This tutorial is part of the Getting started with Watson OpenScale learning path. In this tutorial, you'll see how IBM Watson OpenScale can be used to monitor your artificial intelligence (AI) models for fairness and accuracy. You'll get a hands-on look at how Watson OpenScale will automatically generate a debiased model endpoint to mitigate your fairness issues and provides an explainability view to help you understand how your model makes its predictions. In addition, you'll see how Watson OpenScale uses drift detection. Drift detection will tell you when runtime data is inconsistent with your training data or if there is an increase the data that is likely to lead to lower accuracy.

In January, Liz O'Sullivan wrote a letter to her boss at artificial intelligence startup Clarifai, asking him to set ethical limits on its Pentagon contracts. WIRED had previously revealed that the company worked on a controversial project processing drone imagery. O'Sullivan urged CEO Matthew Zeiler to pledge the company would not contribute to the development of weapons that decide for themselves whom to harm or kill. At a company meeting a few days later, O'Sullivan says, Zeiler rebuffed the plea, telling staff he saw no problems with contributing to autonomous weapons. Clarifai did not respond to a request for comment.

Note: This blog post was authored by Aaron Baughman with Stephen Hammer, Eythan Holladay, Eduardo Morales and Gary Reiss. Tennis play at the US Open consists of 254 matches in the men's and women's singles events totaling tens of thousands of points. During the tournament's two weeks, many matches are played in parallel, and it's virtually impossible for any tennis fan, or the editorial team at the United States Tennis Association (USTA), to capture any sizable percentage of the best points. To help solve this challenge, IBM built an AI system that clips and creates candidate highlight videos and assigns a fair excitement score, all within two minutes of the end of each match. Every highlight is ranked so that tennis fans and video editors at the USTA and its broadcast partners can see the most exciting points of the tournament, while minimizing the influence of player gestures, match analytic score, player rank, player age and crowd size.

IBM has been tweaking the AI-powered highlight picking algorithm it deploys during the Wimbledon tennis championships this year to take into account a wider array of factors to better find and personalise the best points to share with fans around the world. Big Blue is celebrating a 30-year technology partnership with the famous grass court tennis tournament, and in 2017 it unveiled an AI-powered system for picking the best points to insert into a highlights package, with the aim of delivering highlights "better than an international media organisation" as Sam Sneddon, IBM sports and entertainment lead, told Computerworld UK during a tour of its technology bunker on-site at the Championships this year. Whether it was Novak Djokovic and Roger Federer's five-hour epic mens' final, or Simona Halep's swift dismantling of Serena Williams in the ladies' final, IBM was working in the background to map and collect every second of footage before feeding it through a set of machine learning and deep learning algorithms which decide the points that would make for the best 5-10 minute highlight package. The Watson system analyses 39 factors, like player gestures and crowd reactions, from live footage and assigns an'excitement score'. For an idea of scale, IBM collects 4.5 million tennis data points per tournament.

Note: This blog post was authored by Aaron Baughman with Stephen Hammer, Eythan Holladay, Eduardo Morales and Gary Reiss. Wimbledon is one of the most prestigious major events in the world. With over 675 matches played and over 147,000 tennis points played, its size and scale are substantial. In fact, even if fans diligently watch their favorite players, they will miss a high proportion of the played points. Wimbledon uses IBM digital and AI capabilities to provide rapid access to match highlights to serve up the best content to fans.

Do you want to learn how you can accelerate your AI strategy or get ahead of the latest AI trends? Or are you more curious to learn what results businesses are achieving by adopting AI? Either way, make sure you attend Think 2019 and experience Watson AI technology first-hand. Here's a sneak peek at five sessions you can't miss: Being able to explain the decisions your AI makes and have trust in them is crucial to accelerating adoption of AI in your business. In these sessions, you'll learn how AI OpenScale provides businesses with confidence in AI decisions and infuses AI throughout its full lifecycle with trust and transparency, explains outcomes, and automatically mitigates bias. However, there are still a variety of hurdles businesses need to overcome to scale and automate their AI.

IBM today introduced AI OpenScale, a new technology platform that addresses key challenges of artificial intelligence (AI) adoption, such as concerns over how AI applications make decisions, the global shortage of AI skills and the complexities of working with disparate AI tools from multiple vendors. IBM's new technology platform is the first of its kind. It will enable companies to manage AI transparently throughout the full AI lifecycle, irrespective of where their AI applications were built or in which environment they currently run. AI OpenScale can detect and address bias across the spectrum of AI applications, as those applications are being run. As part of AI OpenScale, IBM also will debut NeuNetS, a major scientific breakthrough in which AI builds AI – making it possible to create complex, deep-neural networks from scratch.

One of artificial intelligence's known weaknesses is bias. Now a new platform by IBM aims to give businesses the tool to detect that bias -- and fix it. IBM on Monday announced AI OpenScale, a new artificial-intelligence platform that among other things is supposed to clear up how AI makes decisions. "How do we help organizations have more trust and transparency in AI?" said Ritika Gunnar, vice president of IBM's Watson AI technology, in an interview Friday. She gave this example: Insurance companies are using AI to help them accept or reject claims.

IBM (NYSE: IBM) today introduced AI OpenScale, a new technology platform that addresses key challenges of artificial intelligence (AI) adoption, such as concerns over how AI applications make decisions, the global shortage of AI skills and the complexities of working with disparate AI tools from multiple vendors. IBM's new technology platform is the first of its kind. It will enable companies to manage AI transparently throughout the full AI lifecycle, irrespective of where their AI applications were built or in which environment they currently run. AI OpenScale can detect and address bias across the spectrum of AI applications, as those applications are being run. As part of AI OpenScale, IBM also will debut NeuNetS, a major scientific breakthrough in which AI builds AI – making it possible to create complex, deep-neural networks from scratch.