Vulnerability in Social Epistemic Networks

Social epistemologists should be well-equipped to explain and evaluate the growing vulnerabilities associated with filter bubbles, echo chambers, and group polarization in social media. However, almost all social epistemology has been built for social contexts that involve merely a speaker-hearer dyad. Filter bubbles, echo chambers, and group polarization all presuppose much larger and more complex network structures. In this paper, we lay the groundwork for a properly social epistemology that gives the role and structure of networks their due. In particular, we formally define epistemic constructs that quantify the structural epistemic position of each node within an interconnected network. We argue for the epistemic value of a structure that we call the (m,k)-observer. We then present empirical evidence that (m,k)-observers are rare in social media discussions of controversial topics, which suggests that people suffer from serious problems of epistemic vulnerability. We conclude by arguing that social epistemologists and computer scientists should work together to develop minimal interventions that improve the structure of epistemic networks

Spartan Daily, April 25, 2003

Volume 120, Issue 58https://scholarworks.sjsu.edu/spartandaily/9854/thumbnail.jp

Why don´t you express youself so that I can understand?

Purpose: Create an overall understanding of computers/software and cognitive science. We also want to investigate discrepancies in 4 particular software systems (The discrepancies are between human/computer and NOT between the computer systems). We also want to investigate if eventual discrepancies, or successes, in the programs might have a connection to the human cognition. Meaning; are these systems built in a way that suits the evolutionary cognitive mind? (I.e.: how the human brain/mind works). Finally, with the help of the four systems as practical examples, we wish to indicate the potential for further financial gain when designing software systems as a whole, using a cognitive approach. Methodology: Due to the difficulty in extracting some of the confidential information, we had to write the thesis as an explorative adapted study, relying heavily on interviews, workshops and an explorative case study. The case being the Liverpool Museum project, researching children’s answers of a museum filed trip. We also chose to make two surveys of our own. These will be either added as appendixes, and/or described in the text. Theory: Main: Cognitive Science, focusing on the work by Dave Snowden. Supporting/explaining; Computational complexity, Web scraping, Artificial Intelligence (A.I.), Black Swan and Knowledge Management. Empirical foundation: Primary data consist of interviews, workshops and a survey of LinkedIn.com and Monster.com. Secondary data consists of scientific articles and information from the Internet and an investigation of two confidential search engines. Findings and Conclusions: The investigation of the four search systems illustrates that there is a software design aspect linked to cognitive science. More research is necessary before any clear conclusions can be made, but this thesis implied that a least a part of the investigated discrepancy is caused by neglect of the human cognition when developing software. This also indicates that there is a potential for efficiency impact in financial terms, if considering this in future software development

Blockchain for Organising Effective Grass-Roots Actions on a Global Commons: Saving The Planet

An overwhelming majority of experts has been flagging for decades that “Saving the Planet” requires immediate, persistent and drastic action to curb a variety of catastrophic risks over the 21st century. However, despite compelling evidence and a range of suggested solutions, transnational coordination of effective measures to protect our biosphere continues to fall short. To remedy, we propose a novel platform for addressing the central issue of affording trust, transparency and truth while minimizing administrative overheads. This will empower an even loosely organised, global grass-roots community to coordinate a large-scale project on a shared goal (“Commons”) spanning the digital and real world. The Web3 concept is based on the swiftly emerging “Blockchain” and related cryptographic, distributed and permissionless technologies. “Wisdom of the crowds” mechanisms involving competitive parallelisation and prediction markets are enabled by formalised reputation and staking to incentivise high-quality work, fair validation and best management practice. While these mechanisms have been (mostly separately) applied to science, business, governance, web, sensor, information and communication technologies (ICT), our integrative approach around Blockchain-enabled ‘operating principles and protocols’ sets the basis for designing novel forms of potentially crowdfunded Decentralised Autonomous Organisations (DAOs)

Wisdom of the Crowd within Enterprises: Practices and Challenges

The Wisdom of the Crowd advocates that decisions collectively made by a diverse crowd could be better than those made by an elite group of experts. The Wisdom of the Crowd puts preconditions on this to work correctly. This concerns the di- versity of the crowd, their independence from each other, their decentralisation, and the methods of aggregating their distributed knowledge and forming collec- tive decisions. Although the concept is inspiring, its interpretation and conduct differ significantly amongst enterprises, especially with regard to the culture and style of management. In addition, we still lack reflections on how the Wisdom of the Crowd worked in the practice of modern enterprises. To address this lack of knowledge, this paper conducts an empirical study following a mixed method approach involving 35 senior managers coming from 33 different industries in the UK. In the first phase we interview eight managers and, in the second, we con- firm and enhance the results by a survey consisting of open-ended questions and involving 27 other managers. The results shed light on the current practice of the Wisdom of the Crowd in several UK enterprises, which can inform the analysis and design of future software tools meant to aid this emerging decision-making mechanism

Ethical Questions Raised by AI-Supported Mentoring in Higher Education

Mentoring is a highly personal and individual process, in which mentees take advantage of expertise and experience to expand their knowledge and to achieve individual goals. The emerging use of AI in mentoring processes in higher education not only necessitates the adherence to applicable laws and regulations (e.g., relating to data protection and nondiscrimination) but further requires a thorough understanding of ethical norms, guidelines, and unresolved issues (e.g., integrity of data, safety, and security of systems, and confidentiality, avoiding bias, insuring trust in and transparency of algorithms). Mentoring in Higher Education requires one of the highest degrees of trust, openness, and social–emotional support, as much is at the stake for mentees, especially their academic attainment, career options, and future life choices. However, ethical compromises seem to be common when digital systems are introduced, and the underlying ethical questions in AI-supported mentoring are still insufficiently addressed in research, development, and application. One of the challenges is to strive for privacy and data economy on the one hand, while Big Data is the prerequisite of AI-supported environments on the other hand. How can ethical norms and general guidelines of AIED be respected in complex digital mentoring processes? This article strives to start a discourse on the relevant ethical questions and in this way raise awareness for the ethical development and use of future data-driven, AI-supported mentoring environments in higher education