Automating dynamic consent decisions for the processing of social media data in health research

Funding: This work was supported by the Wellcome Trust [UNS19427].Social media have become a rich source of data, particularly in health research. Yet, the use of such data raises significant ethical questions about the need for the informed consent of those being studied. Consent mechanisms, if even obtained, are typically broad and inflexible, or place a significant burden on the participant. Machine learning algorithms show much promise for facilitating a ‘middle ground approach: using trained models to predict and automate granular consent decisions. Such techniques, however, raise a myriad of follow-on ethical and technical considerations. In this paper, we present an exploratory user study (n= 67) in which we find that we can predict the appropriate flow of health-related social media data with reasonable accuracy, while minimising undesired data leaks. We then attempt to deconstruct the findings of this study, identifying and discussing a number of real-world implications if such a technique were put into practicePostprintPeer reviewe

Global Innovations in Measurement and Evaluation

We researched the latest developments in theory and practice in measurement and evaluation. And we found that new thinking, techniques, and technology are influencing and improving practice. This report highlights 8 developments that we think have the greatest potential to improve evaluation and programme design, and the careful collection and use of data. In it, we seek to inform and inspire—to celebrate what is possible, and encourage wider application of these ideas

Strange Loops: Apparent versus Actual Human Involvement in Automated Decision-Making

The era of AI-based decision-making fast approaches, and anxiety is mounting about when, and why, we should keep “humans in the loop” (“HITL”). Thus far, commentary has focused primarily on two questions: whether, and when, keeping humans involved will improve the results of decision-making (making them safer or more accurate), and whether, and when, non-accuracy-related values—legitimacy, dignity, and so forth—are vindicated by the inclusion of humans in decision-making. Here, we take up a related but distinct question, which has eluded the scholarship thus far: does it matter if humans appear to be in the loop of decision-making, independent from whether they actually are? In other words, what is stake in the disjunction between whether humans in fact have ultimate authority over decision-making versus whether humans merely seem, from the outside, to have such authority? Our argument proceeds in four parts. First, we build our formal model, enriching the HITL question to include not only whether humans are actually in the loop of decision-making, but also whether they appear to be so. Second, we describe situations in which the actuality and appearance of HITL align: those that seem to involve human judgment and actually do, and those that seem automated and actually are. Third, we explore instances of misalignment: situations in which systems that seem to involve human judgment actually do not, and situations in which systems that hold themselves out as automated actually rely on humans operating “behind the curtain.” Fourth, we examine the normative issues that result from HITL misalignment, arguing that it challenges individual decision-making about automated systems and complicates collective governance of automation

Contextual consent: ethical mining of social media for health research

Social media are a rich source of insight for data mining and user centred research, but the question of consent arises when studying such data without the express knowledge of the creator. Case studies that mine social data from users of online services such as Facebook and Twitter are becoming increasingly common. This has led to calls for an open discussion into how researchers can best use these vast resources to make innovative findings while still respecting fundamental ethical principles. In this position paper we highlight some key considerations for this topic and argue that the conditions of informed consent are often not being met, and that using social media data that some deem free to access and analyse may result in undesirable consequences, particularly within the domain of health research and other sensitive topics. We posit that successful exploitation of online personal data, particularly for health and other sensitive research, requires new and usable methods of obtaining consent from the user.Publisher PD

The Impact of IT on Insurance of the Technological Industry

The insurance industry has undergone significant transformations due to the rapid advancement of information technology (IT); This paper explores the multifaceted impact of IT on the insurance sector, covering various aspects such as customer experience, operational efficiency, risk assessment, and data security. Through a comprehensive review of existing literature and industry trends, this paper highlights the ways in which IT has revolutionized insurance processes and business models.” Additionally, this paper delves into the paradigm shift brought about by Insurtech startups, which leverage the convergence of IT and insurance to offer innovative solutions like peer-to-peer insurance and usage-based coverage. These startups are reshaping industry dynamics and compelling traditional insurers to adopt digital innovations to remain competitive. Furthermore, the regulatory landscape and compliance considerations arising from technological disruption are explored. The challenges of navigating data privacy compliance and the collaborative efforts between regulators and industry players in shaping technological policies are discussed. Ethical considerations related to IT-driven insurance are also examined, emphasizing the importance of maintaining transparency, fairness, and accountability in decision-making. Ultimately, this research paper underscores the pivotal role of IT in shaping the insurance industry's future, As technology continues to evolve, insurers that strategically integrate IT tools are better positioned to provide innovative, customer-centric solutions while enhancing operational efficiency, risk assessment accuracy, and data security, By embracing IT-driven transformations, insurers can navigate challenges, tap into opportunities, and maintain a competitive edge in the dynamic and rapidly evolving landscape of the insurance sector

