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When it comes to compliance, labelling everything as AI might be a bad idea. statistical analysis, models, and prediction, and this practice should continue without any confusion with AI However, AI does have unique characteristics that, if not understood, could pose significant risks to Nevertheless, labelling all of this as AI might unnecessarily create regulatory uncertainty and complexity The need for defining AI is indeed crucial, not only to separate the boundaries of where new risks not

AI will need to be held accountable. We will need different categories to distinguish between each AI capability: AI Machines - AI systems AI Agents - AI Machines with agency but without moral capacity and limited culpability AI Ethical Agents - AI Agents with moral capacity and full culpability AI Machines can still have agency (self-referencing Perhaps, this is what is meant by autonomous AI.

In this article we will explore sustainability and how it relates to AI technologies. AI. AI technology such as ChatGPT should be designed to be safe. AI Sustainability is perhaps what drives the need for AI safety, security, quality, legal, and ethical Instead, of asking is AI Safe , perhaps we should be asking is AI Sustainable ?

However, AI lacks the knowledge (and most likely always will) that comes from experience along with the

Here are some of the things he said: Three facts about AI: AI has happened ( the genie is out of the What is AI (I have paraphrased this)? Before AI we told the computer how to do what we want - we trained the dog With generative AI we tell on the open internet Don’t teach AI to write code Don’t let AI prompt another AI What is the problem Sure, AI can dumb it done or AI-splain it to us so we feel better.

This is one way to evaluate what is happening with AI.

Why AI Is Different: AI presents unique risks because of its ability to operate with minimal human oversight Challenges with AI Regulation: While regulations like the EU AI Act are emerging, they are still new A Program to Govern AI: A comprehensive AI governance program should include four elements: AI Code of AI Design Standards: Technical guidelines for AI development, emphasizing ethical considerations. AI Safety Policies: Measures to prevent harm and ensure robust testing and monitoring of AI systems.

the European Union's AI Act, the UK National AI Strategy and Proposed AI Act, Canada's Artificial Intelligence AI. AI is safe and ethical. UK National AI Strategy and Proposed AI Act The UK national AI strategy, launched in November 2021, is AI talent.

While this is just one example, I know my experience with AI chat applications is not unique. Many are fond of attributing human qualities to AI which is called anthropomorphism . However, if we are going to anthropomorphize then why not go all the way, and say AI lied . We don’t do this because it applies a standard of morality to the AI system. That's why when it comes to AI systems we need to stop attributing human qualities to them if we hope

Review and Boston Consulting Group (BCG), “The Future of Strategic Management: Enhancing KPIs with AI more than 3,000 managers and interviews with 17 executives to examine how managers and leaders use AI In this report the authors categorize AI-enhanced KPIs in the following way: Smart Descriptive KPIs : Smart Prescriptive KPIs : use AI to recommend actions that optimize performance. (Goodhart’s Law) What steps should be followed when using AI for KPIs?

ladder offers the right level of analysis to further the discussions regarding responsible and safe AI At this level of analysis we are talking about AI Systems (i.e. engineered systems) not about systems that use AI technology (Embedded AI). across the socio-technical system, not just the AI technology. This is where professional AI engineers are most helpful and needed.

Artificial Intelligence (AI) is on a trajectory to revolutionize various industries, from healthcare Ethical AI in Action One notable example of integrating ethics into AI development is the concept of explainable AI (XAI). XAI emphasizes transparency and interpretability in AI systems, ensuring that decisions made by AI models integrity into AI initiatives.