Data, Power and Bias in Artificial Intelligence

Artificial Intelligence has the potential to exacerbate societal bias and set back decades of advances in equal rights and civil liberty. Data used to train machine learning algorithms may capture social injustices, inequality or discriminatory attitudes that may be learned and perpetuated in society. Attempts to address this issue are rapidly emerging from different perspectives involving technical solutions, social justice and data governance measures. While each of these approaches are essential to the development of a comprehensive solution, often discourse associated with each seems disparate. This paper reviews ongoing work to ensure data justice, fairness and bias mitigation in AI systems from different domains exploring the interrelated dynamics of each and examining whether the inevitability of bias in AI training data may in fact be used for social good. We highlight the complexity associated with defining policies for dealing with bias. We also consider technical challenges in addressing issues of societal bias.Comment: 3 page

Applying Reflexivity to Artificial Intelligence for Researching Marginalized Communities and Real-World Problems

Despite advances in artificial intelligence (AI), ethical principles have been overlooked, harming marginalized communities. These flaws are due to a lack of critical insight into the complex positionality of the researcher, power dynamics between scholars and the communities being studied, and the structural impact on real-world problems when AI systems appear to be accurate but ethically fail. Reflexivity is a process that yields a better understanding of community-specific nuances, areas requiring local expertise, and the potential consequences of scholastic interventions for real-world problems (i.e., social, environmental, or socioeconomic). The paper builds on the five stages of social work reflexivity that can be applied to AI researchers and provided questions that can be asked in order to increase privacy, accountability, and fairness. We discuss the effective implementation of reflexivity in research, detail the stages of social work reflexivity and highlight key questions for AI researchers to ask throughout the research process

Reconceptualizing The Ethical Guidelines for Mental Health Apps: Values From Feminism, Disability Studies, and Intercultural Ethics

Existing ethical guidelines that aim to guide the development of mental health apps tend to overemphasize the role of Western conceptual frameworks. While such frameworks have proved to be a useful first step in introducing ethics to a previously unregulated industry, the rapid global uptake of mental health apps requires thinking more deeply about the diverse populations these apps seek to serve. One way to do this is to introduce more intercultural ethical perspectives into app design and the guidelines that aim to encourage best practices. In addition to this, existing ethical guidelines can also benefit from the ethical scholarship from the feminist and disability traditions, both of which highlight specific ethical considerations for vulnerable users. Rethinking the ethical responsibilities of mental health app designers through the prisms of feminism, disability studies, and intercultural philosophy leads us to a more global and inclusive set of ethical considerations in app design. This white paper explores what existing guidelines for the regulation of mental health apps are missing. It also explores how these guidelines could be improved for users who inhabit an increasingly diverse and globalized worl

Exploring the Role of Higher Education in Responsible Deployment of Artificial Intelligence

Higher education is the key driver for the teaching, research, and development of Artificial Intelligence (AI), as it bears responsibility for preparing engineers, scientists, technologists, and corporate leaders who shape and fuel its revolutionary advances. With AI and automation technologies relying on more advanced levels of training, and universities serving as the prime site for their development, faculty views on the implications of this technology are critically important. The purpose of this case study was to gain insights into how academics and disciplinary experts perceive their roles and responsibilities in the teaching, development, and deployment of AI. Using FIU as a case study provided a base for a contextual understanding of the complex issues surrounding AI from the perspective of key actors at a large public university. In conducting the study, 16 faculty from a range of disciplines were interviewed. The interviews were recorded, transcribed, and analyzed. The data from the interviews were examined to identify the connectedness of ideas and develop themes to classify distinct concepts. The study found that while participants were optimistic about the transformative possibilities of AI for improving human life, they were concerned about its implications. They stressed the intensification of many social challenges by AI, including gender and racial bias in class, gender and race in automated decision-making systems, its negative impact on social media, the use of AI for manipulation of the public, and deceptive practices of internet corporations. The participants also discussed the economic impacts of AI on job markets, particularly the potential for massive job loss, as well as the role of government and higher education in mitigating the adverse impacts of AI through education and appropriate research policies. The findings of this study provide insights into the challenges of a changing society because of AI and how higher education can mitigate its impact. These findings provide a basis for improving organizational policies and practices in response to the imminent technological changes. They also inform educational and research policy formulation to promote social change

