From smart products to smart systems

Prasobh V Nair on January 19, 2019

Today, the business aspirations for AI (Artificial Intelligence happen to be higher than they ever were. Countless companies are working hard to incorporate artificial intelligence technology into their products. Applications such as treatment of medical issues, safety and wellness, and automated vehicles have not gone unnoticed.

However, there are a number of issues that need to be looked into, in order to design innovative artificial intelligence solutions. Gathering useful and accurate data, for instance, is one of the issues. In strategic terms, it requires the designers to design artificial intelligence systems that are in alignment with the user workflows.

For an artificial intelligence system to become successful, it is important for it to fit into the environment that which they support. While artificial intelligence is considered smarter than humans, in reality, it is smart in a rather narrow sense.

It is true that artificial intelligence solutions are capable of outperforming human intelligence in an array of tasks. However, it is certainly not necessary that these systems will fit into our environment and work along well with the humans. As an example, it is possible to train an algorithm to identify animal breeds better than humans can. However, it is also very easy to trick these systems into thinking that a small pup is a household object. Again, this is because these artificial intelligence algorithms are smart but in a very narrow sense and hence, they do not possess what we humans refer to as common sense.

In order to effect this issue which lies at the heart of artificial intelligence algorithms, organizations turn to an HCD (Human-centered design) in order to increase the effectiveness of the artificial intelligence systems. As a result of this, artificial intelligence programs become usable and said otherwise, more intuitive.

With an increase in the innovation being brought in artificial intelligence, it is getting difficult to recognize the big number of potential hurdles that are present in machine-human interaction.

In this regard, a method called participatory design allows stakeholders to design complex artificial intelligence systems. This is a discovery method, which revolves around the design strategy rather than the design principles. The prime benefits of this methodology include:

A better understanding of the problems that have to be addressed

Often at times, issues with technology can be revealed only when stakeholders are approached upfront. For instance, technologies that assist visually impaired individuals in regular tasks are quite beneficial indeed. However, they also impede their social interaction as the individuals frequently rely on the devices for relatively mundane tasks. As a result of this, while these devices solve one problem, they create another which is social isolation.

Cultivating greater trust

If users are made a part of the design process itself, they will be able to get hands-on experience of how the technology works. As a result of this, they will have a greater level of trust in the technology.

Alignment of Artificial Intelligence solutions for long-term needs of the community

Often at times, the problem-solving process and problem understanding process goes hand in hand and occurs concurrently. Hence, there is no end to the redesign and identification of problems. Hence, artificial intelligence solutions should be aligned with the long-term needs of a community and should be redesigned to cover up the identified issues.

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