Values of Ethical Artificial Intelligence

Artificial Intelligence (AI) is impacting all fields, areas, and disciplines. It is difficult to think of a field that is not infiltrated by AI. AI witnesses groundbreaking advancement recently with the revolutions and evolutions of machine learning techniques (Siau and Yang, 2017; Hyder et al., 2019). With proper guidance and appropriate usage, AI can be one of the most powerful tools that can drastically transform the world. Because of the broad and deep impact of AI, the ethical issues surrounding AI are of great concern to many. As AI advances, it is important to ensure that AI is used and evolved ethically (Siau and Wang, 2020). With the exponential advancement of AI, ethical concerns and issues begin to emerge and attract peoples’ attention. Ethics can be described as the moral ways of restricting the behavior or actions of a person or a group and used rules and decision paths to help make decisions on what is good or right. For AI, it is making positive changes to our daily lives such as improving health care, enhancing safety, and boosting productivity (Wang and Siau, 2019a, b). Avoiding a dystopian future created by AI and incorporating ethical principles into AI decision making are the two biggest areas (Torresen, 2018). Ethical decision making by AI is a big challenge for developers, engineerings, business executives, policymakers, and society as a whole (Siau and Wang, 2018). Developers and technicians need to be trained in ethical reasoning so that they can make ethical design and implementation decisions, and the AI system will be programmed ethically. This research investigates the following questions: Why is ethical AI important? What are the values of ethical AI? Understanding the values of ethical AI is critical. The qualitative research methodology, Value-Focused Thinking approach, is adopted to interview subjects, collect their inputs, and construct a means-ends objective network (Keeney, 1996; Sheng et al. 2005, 2010). The means-ends objective network depicts the fundamental and means objectives to achieve the objective of maximizing ethical AI. Some of the means and fundamental objectives derived in this research include Maximize explainable AI, Maximize developers’ awareness of ethics, Maximize fairness and justice, and Maximize government’s oversight on AI. This research contributes to understanding the very important aspects of AI development and utilization – i.e., how to do so ethically and how to ensure that the end product, the AI, will function ethically

Responsible and Representative Multimodal Data Acquisition and Analysis: On Auditability, Benchmarking, Confidence, Data-Reliance & Explainability

The ethical decisions behind the acquisition and analysis of audio, video or physiological human data, harnessed for (deep) machine learning algorithms, is an increasing concern for the Artificial Intelligence (AI) community. In this regard, herein we highlight the growing need for responsible, and representative data collection and analysis, through a discussion of modality diversification. Factors such as Auditability, Benchmarking, Confidence, Data-reliance, and Explainability (ABCDE), have been touched upon within the machine learning community, and here we lay out these ABCDE sub-categories in relation to the acquisition and analysis of multimodal data, to weave through the high priority ethical concerns currently under discussion for AI. To this end, we propose how these five subcategories can be included in early planning of such acquisition paradigms.Comment: 4 page

Artificial Intelligence Ethics from the Perspective of Educational Technology Companies and Schools

Peer reviewe

Toward an Understanding of Responsible Artificial Intelligence Practices

Artificial Intelligence (AI) is influencing all aspects of human and business activities nowadays. Although potential benefits emerged from AI technologies have been widely discussed in many current literature, there is an urgently need to understand how AI can be designed to operate responsibly and act in a manner meeting stakeholders’ expectations and applicable regulations. We seek to fill the gap by exploring the practices of responsible AI and identifying the potential benefits when implementing responsible AI practices. In this study, 10 responsible AI cases were selected from different industries to better understand the use of responsible AI in practices. Four responsible AI practices are identified, including governance, ethically design solutions, risk control and training and education and five strategies for firms who are considering to adopt responsible AI practices are recommended

Machine Ethics: Do Androids Dream of Being Good People?

