People have been searching for content online since the time the internet has been made popular. With the advent of search engines, back in the 1990s, the people-centric design ambition was popular, and users interact with search engines in a way they would interact with each other, without the existence of any keywords or stating problems or requirements. Soon this method was lost its dominance as it was not that popular and yielded poor results, this encouraged us to shift to the keyword-based search patterns which are widely popular today.
Data design has seen vital evolutions over the ages. With the latest being the citizen AI where fresh graduates from college and technology aficionados develop talent from within and add to the homegrown AI talent that comes from non-technology firms. With citizen data scientist, many users have been helping to bring the user-centric thinking into AI design? In the coming years, AI will become more accessible to those outside of major tech companies through the shift from recommender systems to reinforcement learning.
The citizen data scientists think about AI with their own expertise since they know the domain and are used to understanding the nitty-gritty in technology. Back in the early days, feedbacks helped people on the business side to simplify AI for use.
What the current generations of data scientists cannot escape is the all-pervasive automation of ML-powered business systems, where many laborious human tasks will be routinely conducted by tools or bots. So far as data scientists are concerned, that is good news, because human minds will be left free to pursue the complex problem-solving issues.
Self Service Data and Human-Centric Data Design
Self-service, by definition, implies free-form exploration of data, which a highly flexible and effective governance framework may be capable of handling. Data scientists of the future have their use and requirements here. They will initiate the ordinary business users into self-service through formal “onboarding” programs.
A human-centric approach to artificial intelligence solutions leverages human science, qualitative thick data, to understand the deeper needs, aspirations and drivers that underlie customer behaviours in your market. When analytics are applied to human behaviours and choices, patterns appear. These contextual analytics combine data and human science to produce dramatically improved, personalized customer experiences. At the core of human-centric AI, lies the recognition that AI systems solve problems deploying machine learning a concept which was fundamentally alien to humans who did not have a training in computer science or AI.
Clear, informed business strategies can be developed when companies know exactly what their customers do and expect. There was a feeling that the AI development had reached a zenith and needed a complete change in approach. Now AI developers are increasingly waking up to the concept that technology has to be developed for the end users rather than focussing on data scientists alone.
Artificial intelligence, while a great paradigm-shifter in the world of business, is still one of the tools that will be used by humans for making better decisions. At the crux, AI will always be developed by technology specialists to be used by the users who may or may not be technically skilled. And maintaining the ‘human element’ in the way it is made, delivered, used and improved will most certainly make it a lot more successful.
The Macro Goal
AI and allied technologies exist to make human life simpler and richer. Thus, it is important that AI practitioners and data scientists collaborate on a human-centric approach towards its development, deployment and adoption. Even the best into AI technology will become quickly redundant if efficient and tactical inputs are not being assimilated by real humans on how to accelerate strategic processes and decisions.
The human-centric AI recognizes the importance of humans in the environment where the AI systems and autonomous robotics will be deployed in the near future. To make these technologies vital of user interest impacting the lives and societies, AI must appear less alien and more friendly. This does not mean that AI systems should be thinking and acting like humans or learn like humans. But the underlying hypothesis is that they must make themselves accessible and communicate on a level that humans are comfortable and familiar with. As of now, we humans are hard-wired to be effective communicators and collaborators with other humans.
In a crux, instead of making humans learn how AI systems work and reason, human-centric AI should aim to make human beings understand how humans communicate and collaborate for the greater organisational good in the long run.