In this post I give an overview of the theory, tools, frameworks and best practices I have found until now around the testing (and debugging) of machine learning applications. I will start by giving an overview of the specificities of testing machine learning applications.
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Artificial intelligence (AI) is a technology which is increasingly being utilised in society and the economy worldwide, but there is much disquiet over problematic and dangerous implementations of AI, or indeed even AI itself deciding to do dangerous and problematic actions. These developments have led to concerns about whether and how AI systems currently adhere to and will adhere to ethical standards, stimulating a global and multistakeholder conversation on AI ethics and the production of AI governance initiatives. Such developments form the basis for this chapter, where we give an insight into what is happening in Australia, China, the European Union, India and the United States. We commence with some background to the AI ethics and regulation debates, before proceedings to give an overview of what is happening in different countries and regions, namely Australia, China, the European Union (including national level activities in Germany), India and the United States. We provide an analysis of these country profiles, with particular emphasis on the relationship between ethics and law in each location. Overall we find that AI governance and ethics initiatives are most developed in China and the European Union, but the United States has been catching up in the last eighteen months.
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The past two years I have conducted an extensive literature and tool review to answer the question: “What should software engineers learn about building production-ready machine learning systems?”. During my research I noted that because the discipline of building production-ready machine learning systems is so new, it is not so easy to get the terminology straight. People write about it from different perspectives and backgrounds and have not yet found each other to join forces. At the same time the field is moving fast and far from mature. My focus on material that is ready to be used with our bachelor level students (applied software engineers, profession-oriented education), helped me to consolidate everything I have found into a body of knowledge for building production-ready machine learning (ML) systems. In this post I will first define the discipline and introduce the terminology for AI engineering and MLOps.
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Smart city technologies, including artificial intelligence and computer vision, promise to bring a higher quality of life and more efficiently managed cities. However, developers, designers, and professionals working in urban management have started to realize that implementing these technologies poses numerous ethical challenges. Policy papers now call for human and public values in tech development, ethics guidelines for trustworthy A.I., and cities for digital rights. In a democratic society, these technologies should be understandable for citizens (transparency) and open for scrutiny and critique (accountability). When implementing such public values in smart city technologies, professionals face numerous knowledge gaps. Public administrators find it difficult to translate abstract values like transparency into concrete specifications to design new services. In the private sector, developers and designers still lack a ‘design vocabulary’ and exemplary projects that can inspire them to respond to transparency and accountability demands. Finally, both the public and private sectors see a need to include the public in the development of smart city technologies but haven’t found the right methods. This proposal aims to help these professionals to develop an integrated, value-based and multi-stakeholder design approach for the ethical implementation of smart city technologies. It does so by setting up a research-through-design trajectory to develop a prototype for an ethical ‘scan car’, as a concrete and urgent example for the deployment of computer vision and algorithmic governance in public space. Three (practical) knowledge gaps will be addressed. With civil servants at municipalities, we will create methods enabling them to translate public values such as transparency into concrete specifications and evaluation criteria. With designers, we will explore methods and patterns to answer these value-based requirements. Finally, we will further develop methods to engage civil society in this processes.