Is ethics a computable function? Can machines learn ethics like humans do? If teaching consists in no more than programming, training, indoctrinating¿ and if ethics is merely following a code of conduct, then yes, we can teach ethics to algorithmic machines. But if ethics is not merely about following a code of conduct or about imitating the behavior of others, then an approach based on computing outcomes, and on the reduction of ethics to the compilation and application of a set of rules, either a priori or learned, misses the point. Our intention is not to solve the technical problem of machine ethics, but to learn something about human ethics, and its rationality, by reflecting on the ethics that can and should be implemented in machines. Any machine ethics implementation will have to face a number of fundamental or conceptual problems, which in the end refer to philosophical questions, such as: what is a human being (or more generally, what is a worthy being); what is human intentional acting; and how are intentional actions and their consequences morally evaluated. We are convinced that a proper understanding of ethical issues in AI can teach us something valuable about ourselves, and what it means to lead a free and responsible ethical life, that is, being good people beyond merely "following a moral code". In the end we believe that rationality must be seen to involve more than just computing, and that value rationality is beyond numbers. Such an understanding is a required step to recovering a renewed rationality of ethics, one that is urgently needed in our highly technified society.This work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the terms of the Multi-Annual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M17), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). This research has received funding also from the RESTART project – “Continuous Reverse Engineering for Software Product Lines / Ingeniería Inversa Continua para Líneas de Productos de Software” (ref. RTI2018-099915-B-I00, Convocatoria Proyectos de I + D Retos Investigación del Programa Estatal de I + D + i Orientada a los Retos de la Sociedad 2018, grant agreement nº: 412122; and from the CritiRed project – “Elaboración de un modelo predictivo para el desarrollo del pensamiento crítico en el uso de las redes sociales”, Convocatoria Retos de Investigación del Ministerio de Ciencia, Innovación y Universidades (2019–2022), ref. RTI2018-095740-B-I00

Patient-Centric Ethical Frameworks for Privacy, Transparency, and Bias Awareness in Deep Learning-Based Medical Systems

The rapid advancement and deployment of deep learning-enabled medical systems have necessitated the development of robust ethical frameworks to address potential challenges and pitfalls. Based on the foundational principles of medical ethics—non-maleficence, beneficence, respect for patient autonomy, and justice—three ethical frameworks are proposed in this study for the deployment and oversight of deep learning systems in healthcare. This study presents these three distinct yet interconnected ethical frameworks focusing on patient privacy, transparency, and bias mitigation. The patient privacy framework argues for the importance of patient autonomy. It advocates for informed consent, emphasizing the need for patients to be apprised of the system's workings, benefits, potential risks, and alternatives. Consent should be voluntary, devoid of implicit coercion, and patients must retain the right to revoke it without repercussions. The framework also included the principles of transparency, beneficence, privacy, continual consent, accessibility, and accountability. It champions the idea that consent is dynamic, necessitating regular updates, especially when significant system changes occur. Our ethical framework for transparency accentuates the need for full disclosure. Stakeholders should be provided with a general overview of the system's operations, its inputs, and decision-making processes. Performance metrics, including accuracy, sensitivity, and specificity, should be transparently communicated. Openness, through open-source initiatives and third-party audits, is promoted. The principles of accountability, data transparency, continuous improvement, inclusivity, and external validation are also made integral to this framework, ensuring that stakeholders are consistently informed and engaged. The bias minimization framework highlights the imperative of awareness. Stakeholders should be educated about potential biases and their ramifications. The system should be regularly evaluated for inherent biases, both overt and subtle. Representation is crucial; training data must reflect diverse populations, considering various demographic factors. This framework also promotes fairness, ensuring equitable system performance across different patient groups. Transparency in bias reporting, accountability in bias correction, continuous monitoring, inclusivity in stakeholder engagement, and collaboration with interdisciplinary teams are also included and discussed

IoT based solar powered with USB port of smart home gardening system for greener plants

The Internet of Things (IoT) is part of the application domain that provides mechanisms and methods to interconnect processes and technology to automate real-life activities such as home gardening. However, in urban areas, people interested in gardening are always busy with their daily life, causing the plants to be left without proper care, leading to abnormal growing conditions. Henceforth, this paper describes a system that proposes a home gardening turned intelligence that results in a fully automated home gardening via an Arduino Uno as the key controller. In addition, an android application is designed so that users can observe the parameters of the plant in real-time, such as power level, soil moisture level, water level and plant height. Thus, using the IoT coupled with solar power USB charger for remotely supervising the solar battery level and parameters of the plant anytime and anywhere. As a result, this system can significantly enhance the performance, monitoring and preservation of the garden plants. In the meantime, users will also be able to handle their plants with minimum human intervention regarding health and growth based on resource-efficient energy usage