Ethical Principles for Reasoning about Value Preferences

To ensure alignment with human interests, AI must consider the preferences of stakeholders, which includes reasoning about values and norms. However, stakeholders may have different preferences, and dilemmas can arise concerning conflicting values or norms. My work applies normative ethical principles to resolve dilemma scenarios in satisfactory ways that promote fairness

Sparse Training Theory for Scalable and Efficient Agents:Blue Sky Ideas Track

A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinforcement learning. Nevertheless, traditional deep learning approaches make use of cloud computing facilities and do not scale well to autonomous agents with low computational resources. Even in the cloud, they suffer from computational and memory limitations, and they cannot be used to model adequately large physical worlds for agents which assume networks with billions of neurons. These issues are addressed in the last few years by the emerging topic of sparse training, which trains sparse networks from scratch. This paper discusses sparse training state-of-the-art, its challenges and limitations while introducing a couple of new theoretical research directions which has the potential of alleviating sparse training limitations to push deep learning scalability well beyond its current boundaries. Nevertheless, the theoretical advancements impact in complex multi-agents settings is discussed from a real-world perspective, using the smart grid case study

Sparse Training Theory for Scalable and Efficient Agents

A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinforcement learning. Nevertheless, traditional deep learning approaches make use of cloud computing facilities and do not scale well to autonomous agents with low computational resources. Even in the cloud, they suffer from computational and memory limitations, and they cannot be used to model adequately large physical worlds for agents which assume networks with billions of neurons. These issues are addressed in the last few years by the emerging topic of sparse training, which trains sparse networks from scratch. This paper discusses sparse training state-of-the-art, its challenges and limitations while introducing a couple of new theoretical research directions which has the potential of alleviating sparse training limitations to push deep learning scalability well beyond its current boundaries. Nevertheless, the theoretical advancements impact in complex multi-agents settings is discussed from a real-world perspective, using the smart grid case study

Reflective Artificial Intelligence

As Artificial Intelligence (AI) technology advances, we increasingly delegate mental tasks to machines. However, today's AI systems usually do these tasks with an unusual imbalance of insight and understanding: new, deeper insights are present, yet many important qualities that a human mind would have previously brought to the activity are utterly absent. Therefore, it is crucial to ask which features of minds have we replicated, which are missing, and if that matters. One core feature that humans bring to tasks, when dealing with the ambiguity, emergent knowledge, and social context presented by the world, is reflection. Yet this capability is completely missing from current mainstream AI. In this paper we ask what reflective AI might look like. Then, drawing on notions of reflection in complex systems, cognitive science, and agents, we sketch an architecture for reflective AI agents, and highlight ways forward

Normative Ethics Principles for Responsible AI Systems: Taxonomy and Future Directions

The rapid adoption of artificial intelligence (AI) necessitates careful analysis of its ethical implications. In addressing ethics and fairness implications, it is important to examine the whole range of ethically relevant features rather than looking at individual agents alone. This can be accomplished by shifting perspective to the systems in which agents are embedded, which is encapsulated in the macro ethics of sociotechnical systems (STS). Through the lens of macro ethics, the governance of systems - which is where participants try to promote outcomes and norms which reflect their values - is key. However, multiple-user social dilemmas arise in an STS when stakeholders of the STS have different value preferences or when norms in the STS conflict. To develop equitable governance which meets the needs of different stakeholders, and resolve these dilemmas in satisfactory ways with a higher goal of fairness, we need to integrate a variety of normative ethical principles in reasoning. Normative ethical principles are understood as operationalizable rules inferred from philosophical theories. A taxonomy of ethical principles is thus beneficial to enable practitioners to utilise them in reasoning. This work develops a taxonomy of normative ethical principles which can be operationalized in the governance of STS. We identify an array of ethical principles, with 25 nodes on the taxonomy tree. We describe the ways in which each principle has previously been operationalized, and suggest how the operationalization of principles may be applied to the macro ethics of STS. We further explain potential difficulties that may arise with each principle. We envision this taxonomy will facilitate the development of methodologies to incorporate ethical principles in reasoning capacities for governing equitable STS

Is it time for robot rights? Moral status in artificial entities

Some authors have recently suggested that it is time to consider rights for robots. These suggestions are based on the claim that the question of robot rights should not depend on a standard set of conditions for ‘moral status’; but instead, the question is to be framed in a new way, by rejecting the is/ought distinction, making a relational turn, or assuming a methodological behaviourism. We try to clarify these suggestions and to show their highly problematic consequences. While we find the suggestions ultimately unmotivated, the discussion shows that our epistemic condition with respect to the moral status of others does raise problems, and that the human tendency to empathise with things that do not have moral status should be taken seriously—we suggest that it produces a “derived moral status”. Finally, it turns out that there is typically no individual in real AI that could even be said to be the bearer of moral status. Overall, there is no reason to think that robot rights are an issue now

Computational Theory of Mind for Human-Agent Coordination

In everyday life, people often depend on their theory of mind, i.e., their ability to reason about unobservable mental content of others to understand, explain, and predict their behaviour. Many agent-based models have been designed to develop computational theory of mind and analyze its effectiveness in various tasks and settings. However, most existing models are not generic (e.g., only applied in a given setting), not feasible (e.g., require too much information to be processed), or not human-inspired (e.g., do not capture the behavioral heuristics of humans). This hinders their applicability in many settings. Accordingly, we propose a new computational theory of mind, which captures the human decision heuristics of reasoning by abstracting individual beliefs about others. We specifically study computational affinity and show how it can be used in tandem with theory of mind reasoning when designing agent models for human-agent negotiation. We perform two-agent simulations to analyze the role of affinity in getting to agreements when there is a bound on the time to be spent for negotiating. Our results suggest that modeling affinity can ease the negotiation process by decreasing the number of rounds needed for an agreement as well as yield a higher benefit for agents with theory of mind reasoning.</p