<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AI Ethics &amp; Governance on</title><link>https://dasarpai.com/tags/ai-ethics--governance/</link><description>Recent content in AI Ethics &amp; Governance on</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>hari@dasarpai.com (Dr. Hari Thapliyaal)</managingEditor><webMaster>hari@dasarpai.com (Dr. Hari Thapliyaal)</webMaster><copyright>© 2026 Dr. Hari Thapliyaal</copyright><lastBuildDate>Tue, 27 Aug 2024 00:00:00 +0000</lastBuildDate><atom:link href="https://dasarpai.com/tags/ai-ethics--governance/index.xml" rel="self" type="application/rss+xml"/><item><title>All About AI Hype</title><link>https://dasarpai.com/dsblog/all-about-ai-hype/</link><pubDate>Tue, 27 Aug 2024 00:00:00 +0000</pubDate><author>hari@dasarpai.com (Dr. Hari Thapliyaal)</author><guid>https://dasarpai.com/dsblog/all-about-ai-hype/</guid><description>&lt;p>
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&lt;h1 class="relative group">All About AI Hype
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&lt;p>Artificial Intelligence (AI) is a buzzword that has permeated almost every aspect of modern life. From the way we work and communicate to how we manage our environment and interact with animals, AI&amp;rsquo;s impact is being felt far and wide. But how deep is this impact, really? Is it truly revolutionary, or is it just another over-hyped trend that will fade with time?&lt;/p></description></item><item><title>AI Usecases in Cybersecurity</title><link>https://dasarpai.com/dsblog/ai-usecases-in-cybersecurity/</link><pubDate>Wed, 07 Aug 2024 00:00:00 +0000</pubDate><author>hari@dasarpai.com (Dr. Hari Thapliyaal)</author><guid>https://dasarpai.com/dsblog/ai-usecases-in-cybersecurity/</guid><description>&lt;p>
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&lt;h1 class="relative group">AI Usecases in Cybersecurity
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&lt;h1 class="relative group">AI in Cyber Security, Ethics Related Challenges and Usecases
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&lt;p>Threat Detection and Response
AI can enhance the detection and response to cybersecurity threats by:&lt;/p></description></item><item><title>Open Source vs Closed Source AI</title><link>https://dasarpai.com/dsblog/open-source-vs-closed-source-ai/</link><pubDate>Tue, 06 Aug 2024 00:00:00 +0000</pubDate><author>hari@dasarpai.com (Dr. Hari Thapliyaal)</author><guid>https://dasarpai.com/dsblog/open-source-vs-closed-source-ai/</guid><description>&lt;p>
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&lt;h1 class="relative group">Open Source AI vs Closed Source AI
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&lt;p>Major players in the AI industry, such as Google, Microsoft, IBM, Salesforce, etc each have their own proprietary models and infrastructure to host these models. They offer AI services that companies use to develop AI products for either their end customers or internal use. Training or developing AI models requires expensive hardware and highly skilled personnel, making it a costly process. However, the deployment and inference stages are even more expensive, as they involve ongoing costs for hardware and monitoring.&lt;/p></description></item><item><title>LLM Security and Ethics Considerations</title><link>https://dasarpai.com/dsblog/llm-security-and-ethics-considerations/</link><pubDate>Fri, 02 Aug 2024 00:00:00 +0000</pubDate><author>hari@dasarpai.com (Dr. Hari Thapliyaal)</author><guid>https://dasarpai.com/dsblog/llm-security-and-ethics-considerations/</guid><description>&lt;p>
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&lt;h1 class="relative group">LLM Security and Ethics Considerations
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&lt;h2 class="relative group">Question: For my client&amp;rsquo;s highly secured data like health industry data, banking, insurnace, internal security, etc. data can I use gpt4 for finetuning?
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&lt;p>For highly sensitive data, such as health industry data, banking information, insurance details, and security data, using models like ChatGPT-3.5 or GPT-4 involves several considerations to ensure security and compliance:&lt;/p></description></item><item><title>Responsible AI</title><link>https://dasarpai.com/dsblog/responsible-ai/</link><pubDate>Fri, 03 Feb 2023 00:00:00 +0000</pubDate><author>hari@dasarpai.com (Dr. Hari Thapliyaal)</author><guid>https://dasarpai.com/dsblog/responsible-ai/</guid><description>&lt;p>
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&lt;h1 class="relative group">Responsible AI
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&lt;h2 class="relative group">Introduction:
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&lt;p>Artificial Intelligence (AI) is rapidly transforming the way we live, work, and interact with the world around us. As AI systems become increasingly sophisticated and ubiquitous, it is more important than ever to ensure that they are developed and deployed in a responsible and ethical manner. Responsible AI refers to the principles and practices of developing and using AI in a way that is safe, transparent, accountable, and aligned with human values. In this blog post, we will explore the different aspects of responsible AI, including ethics, safety, transparency, accountability, and societal impact.&lt;/p></description></item><item><title>What is XAI?</title><link>https://dasarpai.com/dsblog/what-is-xai/</link><pubDate>Fri, 15 May 2020 15:50:00 +0530</pubDate><author>hari@dasarpai.com (Dr. Hari Thapliyaal)</author><guid>https://dasarpai.com/dsblog/what-is-xai/</guid><description>&lt;p>
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&lt;h2 class="relative group">XAI in Simple Language!
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&lt;p>The discipline of Data Science and AI has introduced many terms into discussions that might seem complicated at first. In reality, many of these terms are intuitive and straightforward when considered from a natural intelligence perspective. However, from a technological standpoint, they can be complex. To understand XAI, let’s explore a few examples.&lt;/p></description></item></channel></rss>