To what extent should one trust a statement that a program is free of Trojan horses? Perhaps it is more important to trust the people who wrote the software.
Can You Trust Your Computer? - GNU Project - Free Software Foundation
Who should your computer take its orders from? Most people think their computers should obey them, not obey someone else. With a plan they call “trusted computing,” large media corporations (including the movie companies and record companies), together with computer companies such as Microsoft and Intel, are planning to make your computer obey them instead of you. (Microsoft's version of this scheme is called Palladium.) Proprietary programs have included malicious features before, but this plan would make it universal.
Joanna Rutkowska (@rootkovska) on X
Distrusts computers. Keeps embracing them anyway. Previously: Golem Foundation/Wildland, Qubes OS, hardware/virtualization/OS security research.
Trust & Safety Library - Trust & Safety Professional Association
Welcome to the Trust & Safety Library! We use this space to collect articles, blog posts, journal articles, lectures, podcasts, and websites that trust and safety professionals may find useful in developing policies, supporting moderators, building systems to detect violations, and generally deepening their practice. We welcome your submissions and feedback. This project was initially
LinkedTrust — Deep Tech. Human Trust.
Verified trust systems, civic tech platforms, and AI-powered tools. Real projects, real impact.
Establishing trust in automated reasoning - MetaROR
Since its beginnings in the 1940s, automated reasoning by computers has become a tool of ever growing importance in scientific research. So far, the rules underlying automated reasoning have mainly been formulated by humans, in the form of program source code. Rules derived from large amounts of data, via machine learning techniques, are a complementary approach currently under intense development. The question of why we should trust these systems, and the results obtained with their help, has been discussed by early practitioners of computational science, but was later forgotten. The present work focuses on independent reviewing, an important source of trust in science, and identifies the characteristics of automated reasoning systems that affect their reviewability. It also discusses possible steps towards increasing reviewability and trustworthiness via a combination of technical and social measures.
Who Can You Trust?
Reprint: R1403K Success in business unquestionably requires some willingness to cooperate through having faith in others. The question is how much faith and in whom? Decades of scientific research show that people’s accuracy in deciding if another can be trusted tends to be only slightly better than chance. But this isn’t because trustworthiness is completely unpredictable. It’s because the guidelines most of us use to make these predictions are flawed. We place too much emphasis on reputation and perceived confidence, ignoring the fact that human behavior is always sensitive to context and can often be better assessed by our own intuition. So when your company’s money and resources are on the line, how can you do a better job of gauging trustworthiness and thereby improve your likelihood of success? This article draws on emerging research to show how trustworthiness works and offers four points to keep in mind the next time you’re deciding whether or not to do business with a new partner: Integrity can vary, power does corrupt, confidence often masks incompetence, and it’s OK to trust your gut.
Building Software for a Zero Trust World
How we are helping to build Zero Trust architectures that safeguard the most protected data in the world
Inverting the physics of trust
Common Fabric is a social computing lab building a medium for software that revolves around people, not apps.
Open Source Hype Antidote
critical and alternative views on free and open source software
Trust Revolution
Unfiltered conversations with builders, thinkers, and operators in Bitcoin and beyond. Exploring systems we trust, why they work (or don't), and what's next.
Self-guaranteeing promises
A self-guaranteeing promise is verifiable and non-reversible. It does not require you to trust anyone.
Can AI Detectors Be Trusted?
Society has always trusted editors, teachers, peer reviewers, and readers to set acceptable standards for writing. In the past, the biggest challenge was detecting plagiarism and cracking down on other forms of cheating. Now, AI detectors are rewriting the script: they assign a score based on the likelihood that the text was created by a human or a large language model (LLM).
Trust: the inside story of the rise and fall of Ethereum | Aeon Essays
Blockchain enthusiasts crave a world without bankers, lawyers or fat-cat executives. There’s just one problem: trust
Why Should I Trust You?
News Commentary Podcast · Updated Weekly · Bold, unfiltered, and uncompromisingly honest, Why Should I Trust You? is a weekly podcast that looks at the breakdown in trust for science and public health. It drops every Thursday, with occasional…
Trust and reliance on AI — An experimental study on the extent and costs of overreliance on AI
Decision-making is undergoing rapid changes due to the introduction of artificial intelligence (AI), as AI recommender systems can help mitigate human flaws and increase decision accuracy and efficiency. However, AI can also commit errors or suffer from algorithmic bias. Hence, blind trust in technologies carries risks, as users may follow detrimental advice resulting in undesired consequences. Building upon research on algorithm appreciation and trust in AI, the current study investigates whether users who receive AI advice in an uncertain situation overrely on this advice — to their own detriment and that of other parties. In a domain-independent, incentivized, and interactive behavioral experiment, we find that the mere knowledge of advice being generated by an AI causes people to overrely on it, that is, to follow AI advice even when it contradicts available contextual information as well as their own assessment. Frequently, this overreliance leads not only to inefficient outcomes for the advisee, but also to undesired effects regarding third parties. The results call into question how AI is being used in assisted decision making, emphasizing the importance of AI literacy and effective trust calibration for productive deployment of such systems.