Metadata enrichment for digital heritage: users as co-creators

This paper espouses the concept of metadata enrichment through an expert and user-focused approach to metadata creation and management. To this end, it is argued the Web 2.0 paradigm enables users to be proactive metadata creators. As Shirky (2008, p.47) argues Web 2.0’s social tools enable “action by loosely structured groups, operating without managerial direction and outside the profit motive”. Lagoze (2010, p. 37) advises, “the participatory nature of Web 2.0 should not be dismissed as just a popular phenomenon [or fad]”. Carletti (2016) proposes a participatory digital cultural heritage approach where Web 2.0 approaches such as crowdsourcing can be sued to enrich digital cultural objects. It is argued that “heritage crowdsourcing, community-centred projects or other forms of public participation”. On the other hand, the new collaborative approaches of Web 2.0 neither negate nor replace contemporary standards-based metadata approaches. Hence, this paper proposes a mixed metadata approach where user created metadata augments expert-created metadata and vice versa. The metadata creation process no longer remains to be the sole prerogative of the metadata expert. The Web 2.0 collaborative environment would now allow users to participate in both adding and re-using metadata. The case of expert-created (standards-based, top-down) and user-generated metadata (socially-constructed, bottom-up) approach to metadata are complementary rather than mutually-exclusive. The two approaches are often mistakenly considered as dichotomies, albeit incorrectly (Gruber, 2007; Wright, 2007) . This paper espouses the importance of enriching digital information objects with descriptions pertaining the about-ness of information objects. Such richness and diversity of description, it is argued, could chiefly be achieved by involving users in the metadata creation process. This paper presents the importance of the paradigm of metadata enriching and metadata filtering for the cultural heritage domain. Metadata enriching states that a priori metadata that is instantiated and granularly structured by metadata experts is continually enriched through socially-constructed (post-hoc) metadata, whereby users are pro-actively engaged in co-creating metadata. The principle also states that metadata that is enriched is also contextually and semantically linked and openly accessible. In addition, metadata filtering states that metadata resulting from implementing the principle of enriching should be displayed for users in line with their needs and convenience. In both enriching and filtering, users should be considered as prosumers, resulting in what is called collective metadata intelligence

OVERVIEW OF ARTIFICIAL INTELLIGENCE (AI) APPLICATION IN THE BANKING INDUSTRY

Many factors have facilitated transformational changes in the banking industry in recent years. Among the most notable drivers of change are technological advances, particularly the development of AI-based solutions. The integration of AI and ML models has caused a significant transformation of banking operations and the overall banking industry. The rapid evolution of AI technology offered solutions for challenges found in traditional banking processes and operations. Consequently, key applications of AI solutions are identified in fraud detection, risk management, customer support, and regulatory compliance

A Transparency Index Framework for Machine Learning powered AI in Education

The increase in the use of AI systems in our daily lives, brings calls for more ethical AI development from different sectors including, finance, the judiciary and to an increasing extent education. A number of AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI, such as fairness, explainability and safety. However, the abstract nature of these existing ethical AI guidelines often makes them difficult to operationalise in real-world contexts. The inadequacy of the existing situation with respect to ethical guidance is further complicated by the paucity of work to develop transparent machine learning powered AI systems for real-world. This is particularly true for AI applied in education and training. In this thesis, a Transparency Index Framework is presented as a tool to forefront the importance of transparency and aid the contextualisation of ethical guidance for the education and training sector. The transparency index framework presented here has been developed in three iterative phases. In phase one, an extensive literature review of the real-world AI development pipelines was conducted. In phase two, an AI-powered tool for use in an educational and training setting was developed. The initial version of the Transparency Index Framework was prepared after phase two. And in phase three, a revised version of the Transparency Index Framework was co- designed that integrates learning from phases one and two. The co-design process engaged a range of different AI in education stakeholders, including educators, ed-tech experts and AI practitioners. The Transparency Index Framework presented in this thesis maps the requirements of transparency for different categories of AI in education stakeholders, and shows how transparency considerations can be ingrained throughout the AI development process, from initial data collection to deployment in the world, including continuing iterative improvements. Transparency is shown to enable the implementation of other ethical AI dimensions, such as interpretability, accountability and safety. The 3 optimisation of transparency from the perspective of end-users and ed-tech companies who are developing AI systems is discussed and the importance of conceptualising transparency in developing AI powered ed-tech products is highlighted. In particular, the potential for transparency to bridge the gap between the machine learning and learning science communities is noted. For example, through the use of datasheets, model cards and factsheets adapted and contextualised for education through a range of stakeholder perspectives, including educators, ed-tech experts and AI practitioners

