I’m not sure this question can be resolved. After all, how do you explain to an AI what our goals are, even with a computational language. How are you going to be sure that the AI, trying to maximize happiness, isn’t going to be doing ridiculous things? Say you train the AI to maximize happiness, so the AI is chasing people on the street with drones having an injection of dopamine so people experience non stop pleasure and don’t do anything else? Or maybe the AI, trying never to hurt anybody, is not saving people because that would mean to inject something in the skin and that causes pain and the AI avoids human pain at all costs so the AI lets people die when it could have saved them?

There’s seemingly an infinite number of potentially ridiculous situations that we won’t take into account simply because we use common sense to solve them, but the AI doesn’t have any common sense. Maybe we could instill some common sense by training it correctly, I don’t know, but I am doubtful.

I’m not sure this question can be resolved. After all, how do you explain to an AI what our goals are, even with a computational language. How are you going to be sure that the AI, trying to maximize happiness, isn’t going to be doing ridiculous things? Say you train the AI to maximize happiness, so the AI is chasing people on the street with drones having an injection of dopamine so people experience non stop pleasure and don’t do anything else? Or maybe the AI, trying never to hurt anybody, is not saving people because that would mean to inject something in the skin and that causes pain and the AI avoids human pain at all costs so the AI lets people die when it could have saved them?

There’s seemingly an infinite number of potentially ridiculous situations that we won’t take into account simply because we use common sense to solve them, but the AI doesn’t have any common sense. Maybe we could instill some common sense by training it correctly, I don’t know, but I am doubtful.

E.g. algorithmic bias and algorithmic access

Idk

Defining the relationship of the Ai with respect to Humans – Call it ethics or morals.

Some ethical things are choice of language controlling AI & availability and access because this is a real event happening now.

relationship between AI and human and animal etc , also with all the Code of Ethics and Code of Conduct

When it comes to language, because it is designed by the people of the time, it will inevitably involve ethics.

As long it follows the normal norm of respecting Human Rights, laws, and regulations.

Designing AI systems for humans involves a range of ethical and equity considerations to make sure that these technologies are developed and deployed in a responsible and fair manner. Here are some key considerations:
Bias and Fairness: AI systems can inherit biases from their training data, which can result in unfair or discriminatory outcomes. It’s crucial to identify and mitigate biases to ensure that the system’s decisions do not disproportionately favor or harm specific groups based on factors like race, gender, or socioeconomic status.
Transparency and Explainability: AI systems should provide understandable explanations for their decisions and actions. It is particularly important when these systems impact people’s lives, such as in healthcare or legal domains. Users should have a clear understanding of why a certain decision was made.
Inclusivity and Diversity: The teams developing AI systems should be diverse and inclusive to ensure a variety of perspectives and experiences are considered. It can help in avoiding unintentional biases and in creating technologies that are suitable for a wider range of users.
Data Privacy: Responsibly handling personal data is essential. AI systems frequently require access to user data, and protecting this data from unauthorized access, misuse, and breaches is a critical ethical consideration.
Human Autonomy: AI should be designed to enhance human capabilities and autonomy rather than replace or diminish human decision-making. People should have the final say and control over important decisions influenced by AI systems.
Accountability and Responsibility: Clear lines of accountability should be established for the outcomes of AI systems. If something goes wrong, there should be a mechanism to determine who is responsible and how to rectify the situation.
Beneficence and Harm Mitigation: AI systems should strive to maximize benefits while minimizing harm. It involves anticipating potential negative consequences and taking proactive measures to mitigate them.
Equitable Access: Access to AI technologies should not be limited by factors like income, geography, or disability. Efforts should be made to ensure that the benefits of AI are accessible to everyone.
Collaboration with Stakeholders: Involving stakeholders, including the communities that may be affected by the AI system, can help in understanding their needs, concerns, and values and in co-designing solutions that align with societal values.
Continuous Monitoring and Iteration: AI systems should be continuously monitored after deployment to identify any emerging biases, errors, or unintended consequences. Regular updates and improvements should be made to address these issues.
Long-Term Impact: Consideration should be given to the potential long-term societal impacts of AI systems. It involves thinking beyond immediate use cases to anticipate how these technologies might shape social, economic, and cultural landscapes.
Regulation and Policy: Collaboration between AI developers, ethicists, policymakers, and regulatory bodies is essential to establish guidelines and laws that ensure the responsible development and deployment of AI systems.
Public Discourse: Engaging the public in discussions about AI’s impact can help raise awareness, gather diverse perspectives, and influence the direction of AI development in ways that align with societal values.
Balancing these considerations is a complex task, and it requires a multi-disciplinary approach involving not only technical experts but also ethicists, social scientists, policymakers, and representatives from the communities that the AI systems will influence.

