Why embedding AI ethics and principles in your organization is essential

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As technology advances, business leaders understand the need to adopt enterprise solutions leveraging artificial intelligence (AI). However, there is understandable hesitation due to the ethical implications of this technology – is AI inherently biased, racist or sexist? And what impact might that have on my business?

It is important to remember that AI systems are nothing in and of themselves. They are human-built tools that can maintain or amplify the biases that exist in the humans who develop them or those who create the data used to train and evaluate them. In other words, a perfect AI model is nothing more than a reflection of its users. As humans, we choose the data used in AI and do so despite our inherent biases.

Ultimately, we are all subject to a variety of sociological and cognitive biases. If we are aware of these biases and continually put measures in place to help combat them, we will continue to make progress in minimizing the damage these biases can cause when they become embedded in our systems.

Review Ethical AI Today

The organization's focus on AI ethics is twofold. The first is related to AI governance, which deals with what is allowed in the field of AI, from development, to adoption, to use.

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The second concerns research in AI ethics aimed at understanding the inherent characteristics of AI models resulting from certain development practices and their potential risks. We believe that lessons learned in this area will continue to become more nuanced. For example, current research is largely focused on base models, and in the next few years will turn to smaller downstream tasks that can either mitigate or propagate the drawbacks of these models.

The universal adoption of AI in all aspects of life will require us to reflect on its power, purpose and impact. This is done by focusing on the ethics of AI and requiring that AI be used ethically. Of course, the first step to achieving this is to agree on what it means to use and develop AI in an ethical way.

A step toward optimizing products for fair and inclusive outcomes is to have fair and inclusive training, development, and testing datasets. The challenge is that selecting high quality data is a non-trivial task. Obtaining these kinds of datasets can be difficult, especially for small startups, because a lot of readily available training data contains biases. Additionally, it is helpful to add debiasing techniques and automated model evaluation processes to the data augmentation process, and to start with thorough data documentation practices early on, so that developers have a clear idea of ​​what they need to augment the data sets they decide on. to use.

The cost of unbiased AI

Red flags exist everywhere, and technology leaders need to be open to seeing them. Since bias is to some extent unavoidable, it is important to consider the primary use case of a system: decision-making systems that can affect human lives (i.e. filtering automated resumes or predictive policing) have the potential to cause untold damage. In other words, the central objective of an AI model can itself be a red flag. Tech organizations should openly examine the purpose of an AI model to determine if that purpose is ethical.

In addition, it increases...