AI has become a part of all our lives. Our cars have automatic braking, platforms like Netflix and Spotify have recommendations, and Alexa and Google can search things for us on command, all powered by artificial intelligence. Although this technology comes with a lot of convenience and advantages, people are also concerned about its dangers. Inadequate security and ethical problems are a few examples of the cons that come with AI. In response to these dangers, the European Union has decided to work on a legal framework to regulate the way AI is used.

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In the past decades, the revolutionary advances of Machine Learning (ML) have shown a rapid adoption of ML models into software systems of diverse types. Such Machine Learning Software Applications (MLSAs) are gaining importance in our daily lives. As such, the Quality Assurance (QA) of MLSAs is of paramount importance. Several research efforts are dedicated to determining the specific challenges we can face while adopting ML models into software systems. However, we are aware of no research that offered a holistic view of the distribution of those ML quality assurance challenges across the various phases of software development life cycles (SDLC). This paper conducts an in-depth literature review of a large volume of research papers that focused on the quality assurance of ML models. We developed a taxonomy of MLSA quality assurance issues by mapping the various ML adoption challenges across different phases of SDLC. We provide recommendations and research opportunities to improve SDLC practices based on the taxonomy. This mapping can help prioritize quality assurance efforts of MLSAs where the adoption of ML models can be considered crucial.

To implement fair machine learning in a sustainable way, choosing the right fairness objective is key. Since fairness is a concept of justice which comes in various, sometimes conflicting definitions, this is not a trivial task though. The most appropriate fairness definition for an artificial intelligence (AI) system is a matter of ethical standards and legal requirements, and the right choice depends on the particular use case and its context. In this position paper, we propose to use a decision tree as means to explain and justify the implemented kind of fairness to the end users. Such a structure would first of all support AI practitioners in mapping ethical principles to fairness definitions for a concrete application and therefore make the selection a straightforward and transparent process. However, this approach would also help document the reasoning behind the decision making. Due to the general complexity of the topic of fairness in AI, we argue that specifying "fairness" for a given use case is the best way forward to maintain confidence in AI systems. In this case, this could be achieved by sharing the reasons and principles expressed during the decision making process with the broader audience.

Today, the European Commission proposed regulations for the European Union (EU). The proposed regulations are discussed on the EU site. They are of interest for more than only facial recognition, but as the start of what will be increasing regulation for many aspects of artificial intelligence (AI). There should be zero surprise that facial recognition is the first major aspect of AI to meet with government regulations. This technology is very intrusive and can directly impact the lives of all citizens in many ways.

If you don't have enough to worry about already, consider a world where AIs are hackers. Hacking is as old as humanity. We are creative problem solvers. We exploit loopholes, manipulate systems, and strive for more influence, power, and wealth. To date, hacking has exclusively been a human activity.

Alongside the rapid development of artificial intelligence, we've seen a proliferation of AI "principles," or guidelines for how AI should be built and used. Governments, companies, advocacy groups, and multi-stakeholder initiatives have all advanced perspectives. This project emerged from our curiosity about these principles. Were they wildly divergent, or was there enough commonality to suggest the emergence of sectoral norms? Some were framed as ethical in nature; others drew from human rights law.

The proposed legislation means that companies will no longer be free in the safety assessment of their AI products and will have to observe strict …

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While US lawmakers muddle through yet another congressional hearing on the dangers posed by algorithmic bias in social media, the European Commission (basically the executive branch of the EU) has unveiled a sweeping regulatory framework that, if adopted, could have global implications for the future of AI development. After extensive meetings with advocate groups and other stakeholders, the EC released both the first European Strategy on AI and Coordinated Plan on AI in 2018. Those were followed in 2019 by the Guidelines for Trustworthy AI, then again in 2020 by the Commission's White Paper on AI and Report on the safety and liability implications of Artificial Intelligence, the Internet of Things and robotics. Just as with its ambitious General Data Protection Regulation (GDPR) plan in 2018, the Commission is seeking to establish a basic level of public trust in the technology based on strident user and data privacy protections as well as those against its potential misuse. "Artificial intelligence should not be an end in itself, but a tool that has to serve people with the ultimate aim of increasing human well-being. Rules for artificial intelligence available in the Union market or otherwise affecting Union citizens should thus put people at the centre (be human-centric), so that they can trust that the technology is used in a way that is safe and compliant with the law, including the respect of fundamental rights," the Commission included in its draft regulations.