Despite increasing levels of automation enabled by AI, whether it’s determining what news and information we see or deciding upon what treatment we receive in hospitals, the commonality between these systems is always the human element. AI’s long-term success is dependent upon our acknowledgement that people are critical in its design, operation, and use.
Image by Ben Shneiderman Via Human-Centred AI
At its core, human-centred AI represents a new frontier of approaches to artificial intelligence where the technology is no longer designed to mimic human behaviour or solve problems in isolation, but rather AI systems are to be crafted with a deeper understanding of human emotions and social dynamics.
Human-centred AI isn’t about replacing humans with machines, instead it is focused on using machines to enhance the human experience. With human-centred AI, human input is kept at the centre of the design and build process. This approach takes advantage of the strengths of both humans and machines, enabling them to collaborate in a way that mutually reduces blind spots. Human-centred AI is created with people’s wellbeing in mind, focusing on technologies that will integrate seamlessly into our lives for the purpose of bettering our overall experience. It’s a means for bridging the gap between human and machine for the benefit of both.
This human-centred approach is what sets the THEMIS 5.0 ecosystem apart from other AI decision support tools. The main aim of THEMIS is to design an AI system that can be used to empower humans by supporting them to improve responsible behaviours, ensuring trustworthy AI and promoting human autonomy. To achieve this, THEMIS 5.0, will establish a mixed and sliding decision-making assessment process, enabling decision makers to benefit from AI decision support while maintaining the human involvement needed for trustworthy decision-making. That is, to allow for increased human-over-the-loop decision making for tasks and contexts identified through AI-based assessments as sufficiently robust and accurate, while identifying where human-in-the-loop and human in command is needed.
Human-in-the-loop goes hand in hand with human-centred AI. It means that humans are involved throughout the training, testing, and tuning process of building a Machine Learning model. For example, humans can label the training data used to help the model learn which features to recognise. Humans can also verify the accuracy of the model’s predictions and provide feedback to the model when it makes an error. This type of Machine Learning training ensures that humans are included as part of the continuous feedback loop that creates the model. It is this approach to AI creation that must be taken if AI systems are to remain accessible to users at all skill levels.
There are legitimate worries about the possible downsides of human-centered AI in addition to its obvious advantages. On one hand, human-centered AI has the potential to completely transform numerous aspects of our daily lives, including customer service and healthcare. However, there are serious risks that need to be taken into consideration. These include the possible loss of jobs to automation, sustained implementation of prejudices and discrimination which almost always exist in the data AI systems are trained on, and the degradation of privacy for individual users as a result of AI's need to access enormous volumes of personal data. Furthermore, if humans depend too much on AI to make decisions, we run the risk of losing our sense of autonomy and critical thinking. The situation is made more difficult by social manipulation, security concerns, and ethical disputes.
It’s all of our responsibility to further conversations in AI that will have a positive impact on our society. The dialogues we engage in can and do influence the priorities and actions of AI practitioners. We need to advocate for AI that’s equitable and provides a net benefit to all of the people who use it, keeping these end users in mind throughout the development process. It’s also important for companies to engage in knowledge sharing, when possible, which can bolster confidence in the human-centred approach. As AI technology advances quickly, it’s more critical than ever to have these discussions on how we collaborate with and use AI. Ultimately, we want to create a technology landscape where humans are enhanced, not replaced, by machines.