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May 15, 2018
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whitePaper | July 20, 2022
The growing diversity of actors and circumstances involved in AI/ML deployment makes establishing a set of ethical participatory practices especially difficult for this field. Even among practitioners working at the same organization, there can be substantial differences in both their knowledge about how AI/ML systems were created or will be used and their ability to incorporate inclusive practices. Additionally, the greater availability of AI development platforms, including “no-code” platforms, means that algorithmic models can be deployed without having deep expertise, expanding the number of circumstances automated systems are deployed in. This means many more instances where automated systems are being deployed without consideration of how the algorithms were developed, the provenance of the datasets and nature of the bias on which they were trained and tested, and the ethical implications of their development and deployment.
whitePaper | January 28, 2022
Since more and more decisions that used to be made by humans are nowadays made either with the help of Artificial Intelligence (AI) or by AI alone, it is essential that the AI algorithms are “fair.” In this whitepaper, we discuss issues surrounding the fairness of AI applications, with a special focus on how it can be assessed independently and subsequently certified. We explain why an AI application cannot be classified – and subsequently certified – as “fair” or “unfair” in a general sense and propose an approach that makes it possible to classify it as “fair” or “unfair” under the (application)-specific definition of fairness.
whitePaper | August 22, 2022
Now in its ninth year, the Microsoft Vulnerabilities Report provides a unique analysis of the vulnerability landscape in Microsoft’s ecosystem. Historically, the report has delivered a holistic annual view of the vulnerabilities within Microsoft’s platforms and products, and has established an undeniable business case for the importance of removing admin rights to reduce risk.
whitePaper | August 14, 2020
As consumers and customers, we are accustomed to easy, intuitive and superfast transactions driven by data. Therefore, businesses have the various challenges of managing and mining the data they produce and use, but also ensuring that their digital experience is 100% reliable for their customers — so the chain of trust is never broken.
whitePaper | September 2, 2022
The past several years brought about an abundance of change in how we as individuals, organizations, and a society approach daily life. Our views on ethics are evolving and our sense of acting with purpose is increasing – and as part of that, diversity, equity, and inclusion have become top corporate and personal objectives. Consumers are asking the brands they engage with for more transparency and are tasking them to act in ways that are fair and respectful to all stakeholders: suppliers, employees, prospects, and anyone else who interacts with the brand.
whitePaper | September 23, 2022
It’s been several years since organizations began adopting artificial intelligence (AI) to improve their business; few have come close to mastering its existing capabilities. A small number of organizations in our research aim to become AI-driven—a status we define as AI and machine learning underpinning almost everything the enterprise does—by 2025. However, this elite group—who we term “AI leaders”1—as well as the many others looking simply to embed AI more firmly in the enterprise foundations face formidable challenges to achieving their objectives.
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