Artificial Intelligence: a modern-day Pandora's box. A realm of rapidly evolving technological advancement filled with tantalizing opportunities, yet fraught with ethical quagmires. As much as AI has revolutionized our world, it's also brought forth a flurry of ethical concerns.

A technology-neutral ethics, considering both individual and collective implications, is the order of the day. And believe it or not, programmers are the new knights of ethical rectitude, ensuring AI systems align with values and principles. In other words, coders are our modern day, caffeine-fueled Gandalfs, defending Middle-Earth from a barrage of rogue algorithms.

Building AI systems isn't a one-man show. It's a performance requiring an ensemble of diverse stakeholders, transparency, and a dash of theatrical flair. Such an approach promotes trust, accountability, and helps tackle biases that can sneak onto the stage like unwelcome extras.

Foundation models in AI are like those fancy multi-tools. They perform complex tasks but also come with a risk tag. An ethical framework, therefore, is a safety manual that forms the bedrock of responsible AI, ensuring these models don't turn into uncontrollable Swiss Army knives.

Responsible AI's holy trinity: accountability, responsibility, and transparency. Getting them right requires an ethical framework that prioritizes proportionality, avoidance of harm, human rights, safety, fairness, non-discrimination, and privacy. Think of them as the seven dwarfs of AI ethics.

Just as the puppeteer controls the puppet, human oversight and control are key elements of ethical AI governance. Ensuring there's a diligent human at the helm can prevent our AI creations from doing a Pinocchio and going rouge.

Model Cards are the report cards of AI, providing transparent information about AI and machine learning models. They are a step towards responsible AI development and accountability — a way of keeping our AI offspring on the straight and narrow.

Protecting data and privacy in the realm of AI is akin to safeguarding a delicious cheese from a very persistent, technologically-advanced mouse. Advanced techniques like Privacy-Enhancing Technologies (PETs) are our mousetraps, ensuring data processing remains anonymous.

AI ethics is a Rubik's Cube of dilemmas. Maximizing dimensions, measuring fairness at group or individual levels, and avoiding ethical traps are challenges we face. As we continue to twist and turn our way through these issues, we must continuously revisit the ethicality of our models, how machines can learn ethical principles, and define what constitutes ethical behavior.

Bias in AI is like a bad party guest. It spoils the fun and leaves a mess. To mitigate this, we need diverse data, bias detection tools, inclusive teams, and ethical impact assessments. Plus, transparency and fairness play a crucial role in future-proofing businesses incorporating AI.

In conclusion, AI ethics is not a one-size-fits-all solution. It's a complex, ever-evolving challenge requiring continual dialogue and consensus-building. Like Indiana Jones searching for the Holy Grail, we must navigate this landscape thoughtfully, armed with our ethical compass to ensure the effective, fair, and inclusive use of AI. Let's remember: AI should augment human intelligence, not replace it. This tale is ours to write, and the quill needs to be guided by shared ethics for humanity at its heart.