DIVERSITY 2016 - INTERNATIONAL WORKSHOP ON DIVERSITY-AWARE ARTIFICIAL INTELLIGENCE (DIVERSITY 2016)

Diversity is pervasive in human nature and culture, and is deeply rooted in the variation of natural traits and experience among individuals, the collectives they form, and the environments they inhabit. When humans reason individually, they maintain different representations, conceptualisations, and theories, and apply different rules of inference, learning, and decision making. When they interact with each other to combine their skills or resources, to coordinate their activities, and to resolve conflicts between their individual objectives, they exchange information and knowledge, negotiate and align their individual views, and adapt to each other’s behaviour dynamically. Arguably, diversity is not only a phenomenon that humans have to deal with, but it is also the vehicle for achieving some of the most impressive products of human intelligence.
Artificial Intelligence, on the other hand, has so far largely relied on a certain degree of homogeneity, not necessarily in terms of the components involved in a method or system, but in terms of the process that combines them. While various areas within AI have already developed methods that can cope with and/or exploit diversity to some extent, for example
electronic markets where individual agents have different goals and aim to maximise their own profit,
hybrid robot architectures that involve different layers of representation and reasoning,
knowledge sharing infrastructures where different agents use different domain ontologies, and
machine learning systems that combine different sources of data and/or learning units,
more often than not, these systems still involve a “monolithic”, global approach to integration. This usually derives from a global task context, a common intermediate representation layer, or a global output to be produced by the integrated system.
We believe that there is a huge potential in bringing the insights from work on problems that involve diversity ? like those listed in the examples above ? together to gain a deeper understanding of the phenomenon of diversity, as well as to develop principled methodological approaches that will enable us to better utilise diversity in future AI systems.
Workshop Description
The workshop seeks to explore diversity as a phenomenon that both poses a challenge for AI in terms of dealing with and managing diversity in an intelligent system (or ecosystem of intelligent human and/or artificial agents) and presents an opportunity in terms of leveraging diversity (for example through processes like crowdsourcing and collaborative knowledge production) to achieve human-like (and human-friendly) capabilities in more open-ended, incrementally evolving, and interactive AI systems.
We aim to bring together researchers from different communities that have each addressed diversity in different ways, such as
hierarchical and hybrid inference systems (combining representation and reasoning mechanisms),
semantic web and ontologies (interoperability of information sources, ontology alignment),
non-monotonic and defeasible reasoning (reasoning about conflicting and changing information),
mechanism design and social choice (reaching agreement in the presence of conflict of interest),
language evolution and emergent semantics (evolving shared symbol and concept spaces),
cross-lingual approaches to natural language understanding (integrating different natural languages),
teamwork and collaborative multiagent systems (integrating heterogeneous knowledge/behaviours),
human-AI/human-robot collaboration (aligning agents’ views and objectives with those of humans),
crowdsourcing and human computation (managing diverse contributions of large human collectives).
The workshop will provide an open forum for researchers from these (and other) areas to contribute their insights on diversity in order to develop a shared agenda for the future study of diversity in AI. We welcome submissions on all aspects of diversity, ranging from theoretical foundations to practical applications, case studies, and surveys. The workshop will be heavily discussion-based, with relatively short paper presentations and a focus on formulating key research questions and a longer-term research agenda for the area. To enable high-quality discussion and debate, a key evaluation criterion will be the focus of papers contributed to the workshop on the diversity “angle“ of the research reported. Specifically, papers should clearly identify
what type of diversity or aspects of diversity the reported research investigates or accommodates,
the methods the paper proposes to deal with and/or exploit diversity,
how the proposed method combines and/or exceeds existing diversity-oriented capabilities, and
what key challenges in terms of diversity it leaves open for future research.
Beyond this key requirement, we deliberately impose no restrictions on methodological approach, or maturity of the research. In particular, the workshop aims to be inclusive with regard to the types of diversity considered, including (but not limited to) diversity of representations, algorithms, systems infrastructures, datasets, agent behaviours, skills and capabilities, preferences and objectives, but also users, user populations, cultures, contexts of use, application domains, user interfaces, etc.
Also, in keeping with the Special Topic of ECAI 2016 Artificial Intelligence for Human Values, we particularly invite papers that address the ethics and social impact of AI applications related to diversity, for example addressing issues related to the social dynamics of diversity in systems comprising of humans and artificial agents, the emergence of “digital divides“ and the implications of diversity on the cohesiveness of these systems, diversity-aware accountability and privacy methods, or the potential risks and benefits of diversity-aware AI in terms of promoting human diversity in various domains.