Dominant Algorithms to Evaluate Artificial Intelligence: From the view of Throughput Model is an informative reference for all professionals and scholars who are working on AI projects to solve a range of business and technical problems. The six AI algorithmic pathways represent. As AI is increasingly employed for applications where decisions require explanations, the Throughput Model offers business professionals the means to look under the hood of AI and comprehend how those decisions are attained by organizations. Finally, The Throughput Model provides the first steps towards building architectures that combine the strengths of the symbolic approaches that can be adapted for machine learning/ deep learning, and to develop better techniques for extracting and generalizing abstract knowledge from large, often noisy data sets. As AI is employed more and more for applications where decisions require explanations, the Throughput Model offers the means to look under the hood of AI and comprehend how those decisions are attained by organizations.

Daniel E Ho and colleagues explore the legal implications of using artificial intelligence in the response to covid-19 and call for more robust evaluation frameworks Numerous proposals, prototypes, and models have emerged for using artificial intelligence (AI) and machine learning to predict individual risk related to covid-19. In the United States, for instance, the Department of Veterans Affairs uses individualised risk scores to allocate medical resources to people with covid-19,1 and prisons have sought to detect symptoms by processing inmates’ phone calls.2 Further tools, such as vulnerability predictions for individuals3 and voice based detection of infection,4 are on the horizon. But use of AI for such purposes has given rise to questions about legality. When a state or federal government seeks to use AI models to predict an individual’s risk of covid-19, the key legal questions will ultimately turn on how effective the models are and how much they burden legal interests. We focus on two of the most salient legal concerns under US law: privacy and discrimination. Challenges on privacy or discrimination grounds might appear in a variety of contexts, including challenges to regulatory decisions, tort actions, or lawsuits under health privacy laws. We argue that the basic need to balance benefits against burdens runs through all of these legal regimes. Governments implementing risk scoring tools must show that their tools produce valid, reliable predictions and burden individuals’ civil liberties no more than necessary. In evaluating the legality of public health use of algorithms, courts will likely also probe how the output of these tools is used to shape policies and programs. But showing that a model performs well and does not exceedingly burden privacy and other interests are essential preconditions for lawful deployment. ### Privacy law Government intrudes on privacy when it forces people to reveal what …

AI is everywhere from Google Search to Waze, from Chatbots to intelligent automatic email responses. As a B2B marketer, this is just another technology that we need to consider as part of the martech stack. So how do we evaluate how AI can help or optimize our marketing? Many marketers feel intimidated by AI because they don't know what it is and how to take advantage of it in the context of marketing. In my previous blog post, I explained AI, Machine Learning and Deep Learning in relationship to Marketing.