Issues in Computer Vision Data Collection: Bias, Consent, and Label Taxonomy

Recent success of the convolutional neural network in image classification has pushed the computer vision community towards data-rich methods of deep learning. As a consequence of this shift, the data collection process has had to adapt, becoming increasingly automated and efficient to satisfy algorithms that require massive amounts of data. In the push for more data, however, careful consideration into decisions and assumptions in the data collection process have been neglected. Likewise, users accept datasets and their embed- ded assumptions at face-value, employing them in theory and application papers without scrutiny. As a result, undesirable biases, non-consensual data collection, and inappropriate label taxonomies are rife in computer vision datasets. This work aims to explore issues of bias, consent, and label taxonomy in computer vision through novel investigations into widely-used datasets in image classification, face recognition, and facial expression recognition. Through this work, I aim to challenge researchers to reconsider normative data collection and use practices such that computer vision systems can be developed in a more thoughtful and responsible manner

Agentes Virtuales Cognitivos en el proceso del aprendizaje en el Perú: Escenario al 2032

La presente Investigación aborda la importancia de la creación de escenarios futuros de los Agentes Virtuales Cognitivos en el proceso del aprendizaje, a través del uso la prospectiva en los planes de gestión de la innovación en el marco del uso de las aplicaciones de la computación cognitiva, que permitan relacionar la información, tecnología, personas y la ciencia cognitiva. Actualmente se sobreestima el uso de las nuevas tecnologías, no se pueden cuantificar los beneficios a largo plazo como las verdaderas necesidades, ni el contexto en que se pueden desenvolver; como la relación de las personas y las máquinas. El potencial que se puede aprovechar o desaprovechar al generar nuevo conocimiento que puede beneficiar a los diferentes actores involucrados en el proceso del proceso del aprendizaje, estimando esfuerzo y valiosos recursos que pueden ser aprovechados. Este trabajo se desarrolla bajo el marco de la disciplina prospectiva, usando la construcción de escenarios, tomando como referencia las metodologías en la investigación del futuro como el análisis de drivers, paneles de expertos, análisis estructural, construcción de escenarios. Los resultados obtenidos mostrarán los escenarios posibles que servirán en un futuro, como base en la definición de un plan estratégico en la gestión de la innovación entre los agentes cognitivos y el proceso de aprendizaje permitiendo la inclusión de las tecnologías de la Inteligencia artificial en general, en el campo de la cognición. Mejorando el entendimiento y la transferencia de un conocimiento claro y preciso adoptando estos proyectos basados en tecnologías con un carácter disruptivo, donde la única premisa es la adaptabilidad al cambio. Siendo los únicos beneficiados la sociedad en general en todos los sectores no sólo los que trabajan en una entidad estatal, sino todos los peruanos que accedamos a un servicio bajo estas tecnologías de la ciencia cognitiva.This Research addresses the importance of the creation of future scenarios of Cognitive Virtual Agents in the learning process, through the use of prospective in innovation management plans within the framework of the use of cognitive computing applications, that allow relating information, technology, people and cognitive science. Currently the use of new technologies is overestimated, the long-term benefits cannot be quantified as the true needs, nor the context in which they can be developed; like the relationship of people and machines. The potential that can be exploited or wasted when generating new knowledge that can benefit the different actors involved in the learning process, estimating effort and valuable resources that can be used. This work is developed under the framework of the prospective discipline, using the construction of scenarios, taking as a reference the methodologies in future research such as the analysis of drivers, panels of experts, structural analysis, construction of scenarios. The results obtained will show the possible scenarios that will serve in the future, as a basis for defining a strategic plan in the management of innovation between cognitive agents and the learning process, allowing the inclusion of artificial intelligence technologies in general, in the field of cognition. Improving the understanding and transfer of clear and precise knowledge by adopting these projects based on technologies with a disruptive nature, where the only premise is adaptability to change. The only beneficiaries being society in general in all sectors, not only those who work in a state entity, but all Peruvians who access a service under these cognitive science technologies