I would highly recommend people to listen to Roger Penrose’s idea about why consciousness is not a computation.
He talks about that we still don’t understand quantum mechanics and it has something to do with consciousness. To understand consciousness we need to have a deeper theory of quantum mechanics.

I think it’s going to be a big question of the science of the future, understanding consciousness and computation and fundamental physics.
What our Minds do, is it a computation or something else.
How fundamental is computation and is the principle of computational equivalence true or not, that is the key to the universe.

I think AI is just like nuclear power, human use it as nuclear bomb to destroy, but also use it as power full energy to make better life for everybody.

Developing AI systems for human use entails a multitude of ethical and equity considerations to ensure responsible and equitable development and deployment. Here are some key areas of focus:

Bias and Fairness: AI systems can inherit biases from their training data, potentially leading to unfair or discriminatory outcomes. Identifying and mitigating biases is crucial to prevent decisions that unfairly favor or harm specific groups based on factors like race, gender, or socioeconomic status.

Transparency and Explainability: AI systems must offer understandable explanations for their decisions, especially in domains like healthcare or law where their actions impact people’s lives. Users should have clear insights into the reasoning behind AI-driven decisions.

Inclusivity and Diversity: Ensuring diversity and inclusivity within AI development teams helps incorporate a wide range of perspectives and experiences, reducing the risk of unintentional biases and ensuring technologies are suitable for diverse users.

Data Privacy: Responsibly handling personal data is paramount. AI systems often require access to user data, necessitating robust measures to safeguard against unauthorized access, misuse, and breaches.

Human Autonomy: AI should augment human capabilities and decision-making rather than supplanting them. Maintaining human control and autonomy over important decisions influenced by AI is essential.

Accountability and Responsibility: Establishing clear lines of accountability for AI system outcomes is crucial. Mechanisms should be in place to identify responsible parties in case of errors or adverse events, along with processes for rectification.

Beneficence and Harm Mitigation: AI systems should aim to maximize benefits while minimizing harm. This entails anticipating and mitigating potential negative consequences through proactive measures.

Equitable Access: Access to AI technologies should not be restricted by factors like income, location, or disability. Efforts should be made to ensure that the benefits of AI are accessible to all members of society.

Collaboration with Stakeholders: Involving stakeholders, including affected communities, in the development process can help understand their needs, concerns, and values, leading to the co-design of solutions aligned with societal values.

Continuous Monitoring and Iteration: Ongoing monitoring of deployed AI systems is essential to detect emerging biases, errors, or unintended consequences. Regular updates and improvements should be implemented to address these issues.

Long-Term Impact: Consideration should be given to the broader societal impacts of AI systems beyond immediate use cases. Anticipating how these technologies may shape social, economic, and cultural landscapes is crucial.

Regulation and Policy: Collaboration among AI developers, ethicists, policymakers, and regulatory bodies is necessary to establish guidelines and laws ensuring the responsible development and deployment of AI systems.

Public Discourse: Engaging the public in discussions about AI’s impact can raise awareness, gather diverse perspectives, and shape the direction of AI development in alignment with societal values.

Balancing these considerations requires a multidisciplinary approach involving technical experts, ethicists, social scientists, policymakers, and representatives from affected communities.