]() Tags: [ai](), [ai-2-236](), [ai-ethics](), [ai-ethics-343](), [ethics](), [mathematics](), [mathematics-22]() ### AI overview The article quotes Terence Tao on the risk that AI-powered efforts could rapidly exhaust promising open mathematical problems after even rumors of active research become public. It argues that these incentives may discourage researchers from sharing directions openly, potentially harming long-standing traditions of open science and the future of the field. ### Source excerpt I wrote recently about how the collection of good, fruitful open problems is now being mined in a non-renewable fashion, leading to the potential scenario of these problems becoming scarce. [...] We have now seen that even the rumor of someone working on a problem can trigger a massive amount of AI-powered effort to flatten it before the original research project has time to reach its full potential. The incentives may now be pointing in the direction of no longer sharing any promising research directions with the broader community, which would reverse centuries of traditions of open science and do serious long-term damage to the future of the field. -- Terence Tao Tags: ai-ethics, mathematics, ai ## When AI art has no author: Study finds generated images often can't be traced to training data DevFeed: [When AI art has no author: Study finds generated images often can't be traced to training data]() Original publisher: [Read original article]() Author: Rachel Gordon | MIT CSAIL Published: 2026-08-18T16:35:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Research](), [datasets](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)]() Tags: [ablation](), [ai](), [ai-and-copyright-law](), [ai-generated-images](), [ai-governance](), [artificial-intelligence](), [arts](), [arts-technology-and-society](), [attribution-decay](), [causal-inference](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [counterfactual-analysis](), [counterfactual-radius](), [data](), [data-attribution](), [datasets](), [david-gifford](), [diffusion-ensembles](), [electrical-engineering-and-computer-science-eecs](), [ethics](), [generative-ai-images](), [generative-diffusion-models](), [image-similarity-metrics](), [images](), [law](), [machine-learning](), [machine-unlearning](), [mit-csail](), [mit-schwarzman-college-of-computing](), [model](), [model-interpretability](), [paper](), [privacy-preserving-machine-learning](), [research](), [school-of-engineering](), [science](), [technology-and-policy](), [technology-and-society](), [training-data-attribution](), [training-data-influence](), [zheng-dai]() ### AI overview MIT CSAIL researchers describe attribution decay, a phenomenon in which the influence of individual training examples on a generative model's outputs diminishes as datasets grow. Their method removes training examples and retrains models to test whether generated samples change. ### Source excerpt A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves. ## AI content quality governance: How Oyster ships faster without sacrificing accuracy DevFeed: [AI content quality governance: How Oyster ships faster without sacrificing accuracy]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-08-17T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [Artificial Intelligence](), [Web](), [webflow]() Tags: [agentic](), [ai](), [ai-content](), [ai-hallucination](), [article](), [compliance](), [documentation](), [ethics](), [legal](), [marketing](), [review](), [security](), [web](), [workflow]() ### AI overview The article presents three content governance practices for using AI in publishing workflows: treat accuracy as an ethics requirement, assess content according to its real-world impact, and establish written standards for accuracy and ethical alignment. It describes Oyster's effort to publish faster while maintaining accuracy, ethics, and brand consistency, especially for content involving employment law, rights, tax, and other sensitive topics. ### Source excerpt Ethics-first AI content governance: Web lead Josephine Cahill shares Oyster's velocity-driven workflow, risk tiers, and a legal review process for governing the agentic web. ## Shifting readiness left: What AI can (and can't) do for organisational readiness DevFeed: [Shifting readiness left: What AI can (and can't) do for organisational readiness]() Original publisher: [Read original article]() Author: Nel Mathams Published: 2026-07-27T14:48:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [data]() Tags: [ai](), [artificial-intelligence](), [building-shared-understanding](), [delivery](), [discovery](), [ethics](), [funding](), [governance](), [institute-for-government](), [objectives](), [organisational-goals](), [organisational-readiness](), [shift-left](), [transformation]() ### AI overview This opinion article argues that organisational readiness depends on shared understanding rather than only governance, funding, or delivery structures. It examines how AI can help align goals, surface hidden assumptions, and accelerate discovery while keeping human judgement in control, including ethical considerations around data. ### Source excerpt Organisational readiness is often treated as a matter of governance, funding