Do you know that organizations have started paying attention to whether AI/machine learning (ML) models are doing unbiased, safe and trustable predictions based on ethical principles? Have you thought through consequences if AI/machine learning (ML) models you created for your clients make predictions which are biased towards a class of customer, thus, hurting other customers? Have you imagined scenarios in which customers blame your organization of benefitting a section of customers (preferably their competitors), thus, filing a case against your organization and bring bad names and loss to your business? Have you imagined the scenarios when ML models start making incorrect predictions which could result in loss of business?
If above have not started haunting you, its time that you started thinking about some of the above. And, this is where you need to think about implementing the code of ethics in implementing artificial intelligence (AI) practices/principles. This post represents the definition of ethical AI, key traits and why businesses need to start paying attention to rolling out ethical AI practices in their organization. The following topics would be discussed:
The meaning of the word, ethics, is moral principles that govern a person’s behavior or the conducting of an activity. In other words, ethics means a set of generalized rules for being a good person. When applied to an organization, an organization doing business based on ethics is governed by a set of principles based on which the organization conducts one or more business activities based on good principles such as honesty, integrity etc.
When ethics get applied to artificial intelligence (AI), it represents the fact that AI/machine learning models make predictions which are trustable/explainable (and hence transparent), unbiased/fair (towards all class of users or entities) and safe (not hurting businesses). Unethical AI would mean that models are biased towards a specific class of users (and, thus, unfair towards other users), or, intended to harm a specific class of user entity (and, thus, unsafe).
The following are some of the key traits of implementing ethical AI in your organization:
Here are the details on above.
One may argue the need to intentionally create biased models. Or, one may ask whether any form of bias is bad. Here are some thoughts in relation to the different form of bias or need to create biased models:
The following are some of the reasons why your organization should/must consider implementing ethical AI practices/principles:
Who all needs to be involved with ethical AI implementation is the key question? The following could be some of the stakeholders in ethical AI implementation:
In this post, you learned about implementing the code of ethics for artificial intelligence (AI) / machine learning models. It is important to set ethical AI principles to ensure AI/ML models are making safe, unbiased and trustable predictions impacting the end customers and partners. Failing to do so would result in loss of business, health, and impact society in a negative manner.
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