Evaluating CAM research
Authors: Gauthier Salavert, Chiara Semenzin (PhD)
By placing emphasis on process supervision, we not only gain valuable insights into the inner workings of our AI system but also ensure ethical practices, transparency, and accountability throughout the design and deployment stages.
By focusing on process supervision, we are working everyday towards creating an accountable healthcare-oriented AI system that is making a positive impact and benefiting society as a whole.
The black-box pitfall
In the development of AI-based technologies, it is crucial to strike a balance between focusing solely on the outcome and supervising the underlying process.
Through our daily observations during technology development, we have come to realize that outcomes, although measurable indicators of success, can be influenced by uncontrollable factors, limiting our understanding of the underlying processes and rendering them non-transparent.
All this can sometimes make the most powerful of technologies nothing but a “black-box”.
By placing emphasis on process supervision, we not only gain valuable insights into the inner workings of our AI system but also ensure ethical practices, transparency, and accountability throughout the design and deployment stages.
Managing processes to track errors
What we prioritize in our approach to developing new technology is identifying and correcting errors and those unexpected outcomes that can throw us off track.
We have seen firsthand how complex AI systems can be and how they can sometimes generate results that catch us by surprise. This becomes especially concerning in the realm of health-centered technology, like Unali.
These errors can stem from a range of factors, whether it is limitations in the available data, algorithmic shortcomings, or the dynamic nature of the real world that is sometimes so complex its artificial representation may not be able to keep up with.
In health-based platforms, as much as in other applications, regardless of the cause, inaccurate information can lead to incorrect decisions and foster a false sense of knowledge. That's why closely monitoring the process is so crucial to us.
Through process supervision, we can swiftly identify and rectify errors, refine our algorithms, and enhance the overall quality and reliability of our AI-based system. It serves as a safeguard against misleading outcomes and ensures that our technology operates with precision and accuracy.
Managing processes for greater transparency
But process supervision goes beyond error correction. It also plays a pivotal role in transparency and building trust with users.
In an era where AI technologies have a significant impact on our health and well-being, understanding the inner workings of AI decision-making is more important than ever. Users, stakeholders, and regulatory bodies demand transparency to comprehend how decisions are made, which data is utilized, and the potential implications of AI-based technologies.
By prioritizing process supervision, we aim to provide explanations and justifications for our system's behavior, building trust and confidence among our users.
Additionally, process supervision addresses the challenges of accountability and responsibility. Developers need to establish clear lines of accountability, ensuring that decision-making processes are auditable and traceable. It is all about being responsible and ensuring that our system's actions can be accounted for.
In conclusion, while outcomes serve as tangible measures of success, process supervision is of paramount importance to us when it comes to integrating AI in our recommendation system. By focusing on process supervision, we are working everyday towards creating a healthcare-oriented AI system that not only delivers desirable outcomes but also operates ethically, transparently, and reliably. It's all about making a positive impact and benefiting society as a whole.
