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Six ways to avoid date errors
AI rises in all areas of business and in our daily lives. Today, many companies rely on intelligent applications to provide the understanding needed to make decisions that can affect people’s lives, such as who is eligible for a mortgage or who will be insured.
Below are six best practices to avoid data errors in building an artificial intelligence solution.
Hire a varied team
As people from different origins and cultures have different perspectives, sensitivities and ways of thinking, it is important to have a team of data researchers who take this variety into account.
Training on diversity and inclusiveness
Even with a diverse team and better intentions, people can use unconscious prejudices and assumptions in the algorithms they develop. Today, data scientists, data engineers, and other team members need to know more than just technical skills – they need to think about ethics and potential prejudices.
Be transparent
It is very important that companies are transparent about the algorithms that affect people’s lives. They should disclose the type of data used to teach the algorithm and the criteria that helped make the decision. These companies should then carefully consider the feedback they receive from the public and adjust their algorithms accordingly.
Hiring technical linguists to develop appropriate dialogues
Developing conversation AI applications for chat bots adds another layer of responsibility for organizations. Companies must ensure that they communicate with their customers appropriately and with respect.
Develop chat talks in the language in which it will be used
Chatbot must understand cultural norms, idioms and dialects as well as accents.
Continuous testing and monitoring of the algorithm
It is very important to constantly monitor your algorithm, not only to improve the results, but also to make sure that new data does not bring new bias to the software. For chat bots, analyze and report what questions they could not answer and request ongoing feedback and recommendations from users.
As AI becomes more common in organizations and in our daily lives, data professionals need to be vigilant and free of bias. In addition to being unfair and unethical, bias can result in the organization being unable to comply and being subject to fines and penalties.