Automated systems that implement Machine learning (ML) and Artificial Intelligence (AI) algorithms present promising solutions to a variety of technological and non-technological issues. Although, industry leaders are rapidly adopting these systems for anything from marketing to national defense operations, these systems are not without flaws. Recently, many of these systems are found to inherit and propagate gender and racial biases that disadvantages the minority population. In this paper, we analyze academic publications in the area of gender biases in ML and AI algorithms thus outlining different themes, mitigation and detection methods explored through research in this topic. Through a detailed analysis of N = 120 papers, we map the current research landscape on gender specific biases present in ML and AI assisted automated systems. We further point out the aspects of ML/AI gender biases research that are less explored and require more attention. Mainly we focus on the lack of user studies and inclusivity in this field of study. We also shed some light into the gender bias issue as experienced by the algorithm designers. In conclusion, in this paper we provide a holistic view of the breadth of studies conducted in the field of exploring, detecting and mitigating gender biases in ML and AI systems and, a future direction for the studies to take in order to provide a fair and accessible ML and AI systems to all users.

Interactions in healthcare, by necessity, involve sharing sensitive information to achieve high-quality patient outcomes. Therefore, sensitive data must be carefully protected. This article explores existing privacy and security research conducted in the context of healthcare organizations. We conducted a systematic literature review of N =1,553 articles that examine the security and privacy of healthcare data and focus on 80 articles addressing human factors. Key findings show that much of the healthcare security and privacy research is focused on technology (44.11%, 712 articles), with a lack of emphasis on the human element (4.96%, 80 articles). In the subset of user studies, we find that patients and the general public express concerns about privacy and security with technologies like electronic health records (EHRs). Furthermore, our analysis shows that healthcare professionals often have low awareness of risks related to data security. Additionally, our analysis revealed that most research focuses narrowly on large hospitals, neglecting private practices and the unique challenges they face. We conclude by identifying research gaps and providing potential solutions to enable robust data security for sensitive patient data.

The World Wide Web (www) consists of the surface web, deep web, and Dark Web, depending on the content shared and the access to these network layers. Dark Web consists of the Dark Net overlay of networks that can be accessed through specific software and authorization schema. Dark Net has become a growing community where users focus on keeping their identities, personal information, and locations secret due to the diverse population base and well-known cyber threats. Furthermore, not much is known of Dark Net from the user perspective, where often there is a misunderstanding of the usage strategies. To understand this further, we conducted a systematic analysis of research relating to Dark Net privacy and security on $N=200$ academic papers, where we also explored the user side. An evaluation of secure end-user experience on the Dark Net establishes the motives of account initialization in overlaid networks such as Tor. This work delves into the evolution of Dark Net intelligence for improved cybercrime strategies across jurisdictions. The evaluation of the developing network infrastructure of the Dark Net raises meaningful questions on how to resolve the issue of increasing criminal activity on the Dark Web. We further examine the security features afforded to users, motives, and anonymity revocation. We also evaluate more closely nine user-study-focused papers revealing the importance of conducting more research in this area. Our detailed systematic review of Dark Net security clearly shows the apparent research gaps, especially in the user-focused studies emphasized in the paper.

Privacy policies outline data collection and sharing practices followed by an organization, together with choice and control measures available to users to manage the process. However, users have often needed help reading and understanding such documents, regardless of their being written in a natural language. The fundamental problems with privacy policies persist despite advancements in privacy design, frameworks, and regulations. To identify the causes of privacy policies being persistently challenging to comprehend, it is vital to investigate historical policy patterns and understand the evolution of privacy policies concerning information packaging and presentation. To this aid, we create a sentence-level classifier to conduct a large-scale longitudinal analysis on different privacy policies from 130,604 organizations, totaling approximately one million policies from 1997 to 2019. We annotate 10,717 sentences from 115 policies in the OPP-115 corpus to implement the classifier and then use those annotations to train the XLNet and BERT classifiers. Results from our analysis reveal that specific data practice categories experience more frequent policy changes than others, making it challenging to track relevant information over time. In addition, we discover that every category has distinct composition, readability, and structural issues, which exacerbate when categories frequently co-occur in a document. Based on our observations, we provide recommendations for policy articulation and revision to make privacy policy documents conform to better coherence and structure.