How to Get Artificial Intelligence ‘Right’:

 

Given the hype and excitement around artificial intelligence (AI), it is quite easy to focus only on the technology and coding aspects, what could be considered the ‘artificial’ aspects. However, its ‘intelligent’ aspects cannot exist without data, the cornerstone for all AI processes.

 

Unfortunately, most executives and IT professionals who use AI, while experts in people processes and technology, lack the skills required to use data effectively.

 

This missing link can result in AI being used incorrectly, and that can unintentionally reinforce biases, increase polarization and lead to other damaging consequences.

CIOs must create and foster a data-literate society

To be able to use AI accurately, businesses must first incorporate data literacy as a new core competency for both creators and consumers of AI. CIOs responsible for enabling AI initiatives must follow these three steps:

Build AI right

First establish the basic vocabulary of AI, the way people ‘speak data’. CIOs must be aware of the basic terms that are used to describe the AI system or solution being developed. Knowing the reason the AI solution is being developed for is not enough; they must know key terms, such as the types of data used and gathered from the solution.

Use AI right

Once the information language barrier is detected, it requires deliberate acknowledgment and intervention to course correct. CIOs should develop a data literacy program by:

 

  • Identifying fluent and native speakers who speak data naturally and effortlessly.
  • Identifying skilled translators.
  • Identifying areas where communication barriers are inhibiting the effectiveness of data and analytics initiatives.
  • Actively listening for business outcomes not clearly articulated in terms of explicit action.
  • Identifying key stakeholders requiring specialized translations.
  • Identifying and maintaining a list of words and phrases..

Keep AI right

No company can afford to think it is immune to ethical mishaps. By indulging in extensive discussions a business can distinguish between various ethical questions and dilemmas it can face versus the actual ethical position to take. Here’s how it can be done:

 

  • Step back and absorb digital ethics and digital connectivism for the improvement of digital business.
  • Look for ethical case studies relating to the use of data in AI.
  • Use AI algorithms and data exchange as an enabler of digital interactions, and a way to enable stakeholders to participate in an ecosystem.

 

If you wish to know more of how to ensure the right implementation of AI in your business, please join us at the Gartner Data & Analytics Summit 2019, 10 - 11 June, Mumbai.

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