The workshop will allow researchers and practitioners from various research areas -- data mining, machine learning, digital ethics, applied statistics, sociology, psychology, journalism, linguistics -- to share their experiences on studying, designing, and developing cutting-edge methodologies addressing societal challenges and derive policies, strategies, and solutions to support the government bodies to derive guidelines to create more inclusive, innovative and reflective societies. The methodological approach should consider any aspect of the diversity in a community (e.g., gender, special needs, age, ethnic and religious origin) to collect, study, and analyze data with the final aim at, effectively supporting policymakers in the decision process to promote and guarantee equality, inclusion, and well-being.
We live in challenging times, but the complexity of our daily activities needs diversity in skills, knowledge, approaches to be interpreted: the more the context is diversified, the more it needs to be managed by a society capable of understanding and governing it.
Developing an inclusive society with a strong respect for diversity is a challenging issue but an urgent topic to be addressed to build more reflexive societies. Strategic, innovative, and human-readable methodologies are needed to collect and analyze data to enhance awareness about the societal phenomena, guarantee equality and define ad-hoc policies to fill the gap between the present and an equity society promoting inclusion and well-being policies.
Data science is an interdisciplinary field about scientific processes, methodologies, and systems to extract valuable knowledge or insights from data in various forms. From the perspective of societal challenges, data can be analyzed using data mining, machine learning, data analysis, and statistics, optimizing processes and maximizing knowledge exploitation in societies with positive effects on enhancing talents and a sense of belonging.
On the other hand, data science algorithms are powerful and necessary tools behind a large part of the information we use every day; rendering them more transparent (i.e., human-readable relationships among the input and the algorithm's outcomes, understanding of the inner functionalities of black-box models) should improve their usability in various areas, not least because discrimination and biases have to be avoided. Innovative approaches should be devised to make the algorithm software human-readable and usable by both analysts and end-users to increase transparency and user control significantly. We believe that transparent algorithms will have a high impact shortly because they should improve algorithm usability in various application domains and support the definition of policies to build better societies.
The workshop aims to allow academics and practitioners from various research areas to share their experiences studying, analyzing, designing, and developing innovative methodologies centered on societal challenges to derive insights, characterize sociological phenomena, and support policymakers in promoting more inclusive reflexive and innovative societies.
Industrial implementations of explainable/transparent data algorithm applications, design and deployment experience reports on various issues raising data transparency projects, and avoiding bias and discrimination, are particularly welcome. We call for research and experience papers and demonstration proposals covering any aspect of data algorithmic transparency and accountability in real-life applications, fundamental properties to avoid bias and discrimination.
Topics of interest
We invite the submission of high-quality manuscripts reporting relevant and work-in-progress research studies addressing various aspects of data science and analytic for equality, inclusion and well-being challenges. Contributions should be of interest to a large and varied cross-disciplinary audience of researchers and practitioners involved or interested from different perspectives in societal challenges. The workshop welcomes submissions of methodological, experimental, technical papers, application papers, and papers on experiences in real-life application settings addressing – though not limited to – the following topics:
- Concepts, methodologies, solutions for sensing, modeling, managing, mining, and understanding people behavior and activity
- Algorithmic accountability
- Explainable algorithms
- Data analytics and artificial intelligent algorithms
- Recommendations for people and analysts
- Recommendations for policymakers
- Intelligent systems to enhance user awareness of algorithm decisions
- Ethical issues of data collection, storage, and exchange
- Interactive query refinement and processing
- Innovative solutions for exploring, analyzing and visualizing data
- User-controlled algorithms for data integration, cleaning, and analysis
- Perception-aware data processing and analytics
- Database systems designed for highly interactive applications
- Crowd-powered data infrastructure.
In one of – though not limited to – the following application scenarios:
- Private and public employment
- Healthcare applications
- Public safety and security
- Citizens' mobility and transportation
- Urban economy and urban environments
- Data journalism
- Covid-19 pandemic
- Gender Equality Plan
- Inclusive Language
- Social services, unemployment, and homelessness
- Economic, social, and personal development
- Government transparency
- User-generated content (like tweets, micro-blog, check-ins, photos)
addressing – though not limited to – the following societal challenges:
- Gender gap
- Balance of life and work times
- Special needs,
- Ethnic and religious origin
- Topics aligned with the UN development goals: https://www.un.org/sustainabledevelopment/sustainable-development-goals/