Shopping For Privacy: How Technology in Brick-and-Mortar Retail Stores Poses Privacy Risks for Shoppers

As technology continues to rapidly advance, the American legal system has failed to protect individual shoppers from the technology implemented into retail stores, which poses significant privacy risks but does not violate the law. In particular, I examine the technologies implemented into many brick-and-mortar stores today, many of which the average everyday shopper has no idea exists. This Article criticizes these technologies, suggesting that many, if not all of them, are questionable in their legality taking advantage of their status in a legal gray zone. Because the American judicial system cannot adequately protect the individual shopper from these questionable privacy practices, I call upon the Federal Trade Commission, the de facto privacy regulator in the United States, to increase its policing of physical retail stores to protect the shopper from any further harm

Artificial Intelligence in Human Resource Management: Advancements, Implications and Future Prospects

The present condition, challenges, and potential applications of artificial intelligence (AI) in human resource management (HRM) are all explored in this survey article. As an innovation, artificial intelligence (AI) has the potential to completely revolutionize several facets of human resource management (HRM). Examining the usage of AI-powered tools and systems in different HR processes, the present situation with AI in HRM is examined. These encompass learning and development, performance management, employee engagement, and recruiting. The use of AI algorithms and machine learning approaches to automate regular HR operations, analyze vast amounts of employee data, and provide insightful data to aid decision-making is addressed in this article. However, integrating AI into HRM also poses a number of difficulties that must be resolved. Bias, privacy issues, and transparency are just a few of the ethical and legal ramifications of using AI in decision-making processes that are discussed in this survey. The study emphasizes how accountability and fairness must be maintained in AI systems by responsible design, oversight, and periodic evaluation. With an emphasis on job displacement and workforce reorganization, the possible influence of AI on the human workforce is also explored. To effectively traverse this change, strategies including work role redefinition, employee up skilling, and establishing a collaborative atmosphere between humans and AI are suggested. The possible advantages and breakthroughs that AI might bring to HRM practices are highlighted as the future perspectives of AI in HRM are examined. As new applications for AI in HRM, sentiment analysis, predictive analytics, intelligent decision support, and personalized employee experiences are all highlighted. In order to fully realize the promise of AI in HRM, the study underlines the significance of data infrastructure, data governance frameworks, and a data-driven culture. Overall, this survey study offers an in-depth review of the existing situation, difficulties, and prospects for AI in HRM. It aggregates current information, identifies research gaps, and gives practitioners and scholars new perspectives on how AI will fundamentally alter the way HRM activities are carried out in the future

The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism

Computer vision and other biometrics data science applications have commenced a new project of profiling people. Rather than using 'transaction generated information', these systems measure the 'real world' and produce an assessment of the 'world state' - in this case an assessment of some individual trait. Instead of using proxies or scores to evaluate people, they increasingly deploy a logic of revealing the truth about reality and the people within it. While these profiling knowledge claims are sometimes tentative, they increasingly suggest that only through computation can these excesses of reality be captured and understood. This article explores the bases of those claims in the systems of measurement, representation, and classification deployed in computer vision. It asks if there is something new in this type of knowledge claim, sketches an account of a new form of computational empiricism being operationalised, and questions what kind of human subject is being constructed by these technological systems and practices. Finally, the article explores legal mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and the people within it

Limitations Of Artificial Intelligence

Artificial Intelligence is a groundbreaking technology that is now an established field. It is being used to mimic human capabilities such as speaking, listening, learning, and planning by using different algorithms to process data and produce results depending on the information provided by the user. Artificial Intelligence has been used in several industries when it comes to data processing and decision making. Artificial Intelligence has been invented to help decision and solutionmaking processes using a problem-solving approach. The development of Artificial Intelligence software provides efficiency and acceleration on different kinds of workflows, which will help organizations increase their profit and reduce wastage and costs due to poor productivity. There are already many applications that Artificial Intelligence powers; some of these are Web Search, Cybersecurity, and Machine Translations. All people are now having the benefit of using Artificial Intelligence, and it is beneficial for humanity. Artificial Intelligence has many positive aspects as it produces substantial results in people\u27s daily lives and businesses today; some of the most common Artificial Intelligence technologies used by the industry are robots and Virtual Assistants. Artificial Intelligence are powered by Natural Language Processing (NLP) and Speech Recognition Platform (SRP), but it is not limited to these two (2); many factors need to be considered, but these branches help in interpretation and manipulation of the commands stipulated. Indeed, Artificial Intelligence is rapidly advancing, and many organizations are willing to try and test out what is available in the market. However, others are not convinced with the Artificial Intelligence as there are alleged ethical issues that might cause accountability in a particular manner. This thesis will explain how Artificial Intelligence is used in different fields like Law, Medicine, the Military, and others while discussing the limitations present