Ethical AI: Why fair artificial intelligence might need bias data - TechHQ

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Bias and fairness are not always so closely related. Businesses across industries are racing to integrate artificial intelligence (AI). Use cases are proliferating, from detecting fraud, increasing sales, improving customer experience, automating routine tasks, to providing predictive analytics. With machine learning models relying on algorithms learning patterns from vast pools of data, however, models are at risk of perpetuating bias present in the information they are fed. In this sense, AI's mimicking of real-world, human decisions is both a strength and a great weakness for the technology-- it's only as'good' as the information it accesses.


The power of AI comes with a powerful responsibility - AI for Business

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I'm beyond excited to be here in London with my Microsoft colleagues, as well as innovators, researchers, experts and business decision-makers from around the world at Future Decoded. Over the next two days, we will hear inspiring stories about the possibilities that exist for artificial intelligence to transform the future of work in every industry – and how critical it is that businesses foster a culture that includes everyone as we search for ways to incorporate AI responsibly. This morning's announcement that Microsoft is collaborating with Novartis to use AI to develop treatments and medications faster has the potential to improve patients' lives across the globe. A critical component of our work together is the commitment by Novartis to take AI across the entire organization. This will enable Novartis to bring together previously siloed data sets and research, and to use AI to build upon existing work quickly and efficiently.


Driving AI's potential in organizations

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For some organizations, harnessing artificial intelligence's full potential begins tentatively with explorations of select enterprise opportunities and a few potential use cases. While testing the waters this way may deliver valuable insights, it likely won't be enough to make your company a market maker (rather than a fast follower). To become a true AI-fueled organization, a company may need to fundamentally rethink the way humans and machines interact within working environments. Executives should also consider deploying machine learning and other cognitive tools systematically across every core business process and enterprise operation to support data-driven decision-making. Likewise, AI could drive new offerings and business models. These are not minor steps, but as AI technologies standardize rapidly across industries, becoming an AI-fueled organization will likely be more than a strategy for success--it could be table stakes for survival. In his new book The AI Advantage, Deloitte Analytics senior adviser Thomas H. Davenport describes three stages in the journey that companies can take toward achieving full utilization of artificial intelligence.1 In the first stage, which Davenport calls assisted intelligence, companies harness large-scale data programs, the power of the cloud, and science-based approaches to make data-driven business decisions. Today, companies at the vanguard of the AI revolution are already working toward the next stage--augmented intelligence--in which machine learning (ML) capabilities layered on top of existing information management systems work to augment human analytical competencies. According to Davenport, in the coming years, more companies will progress toward autonomous intelligence, the third AI utilization stage, in which processes are digitized and automated to a degree whereby machines, bots, and systems can directly act upon intelligence derived from them. The journey from the assisted to augmented intelligence stages, and then on to fully autonomous intelligence, is part of a growing trend in which companies transform themselves into "AI-fueled organizations."


Driving AI's potential in organizations

#artificialintelligence

For some organizations, harnessing artificial intelligence's full potential begins tentatively with explorations of select enterprise opportunities and a few potential use cases. While testing the waters this way may deliver valuable insights, it likely won't be enough to make your company a market maker (rather than a fast follower). To become a true AI-fueled organization, a company may need to fundamentally rethink the way humans and machines interact within working environments. Executives should also consider deploying machine learning and other cognitive tools systematically across every core business process and enterprise operation to support data-driven decision-making. Likewise, AI could drive new offerings and business models. These are not minor steps, but as AI technologies standardize rapidly across industries, becoming an AI-fueled organization will likely be more than a strategy for success--it could be table stakes for survival. In his new book The AI Advantage, Deloitte Analytics senior adviser Thomas H. Davenport describes three stages in the journey that companies can take toward achieving full utilization of artificial intelligence.1 In the first stage, which Davenport calls assisted intelligence, companies harness large-scale data programs, the power of the cloud, and science-based approaches to make data-driven business decisions. Today, companies at the vanguard of the AI revolution are already working toward the next stage--augmented intelligence--in which machine learning (ML) capabilities layered on top of existing information management systems work to augment human analytical competencies. According to Davenport, in the coming years, more companies will progress toward autonomous intelligence, the third AI utilization stage, in which processes are digitized and automated to a degree whereby machines, bots, and systems can directly act upon intelligence derived from them. The journey from the assisted to augmented intelligence stages, and then on to fully autonomous intelligence, is part of a growing trend in which companies transform themselves into "AI-fueled organizations."


Responsible AI takes more than good intentions - TechHQ

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Last month, 42 countries signed up to the OECD's common artificial intelligence (AI) principles. Just before that, the European Commission published its own ethics guidelines for trustworthy AI. In fact, to date, there has been a huge amount of work on ethical AI principles, guidelines and standards across different organizations, including IEEE, ISO and the Partnership on AI. On top of these principles, there has been a growing body of work in the fairness, accountability, and transparency machine learning community with a growing number of solutions to tackle bias from a quantitative perspective. Both organizations and governments alike clearly recognize the importance of designing ethics into AI, there's no doubt about that.