Artificial intelligence is growing rapidly, and this is evident daily. Therefore, building trust and guaranteeing the reliability of AI technologies is necessary. The more people understand the functionality and reliability of AI technologies, the more trust will result in increased trust. Reliable AI As we become more dependent on smart devices, unexpected downtime can significantly…
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ML models should be tested and optimized to ensure the model accurately fits the data or if the model is overfitting/underfitting the data. If the model begins to drift, then errors can occur. Therefore, ML models should be monitored to guarantee error-free measurements. Before Training Models Before starting a project, developers should thoroughly investigate the…
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Methods have been proposed to measure an AI system’s green or red. (Learn more about Red AI and Green AI here). For example, it can be measured by parameters related to the system itself and by parameters connected to the natural resources required to run them. These parameters include the training cost, data size, and…
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Every year, a new AI scandal emerges to shock the public. In 2016, the country learned that some law-enforcement algorithms routinely recommended incarcerating members of minority groups. Google discovered in 2017 that its Min smart speakers were secretly spying on their owners by turning them on at random, recording audio from their homes, and transmitting…
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It is necessary to explore definitions of AI to acquire a fuller understanding of the field. AI uses machines, computers, or algorithms to simulate human cognitive processes. Most of the technology that is referred to as AI is, in fact, machine learning. Machine Learning Machine learning (ML) is a process by which systems propagate information,…
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As we previously examined the abstract and importance of transparency, we now delve deeper into the concept of interpretable AI. Interpretability plays a crucial role in the transparency of your ML (AI). What is interpretability – we must first start with three important definitions ML managers and developers must understand and apply. Interpretability Concepts Scrutability…
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For many reasons, transparency is potentially one of the most transformative dynamics of Artificial Intelligence. Explaining your AI technology and capabilities builds trust and accountability between you and your users in the models created. As we delve into transparency, AI programmers and managers come to understand the processes that are carried out by ML algorithms…
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Fairness metrics tell how accurately machine learning algorithms predict outcomes. They identify and keep track of true and false positives and negatives. For equal odds, one must equalize false positives and false negatives to increase fairness. It’s important to know that despite efforts to define fairness, no complete definition of it has been developed. In…
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Over the decades, mathematicians and statisticians have attempted to characterize fairness with myriad definitions. At least 21 definitions attempt to capture various ways biases can manifest — and ways to rectify them. What’s important in AI is that these biases can affect data and the algorithmic processes that learn from them. In pursuit of Ethical AI, let’s take a look at six of the most…
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An unfortunate byproduct of machine learning and deep learning is the high computational costs that have an adverse environmental impact. We call these wasteful AI systems red AI. But there is a positive alternative – green AI, which has been spearheaded by conscientious programmers who aim to reduce the computational expense resulting from deep learning algorithms. How Did…
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