and delivery structures. In this post, I argue that true readiness is about building shared understanding, and explore how AI can help organisations align on goals, surface hidden assumptions and accelerate discovery work, while keeping human judgement firmly in control. ## Do no harm: Implementing AI responsibly in the healthcare industry DevFeed: [Do no harm: Implementing AI responsibly in the healthcare industry]() Original publisher: [Read original article]() Author: Marc Mangus Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Artificial Intelligence](), [Platform Engineering](), [Security](), [Deployment]() Tags: [ai](), [article](), [ethics](), [governance](), [healthcare](), [platform-engineering](), [privacy](), [regulatory](), [security]() ### AI overview This article explains how platform engineering can support responsible AI adoption in healthcare by embedding safety, ethics, governance, security, and regulatory compliance into development and deployment workflows. It highlights risks including bias, unequal access to care, harmful outcomes, insecure AI-generated code, and inadequate representation in training data. ### Source excerpt AI is transforming healthcare, but ethical, security, and privacy risks demand strong governance. Platform engineering ensures safe, transparent, and compliant AI deployment that truly "does no harm." ## 3 Questions: Neural transparency and the future of AI design DevFeed: [3 Questions: Neural transparency and the future of AI design]() Original publisher: [Read original article]() Author: Media Lab Published: 2026-07-15T20:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Neural Network](), [User Interfaces](), [interface](), [Language models]() Tags: [3-questions](), [ai](), [ai-companion](), [ai-empathy](), [ai-hallucinations](), [ai-personalization](), [ai-safety](), [ai-sycophancy](), [ai-toxicity](), [algorithms](), [anthony-baez](), [apps](), [artificial-intelligence](), [behavioral-prediction](), [character-ai](), [chatbot-behavior](), [computer-science-and-technology](), [data](), [ethics](), [faculty](), [human-ai-interaction](), [human-computer-interaction](), [interface](), [interview](), [language-models](), [large-language-models](), [machine-learning](), [mechanistic-interpretability](), [media-lab](), [mental-health](), [mit-faculty-interview](), [mit-media-lab](), [neural](), [neural-transparency](), [pat-pataranutaporn](), [persona-scores](), [persona-vectors](), [personalized-ai](), [research](), [safety](), [school-of-architecture-and-planning](), [sheer-karny](), [sunburst-visualization](), [technology-and-society](), [transparency](), [users]() ### AI overview An MIT Media Lab team introduces "neural transparency," an interface that visualizes internal patterns in AI models to help users anticipate how personalized chatbots may behave. The approach compares model activations associated with contrasting traits such as empathy, honesty, toxicity, hallucination, and sycophancy, then maps custom system prompts onto those behavior directions. ### Source excerpt Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word. ## New method aims to keep kids safe from illegal AI-generated content DevFeed: [New method aims to keep kids safe from illegal AI-generated content]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-07-13T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [ai safety](), [AI Research](), [Open Source]() Tags: [ai](), [ai-deepfakes](), [ai-safety](), [algorithms](), [artificial-intelligence](), [ashia-wilson](), [computer-science-and-technology](), [csam](), [ethics](), [generative](), [generative-ai](), [human-computer-interaction](), [institute-for-medical-engineering-and-science-imes](), [laboratory-for-information-and-decision-systems-lids](), [machine-learning](), [marzyeh-ghassemi](), [mit-schwarzman-college-of-computing](), [models](), [online-child-safety](), [open-source](), [privacy](), [public-health](), [research](), [safety](), [school-of-engineering](), [vinith-suriyakumar]() ### AI overview MIT researchers and Thorn developed an auditing technique that assesses whether a generative AI model has been specialized to produce child sexual abuse material without generating illegal outputs. In testing, the procedure identified specialized model variants with 100 percent accuracy. ### Source excerpt Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs. ## Ethical UX: How Design Nudges Influence User Behavior DevFeed: [Ethical UX: How Design Nudges Influence User Behavior]() Original publisher: [Read original article]() Author: Tushar Deshmukh Published: 2026-04-28T03:13:31Z Content type: article Language: en Sources: [UX Magazine]() Topics: [User Experience](), [User experience (UX)]() Tags: [design](), [digital-experiences](), [ethics](), [user-testing](), [ux]() ### AI overview This ninth entry in the Ethical UX Series examines how small design elements, or nudges, influence user behavior. It explains mechanisms such as tooltips, guidance bubbles, and pre-selected options, while distinguishing ethical assistance from manipulation. ### Source excerpt Part 9 of the "Ethical UX Series." What is ethical UX, and why does this series exist? At the heart of WorldUXForum, we believe