Tag Archives: ethical ai

Artificial Intelligence (AI) for Telemedicine: Use cases, Challenges

In this post, you will learn about different artificial intelligence (AI) use cases of Telemedicine / Telehealth including some of key implementation challenges pertaining to AI / machine learning. In case you are working in the field of data science / machine learning, you may want to go through some of the challenges, primarily AI related, which is thrown in Telemedicine domain due to upsurge in need of reliable Telemedicine services. What is Telemedicine? Telemedicine is the remote delivery of healthcare services, using digital communication technologies. It has the potential to improve access to healthcare, especially in remote or underserved communities. It can be used for a variety of purposes, including …

Continue reading

Posted in AI, Data Science, Healthcare, Machine Learning, Telemedicine. Tagged with , , , , , .

Ethical AI Principles – IBM, Google, Intel, Microsoft

microsoft ethical ai principles

In this post, you will get a quick glimpse of ethical AI principles of companies such as IBM, Intel, Google, and Microsoft. The following represents the ethical AI principles of companies mentioned above: IBM Ethical AI Principles: The following represents six ethical AI principles of IBM: Accountability: AI designers and developers are responsible for considering AI design, development, decision processes, and outcomes. Value alignment: AI should be designed to align with the norms and values of your user group in mind. Explainability: AI should be designed for humans to easily perceive, detect, and understand its decision process, and the predictions/recommendations. This is also, at times, referred to as interpretability of AI. Simply …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , , .

Machine Learning Models – Bias Mitigation Strategies

Machine learning models - Bias mitigation strategies

In this post, you will learn about some of the bias mitigation strategies which could be applied in ML Model Development lifecycle (MDLC) to achieve discrimination-aware machine learning models. The primary objective is to achieve a higher accuracy model while ensuring that the models are lesser discriminant in relation to sensitive/protected attributes. In simple words, the output of the classifier should not correlate with protected or sensitive attributes. Building such ML models becomes the multi-objective optimization problem. The quality of the classifier is measured by its accuracy and the discrimination it makes on the basis of sensitive attributes; the more accurate, the better, and the less discriminant (based on sensitive attributes), the better. The following are some of …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , .