It’s Not About What AI Can Do for Us, But What We Can Do for AI

Most seeing artificial intelligence (AI) through a lens in one direction. There is only technology to serve humans and achieve new levels of efficiency, accuracy and productivity. But what if we lose half of the equation? What if, by doing this, we just underestimate technology defects?

Artificial intelligence in its cradle and still faces great restrictions in thinking, quality of data and understanding concepts such as trust, value and incentives. The gap between the current capabilities and the real “intelligence” is great. Good news? We can change this by becoming active collaborators instead of negative consumer of artificial intelligence.

Humans carry the key to smart development by providing better business frameworks, feeding quality data, and filling confidence gap. As a result, Man and Machine can work alongside victory-with better cooperation to generate better data and better results.

Let’s consider the form of a more crowning relationship, and how, as partners, calm cooperation can benefit from both sides of the artificial intelligence equation.

The required relationship between a man and a machine

Amnesty International is undoubtedly great in analyzing extensive data sets and automating complex tasks. However, technology is still primarily limited to thinking like us. First, these models and platforms are struggled with thinking that exceeds their training data. The identification of the patterns and statistical prediction does not constitute any problem, but the contextual rule and the logical frameworks that we consider is more empty of it is more challenging of repetition. This logical gap means that artificial intelligence often stumbles when it faces accurate scenarios or moral rule.

Second, there is the quality of the “garbage in, external garbage”. Current models are trained in wide groups of information with approval and without approval. Unlimited or biased information is used regardless of attribution or correct license, which leads to unspecified or biased AI. Therefore, a “diet for data” for models is doubtful at best and spreads in the worst case. It is useful to think about this food effect. If humans eat unwanted food, we are slow and slow. If the agents are only consumed by copyright and used materials, then their performance is impeded by the unprecedented and unreliable output in general and is not specific. This is still far from making independent and pre -emptive decisions in the wave coming from agents.

It is important, artificial intelligence is still blind to whom and what it interacts with. It cannot distinguish between users alignment and non -alignment, and is fighting to verify relationships, and a failure to understand concepts such as confidence, exchange of value, and stimulants of stakeholders – the basic elements that govern human interactions.

Amnesty International problems with human solutions

We need to think about artificial intelligence platforms, tools and agents as servants and more assistants who can help training. For beginners, let’s take a look at thinking. We can present new logical frameworks, moral guidelines, and strategic thinking that artificial intelligence systems cannot develop alone. Through the studied claim and careful supervision, we can complete the statistical strengths of Amnesty International with human wisdom – teach them to identify patterns and understand the contexts that make these patterns meaningful.

Likewise, instead of allowing Amnesty International to train any information that can get rid of the Internet, humans can raise high -quality data groups that are verified, varied and morally.

This means developing better support systems where content creators are identified and compensated for their training contributions.

The emerging frameworks make this possible. By uniting online identities under one banner and determining whether they are comfortable and shared, users can provide models with information on zero parties that respect privacy, approval and regulations. Better, by tracking this information on Blockchain, users and models can know where the information comes and compensates creators adequately for providing this “new oil”. This is how we acknowledge users of their data and bring them to the information revolution.

Finally, the confidence gap is bridging the armament forms of human values ​​and attitudes. This means designing mechanisms that recognize stakeholders, verify relationships, and distinguish between alignment and non -alignment users. As a result, we help artificial intelligence to understand its operating context – which benefits from its actions, what contributes to its development, and how the value flows through the systems in which it is participated.

For example, Blockchain infrastructure agents are good in this. They can learn about users and give priority to buying the ecosystem shown through reputation, social impact, or ownership of the distinctive symbol. This allows Amnesty International to align incentives by giving more weight to stakeholders with the skin in the game, which creates governance systems as supporters who have been verified in decision -making participate based on their participation level. As a result, artificial intelligence understands its deepest ecosystem and can make decisions aware of the relationships of real interest.

Do not lose sight of the human element in artificial intelligence

Much has been said about the rise of this technology and how it threatens to reform comprehensive industries and survey jobs. However, bread in handrails can ensure the strengthening of artificial intelligence rather than bypassing the human experience. For example, the most successful artificial intelligence applications do not replace humans but extend what we can accomplish together. When AI deals with routine analysis, humans provide creative guidance and moral supervision, both sides contribute to their unique strengths.

Upon properly finished this, artificial intelligence is considered to improve the quality and efficiency of human processes. But when making an error, it is limited to questionable data sources and simulates intelligence only instead of presenting actual intelligence. It is up to us, the human side of the equation, to make these models smarter and make sure that our values, our rule and our morals remain at their core.

Confidence is not negotiable for this technology to go to the main current. When users can check where their data go, know how to use it, and share the value they create, they become partners ready instead of hesitant topics. Likewise, when artificial intelligence systems can take advantage of the stakeholders alignment and transparent data pipelines, they become more worthy of confidence. On the other hand, they are likely to be able to reach the most important special and professional spaces, creating a cupboard better access to data and improving results.

Therefore, with the next stage of artificial intelligence, let’s focus on connecting a man and machine to verified relationships, quality data sources, and accurate systems. We should not ask what Amnesty International can do for us, but what we can do for artificial intelligence.