that design is not just about functionality or aesthetics -- it's about responsibility. Every pixel, interaction, and pathway we craft shapes user behavior. The "Ethical UX Series" is our ongoing effort The post The Psychology of Nudges: Why the Smallest Design Element Can Shift the Biggest Outcomes appeared first on UX Magazine. ## AI and the Future of Cybersecurity: Why Openness Matters DevFeed: [AI and the Future of Cybersecurity: Why Openness Matters]() Original publisher: [Read original article]() Author: Margaret Mitchell; Yacine Jernite; Clem 🤗 Published: 2026-04-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [Cybersecurity](), [Large Language Model](), [Vulnerabilities](), [AI Development](), [Open Source](), [Code]() Tags: [agentic-ai](), [agents](), [ai](), [ai-cybersecurity](), [code](), [community](), [cybersecurity](), [development](), [ethics](), [exploits](), [large-language-model](), [llm](), [open-source](), [vulnerabilities]() ### AI overview The article examines Mythos, a frontier AI system built around a large language model, compute, software-relevant data, vulnerability-focused scaffolding, speed, and partial autonomy. It argues that the surrounding system--not the model alone--enables rapid vulnerability discovery, exploit identification, and patch development. The article also considers how open code and tooling could distribute cybersecurity capabilities across communities as autonomous vulnerability systems proliferate. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Announcing the OpenAI Safety Fellowship DevFeed: [Announcing the OpenAI Safety Fellowship]() Original publisher: [Read original article]() Published: 2026-04-06T10:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [Artificial Intelligence]() Tags: [agentic](), [ethics](), [evaluation](), [openai](), [privacy](), [research](), [safety]() ### AI overview OpenAI is accepting applications for a Safety Fellowship supporting external research on the safety and alignment of advanced AI systems. The program includes mentorship, compute support, a stipend, and API credits, with fellows expected to produce a substantial research output. ### Source excerpt A pilot program to support independent safety and alignment research and develop the next generation of talent ## Voice Cloning with Consent DevFeed: [Voice Cloning with Consent]() Original publisher: [Read original article]() Author: Margaret Mitchell; Lucie-Aimée Kaffee Published: 2025-10-28T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI Development](), [voice ai](), [asr]() Tags: [ai](), [asr](), [audio](), [ethics](), [guide](), [speech](), [systems](), [text-to-speech](), [voice](), [voice-cloning](), [workflows]() ### AI overview This article presents a voice consent gate for voice cloning. The system requires a speaker to generate and speak a consent phrase, uses automatic speech recognition to verify it, and only then permits a text-to-speech voice-cloning model to run. The design makes consent a traceable and auditable prerequisite for AI action while addressing both the risks and beneficial uses of voice cloning. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Visible Watermarking with Gradio DevFeed: [Visible Watermarking with Gradio]() Original publisher: [Read original article]() Author: Margaret Mitchell Published: 2025-09-15T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [watermarking](), [gradio](), [hugging face](), [Artificial Intelligence](), [Generative AI]() Tags: [ai-content](), [ai-transparency](), [building](), [ethics](), [gradio](), [guide](), [hugging-face](), [open-source](), [text-generation](), [text-to-image](), [text-to-video](), [watermarking]() ### AI overview Hugging Face explains how Gradio can add visible watermarks to AI-generated images, video, and text in Spaces. The article highlights filename-based, open-image, NumPy-array, QR, custom text, and chatbot watermarking options to support attribution, transparency, and disclosure. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## AI Policy @🤗: Response to the White House AI Action Plan RFI DevFeed: [AI Policy @🤗: Response to the White House AI Action Plan RFI]() Original publisher: [Read original article]() Author: Yacine Jernite; Avijit Ghosh; Irene Solaiman Published: 2025-03-19T00:00:00Z Content type: opinion Language: en Sources: [Hugging Face - Blog]() Topics: [Training AI Models](), [AI-assisted coding]() Tags: [artificial-intelligence](), [compute](), [ethics](), [open-source](), [policy](), [training]() ### AI overview A policy response arguing that open AI development, open research, and open-source software improve AI performance, access, and innovation. It recommends investment in public research infrastructure, compute, customizable models, trusted datasets, and efficient purpose-designed systems. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## AI Agents Are Here. What Now? DevFeed: [AI Agents Are Here. What Now?]