Equitable Ecosystem: A Two-Pronged Approach to Equity in Artificial Intelligence

Lawmakers, technologists, and thought leaders are facing a once-in-a-generation opportunity to build equity into the digital infrastructure that will power our lives; we argue for a two-pronged approach to seize that opportunity. Artificial Intelligence (AI) is poised to radically transform our world, but we are already seeing evidence that theoretical concerns about potential bias are now being borne out in the market. To change this trajectory and ensure that development teams are focused explicitly on creating equitable AI, we argue that we need to shift the flow of investment dollars. Venture Capital (VC) firms have an outsized impact in determining which innovations will scale, we argue that influencing how these firms allocate the capital in their funds can ensure that issues of equity are top of mind for development teams. To shift the flow of investment dollars, we propose a two-pronged approach that will address two core drivers of the flow of investment: intellectual property (IP) and diversity. Our current IP system incentivizes a lack of transparency in the AI space frustrating attempts by third parties to assess whether AI- powered products and services are inequitable. And the current demographic makeup of VC firms and companies within the AI investment environment are out of sync with the general population, which can have negative downstream effects in terms of bias in AI. To change the existing dynamic, we argue for 1. creating a fifth category of IP for data and AI that would exchange ownership for compliance with a human rights framework and 2. establishing a tax incentive for VC firms graded favorably on our commitment index. Our approach is designed to create an equitable ecosystem of sorts, one that both necessitates and encourages equitable AI from conception to implementation

A semantic model to fight social exclusion

This work presents a semantic model meant to help with the identification and prediction of individuals at risk of social exclusion. The model is based on the self-sufficiency matrix, a tool that evaluates a person's self-sufficiency in different areas, and that is used by Barcelona's City Council. Existing data sources can then be mapped to this model, in order to analyze, query, and visualize the data.This work is partially supported by the Semiotic project, funded by Ministerio de Economia, Industria, y Competitividad (TIN2016-78473-C3-2-R).Peer ReviewedPostprint (author's final draft

The Role of Artificial Intelligence in Mental Health: A Review

The domains of mental health and artificial intelligence (AI) are undergoing rapid advancements, exhibiting the capacity to mutually influence one another in significant ways. The increasing prevalence of mental health illnesses has prompted the exploration of potential remedies in the field of AI, which show promise in the areas of early detection, prevention, and therapy. Sophisticated machine learning algorithms possess the capability to evaluate extensive volumes of data, including social media posts and voice patterns, with the objective of detecting patterns and symptoms associated with mental illness. This facilitates the implementation of more focused interventions and individualized treatment strategies. Furthermore, chatbots utilizing AI have the capability to deliver round-the-clock assistance to those undergoing acute distress or grant them access to therapy in cases where waiting lists are extensive. Nevertheless, it is of utmost importance to guarantee the incorporation of ethical issues throughout the use of AI in the field of mental healthcare. In order to achieve successful integration, it is imperative to address many concerns, including but not limited to privacy, bias, and accurate diagnosis. However, the convergence of mental health and AI offers a distinct prospect to transform our approach to mental disease and improve the availability of care for countless individuals globally

When artificial intelligence meets educational leaders’ data-informed decision-making: A cautionary tale

Artificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision making. Drawing upon multidisciplinary literature that intersects AI, decision making, educational leadership, and policymaking, this position paper aims to examine promising applications and potential perils of AI in educational leaders’ data-informed decision making (DIDM). Endowed with ever-growing computational power and real-time data, highly scalable AI can increase efficiency and accuracy in leaders’ DIDM. However, misusing AI can have perilous effects on education stakeholders. Many lurking biases in current AI could be amplified. Of more concern, the moral values (e.g., fairness, equity, honesty, and doing no harm) we uphold might clash with using AI to make data-informed decisions. Further, missteps on the issues about data security and privacy could have a life-long impact on stakeholders. The article concludes with recommendations for educational leaders to leverage AI potential and minimize its negative consequences

Futures of Responsible and Inclusive AI: How Might We Foster an Inclusive, Responsible and Foresight-Informed AI Governance Approach?

This paper seeks to investigate how we might foster an inclusive, foresight-informed responsible AI governance framework. This paper discusses the gaps and opportunities in current AI initiatives across various stakeholders and acknowledges the importance of anticipation and agility. This paper also posits that it is important for legal, policy, industry and academia to understand the specificities of each other’s domains better to build an inclusive governance framework