Building Ethics into Artificial Intelligence

As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researchers. In this paper, we complement existing surveys, which largely focused on the psychological, social and legal discussions of the topic, with an analysis of recent advances in technical solutions for AI governance. By reviewing publications in leading AI conferences including AAAI, AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four areas: 1) exploring ethical dilemmas; 2) individual ethical decision frameworks; 3) collective ethical decision frameworks; and 4) ethics in human-AI interactions. We highlight the intuitions and key techniques used in each approach, and discuss promising future research directions towards successful integration of ethical AI systems into human societies

Digital Technology Disorder: Justification and a proposed model of treatment

Due to advances in technology being made at an exponential rate, organisations are attempting to compete with one another by utilising state-of-the-art technology to provide innovative products and services that encourage use. However, there is no moral code to inform sensitive technology design, a consequence of which is the emergence of so-called technology addiction. While addiction as a term is problematic, increasing evidence suggests that related-conditions present implications for the individual, for organisations and for wider society. In this research, a consideration of the potentially addictive elements of technology indicates that it can be possible to reverse engineer these systems, as it were, to promote the development of new behaviours, which can enable the individual to abstain from overuse. Utilising smartphones to deliver digital behavioural change interventions can leverage abundant data touchpoints to provide highly tailored treatment, in addition to allowing for enhanced monitoring and accuracy. To inform understanding of this contemporary phenomenon, the literature on addiction has been reviewed, along with the literature on persuasion architecture to inform an understanding of techniques that lend themselves to overuse and how these can be leveraged to promote recovery. From which, the authors have developed a proposed model to inform the practice of those operating in the domains of computer science

'HighChest': An augmented freezer designed for smart food management and promotion of eco-efficient behaviour

This paper introduces HighChest, an innovative smart freezer designed to promote energy efficient behavior and the responsible use of food. Introducing a novel humanâmachine interface (HMI) design developed through assessment phases and a user involvement stage, HighChest is state of the art, featuring smart services that exploit embedded sensors and Internet of things functionalities, which enhance the local capabilities of the appliance. The industrial design thinking approach followed for the advanced HMI is intended to maximize the social impact of the food management service, enhancing both the user experience of the product and the userâs willingness to adopt eco- and energy-friendly behaviors. The sensor equipment realizes automatic recognition of food by learning from the users, as well as automatic localization inside the deposit space. Moreover, it provides monitoring of the applianceâs usage, avoiding temperature and humidity issues related to improper use. Experimental tests were conducted to evaluate the localization system, and the results showed 100% accuracy for weights greater or equal to 0.5 kg. Drifts due to the lid opening and prolonged usage time were also measured, to implement automatic reset corrections

Algorithmic aversion in artificial intelligence co-leadership and the impact of metaphors and comparisons

The importance of Artificial Intelligence (AI) has been increasing at a fast pace. Besides transforming our lives, these technologies are crucial to improving business operations by conducting tasks faster, better and with lower costs, and by helping in the decision­making process that is an intellectually demanding task. However, despite the advantages AI can offer, people still disbelieve the ability of algorithms, often prefer to decide for themselves and refuse to rely on algorithms after seeing them err. This phenomenon is called algorithm aversion and goes against the best interest of companies that need to gain competitive advantage in a very competitive market. In this way, this dissertation intends to study the potential use of metaphors and comparisons as strategies to reduce algorithm aversion. For this to be done, an experimental study with three experimental groups was conducted. The effect of a language­based metaphor, a visual metaphor and an explicit comparison between human and artificial neurons was studied by relying on a specific type of AI called Artificial Neural Network (ANN) to see if people would prefer this technology that seems to function in a similar way to humans, over a general AI in a leadership position. The results of the study did not corroborate the hypothesis that the metaphors were going to reduce algorithm aversion, as the only difference found in the leadership acceptance was between the general AI group and the human one in which the new leader was a normal person.A importância da Inteligência Artificial (IA) tem aumentado a um ritmo rápido. Além de transformar as nossas vidas, estas tecnologias são cruciais para melhorar operações de empresas, realizando tarefas de modo mais rápido, melhor, com menos custos, e auxiliando a tomada de decisão que é uma tarefa intelectualmente exigente. No entanto, apesar das vantagens que a IA pode oferecer, as pessoas ainda não acreditam na capacidade dos algoritmos, muitas vezes preferem decidir por si mesmas e recusam­se a confiar nos algoritmos depois de os verem errar. Este fenómeno é chamado de aversão ao algoritmo e vai contra o melhor interesse das empresas que precisam de ganhar vantagem competitiva num mercado muito competitivo. Assim, esta dissertação pretende estudar o potencial uso de metáforas e comparações como estratégias para reduzir a aversão a algoritmos. Para isso, foi realizado um estudo experimental com três grupos experimentais. O efeito de uma metáfora linguística, uma metáfora visual e uma comparação explícita entre neurónios humanos e artificiais foi estudado através de um tipo específico de IA chamada Rede Neural Artificial (RNA) para ver se as pessoas prefeririam esta tecnologia que parece funcionar de modo semelhante aos humanos, em vez de uma IA geral numa posição de liderança. Os resultados do estudo não corroboraram a hipótese de que a metáfora incluída na RNA iria reduzir a aversão ao algoritmo, pois a única diferença encontrada na aceitação da liderança foi entre o grupo de IA geral e o humano em que o novo líder era uma pessoa normal