Tag: Responsible

Safe, Secure, Ethical, Responsible Technologies and Emerging Applications


Free Download Safe, Secure, Ethical, Responsible Technologies and Emerging Applications
English | 2024 | ISBN: 3031563956 | 420 Pages | PDF (True) | 52 MB
The 24 full papers were carefully reviewed and selected from 75 submissions. They were organized in topical sections as follows: Regulations and Ethics of Artificial Intelligence, Resource-constrained Networks and Cybersecurity, Emerging Artificial Intelligence Applications, Reviews.

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Responsible Use of AI in Military Systems


Free Download Responsible Use of AI in Military Systems
English | 2024 | ISBN: 1032524308 | 387 Pages | PDF (True) | 6 MB
Artificial Intelligence (AI) is widely used in society today. The (mis)use of biased data sets in machine learning applications is well‑known, resulting in discrimination and exclusion of citizens. Another example is the use of non‑transparent algorithms that can’t explain themselves to users, resulting in the AI not being trusted and therefore not being used when it might be beneficial to use it.

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Simply Responsible Basic Blame, Scant Praise, and Minimal Agency [Audiobook]


Free Download Matt King, Kyle Snyder (Narrator), "Simply Responsible: Basic Blame, Scant Praise, and Minimal Agency"
English | ASIN: B0CFW16XWM | 2023 | M4B@64 kbps | ~06:23:00 | 161 MB
We evaluate people all the time for a wide variety of activities. We blame them for miscalculations, uninspired art, and committing crimes. We praise them for detailed brushwork, a superb pass, and their acts of kindness. We accomplish things, from solving crosswords to mastering guitar solos. We bungle our endeavors, whether this is letting a friend down or burning dinner. Sometimes these deeds are morally significant, but many times they are not.
Simply Responsible defends the radical proposal that the blameworthy artist is responsible in just the same way that the blameworthy thief is. We can be responsible for all kinds of different activities, from lip-synching to long division, from murders to meringues, but the relation involved, what author Matt King calls the basic responsibility relation, is the same in every case. We are responsible for the things we do first, then blameworthy or praiseworthy for having done them in light of whether they’re good or bad, according to a variety of standards.
According to most accounts, moral responsibility is either a special species of responsibility or else depends on moralized capacities. In contrast, King argues that we get a more complete and unifying picture of responsible agency from a more general theory of responsibility.

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Data-Centric AI Best Practices, Responsible AI, and More


Free Download Data-Centric AI Best Practices, Responsible AI, and More
Released 1/2024
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 2h 50m | Size: 321 MB
Machine learning typically focuses on producing effective models for a given dataset. In real-world applications, data is messy and improving models is not the only way to get better performance. Data-centric AI (DCAI) is an emerging science that studies techniques to improve datasets, which is often the best way to improve performance in practical ML applications. While data scientists have long practiced this manually via ad hoc trial/error and intuition, DCAI considers the improvement of data as a systematic engineering discipline. In this course, Aishwarya Srinivasan covers the data-centric principles that guide our path forward in this new age of AI as we shift from a model-centric approach to a data-centric paradigm. Learn about DCAI-what it is and the value it offers. Aishwarya covers the DCAI workflow; MLOps as part of DCAI; data validation and preprocessing; model validation; bias detection and mitigation; responsible AI; and more.

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Building Responsible AI Algorithms A Framework for Transparency, Fairness, Safety, Privacy, and Robustness


Free Download Building Responsible AI Algorithms: A Framework for Transparency, Fairness, Safety, Privacy, and Robustness by Toju Duke
English | August 17, 2023 | ISBN: 1484293053 | 208 pages | MOBI | 0.76 Mb
This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts – that in some cases have caused loss of life – and develop models that are fair, transparent, safe, secure, and robust.

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Responsible AI in the Enterprise


Free Download Responsible AI in the Enterprise: Practical AI risk management for explainable, auditable, and safe models with hyperscalers and Azure OpenAI by Adnan Masood, Heather Dawe, Dr. Ehsan Adeli
English | July 31, 2023 | ISBN: 1803230525 | 318 pages | PDF | 6.89 Mb
Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfalls Purchase of the print or Kindle book includes a free PDF eBook

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