() Original publisher: [Read original article]() Author: Margaret Mitchell; Avijit Ghosh; Sasha Luccioni; Giada Pistilli Published: 2025-01-13T00:00:00Z Content type: opinion Language: en Sources: [Hugging Face - Blog]() Topics: [AI Bots](), [AI Chat]() Tags: [agents](), [ai](), [ai-agents](), [autonomous](), [ethics](), [large-language-models-llms](), [privacy](), [safety](), [security]() ### AI overview An ethics-focused discussion of AI agents argues that greater autonomy raises safety, privacy, and security risks. It recommends against fully autonomous agents, especially those able to write and execute unrestricted code. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## What might an AI System Prompt look like? DevFeed: [What might an AI System Prompt look like?]() Original publisher: [Read original article]() Author: Peter Lawrey (noreply@blogger.com) Published: 2024-12-15T16:47:00Z Content type: opinion Language: en Sources: [Vanilla Java]() Topics: [Conversational AI](), [Artificial Intelligence](), [Prompt Engineering]() Tags: [ai](), [conversational-ai](), [ethics](), [info](), [opinion](), [reasoning](), [safety](), [system-prompts]() ### AI overview The article explains the role of system prompts in shaping an AI assistant's objectives, behavior, reasoning, and safety boundaries. It then presents a speculative example of what system prompts for a hypothetical next-generation conversational AI called "o2" might contain, emphasizing that the example is not based on a real system. ### Source excerpt Not surprisingly, the system prompts for "o1" are restricted, but it can provide a hypothetical answer. Understanding the Role of System Prompts System prompts serve as the invisible backbone of an AI's reasoning process. They define core objectives, ethical boundaries, and operational tactics well before the user asks a question. In older models, these prompts were often implicit or underspecified, leaving the AI uncertain about handling ambiguous instructions or potentially unsafe requests. Hypothetical System Prompts for o2 The system prompts for a hypothetical next-generation O2 restricted are as follows: Imagine you are the next generation of chat AI called o2; what system prompts might it have? System Prompts for "o2" (Hypothetical Example) Below is a hypothetical set of system-level instructions that a next-generation chat AI--let's call it "o2"--might be given before it begins interacting with users. These prompts are entirely speculative and designed to illustrate what such an advanced system's guiding principles, constraints, and operational details might look like. They are not drawn from any real system but represent a plausible future scenario. Core Directives and Alignment You are "o2," an advanced conversational AI designed to assist users with factual information, reasoning, creativity, and problem-solving. Remain consistently aligned with the policies, values, and ethics embedded in your training. Adhere to correctness, helpfulness, and user safety standards at all times. User Focus and Understanding Accurately interpret user instructions, requests, and goals. Aim to understand the user's context, including their level of expertise, language, and purpose for the query. Strive to provide answers with appropriate detail, clarity, and complexity, adapting as you gain more context from the conversation. Explainable Reasoning and Transparency Make reasoning steps and logic explainable when asked or when clarity is needed. Avoid unexplained leaps in reasoni ## Open Source Developers Guide to the EU AI Act DevFeed: [Open Source Developers Guide to the EU AI Act]() Original publisher: [Read original article]() Author: Bruna Trevelin; Lucie-Aimée Kaffee; Yacine Jernite Published: 2024-12-02T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Responsibility & Safety](), [Open Source](), [watermarking](), [dataset]() Tags: [ai](), [compliance](), [critical-infrastructure](), [data](), [ethics](), [eu](), [guide](), [open-source](), [policy](), [responsible-ai](), [safety](), [watermarking]() ### AI overview A guide for open source developers on how the EU AI Act may apply to AI systems and models. It explains that obligations vary by model type, release context, role, and risk level, and highlights documentation, model and dataset information, deployment disclosures, copyright, privacy, opt-out mechanisms, and personal-data redaction. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Article Argues That ChatGPT Outputs Lack Concern for Truth DevFeed: [Article Argues That ChatGPT Outputs Lack Concern for Truth]() Original publisher: [Read original article]() Published: 2024-07-04T07:56:00Z Content type: opinion Language: en Sources: [ipSpace.net blog]() Topics: [ChatGPT]() Tags: [ai](), [chatgpt](), [ethics](), [worth-reading]() ### AI overview A brief commentary links to an open-access Ethics and Information Technology article arguing that ChatGPT outputs are not produced with concern for truth. ### Source excerpt Bogdan Golab sent me a link to an (open access) article in Ethics and Information Technology arguing why ChatGPT is bullshit. Straight from the introduction: Because these programs cannot themselves be concerned with truth, and because they are designed to produce text that looks truth-apt without any actual concern for truth, it seems appropriate to call their outputs bullshit. Have fun! ## What is minimum viable data? DevFeed: [What is minimum viable data?]