How to get artificial intelligence right

• Updated on: 2019-04-16
• Read original article here

Due to the ever-increasing ubiquity of artificial intelligence (AI) in our day-to-day lives, and businesses’ growing reliance on it for a range of tasks, the stakes have never been higher for IT to get AI right. The consequences of successful or unsuccessful implementation are potentially profound. Used incorrectly, AI is capable of reinforcing harmful biases and increasing polarisation, as well as having many other damaging ramifications.

With the appetite for AI growing ever stronger and the hype surrounding its possible applications, it can be tempting to focus on the technology and coding disciplines — what one might call the “artificial” aspects. Nevertheless, the “intelligent” aspects of a digitally connected world cannot exist — let alone function — without data. Although the majority of executives and business and IT professionals are familiar with the people, process and technology capabilities of business models, they do not “speak data” fluently. Yet data is one of the cornerstones of any AI process.

In order to use AI accurately, organisations must advocate for data literacy becoming a new core competency for creators and consumers of AI alike. CIOs responsible for enabling AI initiatives are well-advised to follow a three-step approach: build AI right, use AI right, and, finally, keep AI right.

Before attempting to “build AI right”, it is imperative that those involved first establish the basic vocabulary of AI — a technical dialect used by people who “speak data”. CIOs should at least determine the primary terms employed to describe an AI system or solution, the reason the solution is being developed, and other key terms in relation to the different types of data that are used in and gathered from the solution.

Along with models and algorithms, data is foundational in any AI process. AI consumes and produces data. AI data design requires an understanding of and confidence in processing the datasets that will be parsed by the AI algorithm. CIOs and data and analytics leaders will be responsible for establishing and maintaining the data management aspects of AI. To succeed, developing data management expertise across the entire process is crucial.

Information language barriers can exist locally or systemically, regardless of the scope of a program or maturity of an organisation. Addressing a barrier demands a shift in mindset, as well as deliberate acknowledgement and intervention to “course correct”. While some may already appreciate the foundational role played by data in business transformation, the majority will not. Therefore, skilled leadership and purposeful change management discipline are required, beginning with the recognition of information as the new vernacular of the digital revolution.

Data literacy is the ability to read, write and communicate data in context. It includes an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, application and resulting value. It is an underlying component of digital dexterity, which is an employee’s ability and desire to utilise existing and emerging technology to drive better business outcomes.

As organisations become more data driven, poor data literacy will become an inhibitor to growth. In order to hone data literacy across an organisation, CIOs should develop a data literacy program, implementing the following steps:

With all AI solutions, the concepts of “good AI” and “bad AI” arise. However, there is no singular or comprehensive definition of the terms. Choices made by different parties can have significant implications. Even the most successful companies should avoid falling into a false sense of security by believing they are immune to ethical mishaps implicated in AI. Extensive, dedicated discussions are necessary to discern different types of ethical questions and dilemmas that a company can be faced with versus the actual ethical position that it can take. CIOs should apply digital ethics and digital connectivism to guide and make explicit the business decisions and choices where AI is to be adopted.