Conditioned Language Models: Why Unsanitise?

Artificial Intelligence has really reached the highest level in numerous aspects, but its applications sometimes lack the human insight that can make all the difference. With the infusion of human biases, what would change in outcomes and make them more relevant and impactful?

Existing Alignment Practices

Alignment practices in AI ensure that models remain within the frame of ethical and socially acceptable operating parameters. Under this context, the aim is to train AI not to get into harmful biases but rather to be fair and stick to legal and moral standards. Techniques such as reinforcement learning from human feedback and adversarial testing make it possible to refine AI behaviours into more reliable and trustworthy ones. In such a case, approaches become extremely important in preventing AI from making biased or even discriminating decisions.

The argument is that alignment while ensuring safety and ethical AI operations by reducing risks of unintentional harm, so does not output harmful stereotypes or biased decisions in critical areas, such as job hiring or money lending. On the other hand, excessive alignment can strip AI of its creative and adaptive potential. Too much cleaning and sanitisation would put limits on a model from providing different perspectives or new solutions, making it overly conservative, and therefore less useful in dynamic environments.

Embracing the Human Element

Current AI training methods strive to find a way to be neutral and often attempt to make the data sanitised to remove bias. But in most contexts, a touch of human bias is not only beneficial but critical. Think about an AI system for medicine trained solely on clinical data. It would likely answer fine, but without compassion and understanding, which are so critically needed by patients. Conditioning models with human insights can bridge this gap, enabling AI to offer not just answers but compassionate solutions.

Think of it this way: It's not just its understanding of the legal texts but also the benefit from conditioning in a legal setup, which would, for example, factor in situational opinions about how various judicial philosophies interpret the very same law. Such a training regime can then make AI outputs sensitive and contextually grounded. Reflecting real-world messes, these models can thus produce not just correct but also deeply valuable content.

By "unsanitising" AI, we advocate for the presence of good biases to reflect domain realities. This way, a model that is taught about political biases is one that can thus generate more balanced news articles, reflective of a number of viewpoints, for journalism. In light of this, it is not only increasing the misrepresentation of facts but also improving the ability of the model to capture and represent different views, thereby making a readership that is much more informed and engaged.

Ascertaining human bias can brainstorm more applicable ideas and solutions, suitably well-fitted to concrete needs. In the creative industries, a model trained on biases towards some specific style in art will generate innovative works that resonate with a concrete audience. Such targeted creativity can showcase AI's potential to go beyond mere replication of human effort and become a genuine collaborator in the creation process.

Training for the Right Balance

Achieving the right balance in situational conditioning involves a nuanced approach. Here are some strategies:

• Contextual Training: infuse domain-specific knowledge and human insights to real-world complications. For example, in healthcare, this would translate to infusing clinical data with empathetic communicative practices in interactions with a patient.
  

• Dynamic Feedback Loops: utilise the ongoing user-provided feedback to modulate the model's behaviour. This helps in the fine-tuning of the AI to accordingly respond to the dynamic situations and diverse user needs.
  

• Ethical Frameworks: ethical standards that can offer flexibility without compromising the core moral principles of the AI. This is to ensure adaptability yet compliance with ethical standards.
  

• Diverse Data Sources: train the models on a wide variety of data sources to allow for different perspectives and mitigate echo chambers. This may result in more balanced and informed AI outputs.
  

With close attention and conditioning to AI with situational awareness and human bias, maybe we can form more effective models yet aligned to human values and societal needs. Such a balanced approach could help make AI into a powerful tool that can enhance our capabilities while being ethical and relevant.

While "alignment" is a standard practice, intentionally incorporating purposeful bias offers a unique and valuable perspective. This approach ensures that AI makes empathetic, context-aware, and relevant decisions, mirroring human complexity. It's not about augmenting existing methods, but about humanizing technology to align closer with human experience. At Artificial Cognition Research & Development, we are excited about the potential this paradigm holds. As we push the boundaries of AI, we remain committed to exploring how it can better serve humanity by embodying the essence of human nature.