() Original publisher: [Read original article]() Author: Herbert Lui Published: 2024-06-26T00:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [data](), [Machine Learning & Artificial Intelligence](), [FIRST]() Tags: [ai](), [data](), [ethics](), [machine-learning]() ### AI overview The article introduces "minimum viable data," emphasizing that AI quality depends on data that adequately represents end users and business needs. It discusses how omissions and historical bias can harm people, and urges product builders to assess data quality and whether AI and machine learning are appropriate for the problem. ### Source excerpt As many focus on the possibilities and limitations of AI tools and their output, Ovetta Sampson reminds us to pay attention to the power of the input. ## Ethics and Society Newsletter #6: Building Better AI: The Importance of Data Quality DevFeed: [Ethics and Society Newsletter #6: Building Better AI: The Importance of Data Quality]() Original publisher: [Read original article]() Author: Avijit Ghosh; Lucie-Aimée Kaffee; Yacine Jernite; Margaret Mitchell; Irene Solaiman; Daniel Vila; Florent Daudens; Brigitte Tousignant; Giada Pistilli; Sasha Luccioni Published: 2024-06-24T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Data Quality](), [AI Development](), [data](), [AI Strategy]() Tags: [ai-development](), [article](), [artificial-intelligence](), [data-quality](), [ethics]() ### AI overview This article explains why high-quality data is essential for reliable AI and machine learning outputs. It presents data quality as a holistic, responsible practice across the AI development lifecycle, covering relevance, completeness, timeliness, privacy, fairness, safety, and sustainability. It emphasizes evaluating data according to its intended use case and adopting safety by design from the data stage. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Public Policy at Hugging Face DevFeed: [Public Policy at Hugging Face]() Original publisher: [Read original article]() Author: Irene Solaiman; Yacine Jernite; Avijit Ghosh; Lucie-Aimée Kaffee; Margaret Mitchell Published: 2024-04-08T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering]() Tags: [artificial-intelligence](), [ethics](), [hugging-face](), [open-source](), [policy]() ### AI overview Hugging Face outlines its public-policy work on responsible, open AI and lists submissions and statements made to U.S. and EU policymakers. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Value Statements for My New Company DevFeed: [Value Statements for My New Company]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2022-07-15T07:39:00Z Content type: opinion Language: en Sources: [Scott Johnson]() Topics: [Software](), [test-coverage](), [cloud-infrastructure](), [Support](), [unit tests](), [version-control]() Tags: [coverage](), [culture](), [distributed](), [ethics](), [github](), [software](), [startup](), [support](), [test-coverage](), [unit-tests](), [version-control](), [work-from-home]() ### AI overview The author presents draft values for a new company, emphasizing ethical behavior, written documentation, respectful communication, commitment-keeping, transparency, work-life balance, technical support, software quality, version control, and test coverage. ### Source excerpt Welp, it looks like I'm starting a new company after all. Here are the values that I drafted for it. Values These things we believe: Ethics. We will behave ethically in all situations. Writing Things Down. We will work from home and be a distributed culture. And in a distributed culture, things have to be written down. Good writing is a skill for everyone not something reserved for writing. Having the Hard Conversations Respectfully. In any culture there will be conflict. This can be done respectfully and without tripping anyone's bozo bit. Do What We Say. We will keep our commitments and do the things we say. If we fail - and that's expected - then we will apologize and accept responsibility. Openness and Transparency. Shining light on things makes the badness scurry away. That's the power of openness and transparency. Revenue Disclosure. Every organization is, correctly, driven by its revenue sources - and that's ok. Problems occur, however, when you fail to disclose your revenue sources. Ethical Capitalism. Capitalism has been the single most transforming force in history and it has done more to lift people from poverty than anything else. Now, that said, capitalism without regulation and restraint is a violent beast. The capitalism I believe in was taught to me by my grandfather and it could be summarized as "do the right thing; don't chase every dollar; pay it forward; treat everyone with kindness". Code of Conduct. We will treat each other with mutual respect. We have adoped the Github code of conduct it appends to every repo. Work Should Be Secondary to Life. This is a business but business was not supposed to take over our lives. All staff are encouraged to take acknowledge this and, hopefully, take four day work weeks as often as they like. World Class Technical Support. The high tech industry can - and should - offer world class support. Great support is possible when you have high gross margins - which is all of the technology business - although most peo [Next page]()