Zoekresultaten

Socially responsible funds (SRI)

Posterpresentatie KCO conferentie, 16 november 2015.

Responsible Design of Technical Applications of AI

While the technical application domain seems to be to most established field for AI applications, the field is at the very beginning to identify and implement responsible and fair AI applications. Technical, non-user facing services indirectly model user behavior as a consequence of which unexpected issues of privacy, fairness and lack of autonomy may emerge. There is a need for design methods that take the potential impact of AI systems into account.

Why consumers make a difference - An inquiry into the nature and cuases of buying socially responsible products

The contribution of professional youth work to the development of socially vulnerable youngsters

The central question in this study is how, for whom, and under which conditions professional youth work contributes to the personal development of socially vulnerable youngsters, the reinforcement of their social network, the enhancement of their social participation, and the timely finding of appropriate specialized care services in relation to contextual factors such as life events and the influence of significant others. This research used a multiple case study with a comparative design. During a 12-month period, youth workers (N = 20) participated in group intervision meetings and kept diaries reporting on their actions and the development of the youngsters (N = 23). An analysis of this data revealed four patterns of development of socially vulnerable youngsters in youth work settings. Each pattern consisted of a specific form of multi-methodic action that resulted in a specific outcome. The study also revealed how these processes of development are influenced by important life events and significant others. The findings suggest that youth work contributes to personal development and social participation and thereby may lessen the need for formal social care services.

Prevalence of responsible research practices among academics in The Netherlands [version 2; peer review: 2 approved]

Background: Traditionally, research integrity studies have focused on research misbehaviors and their explanations. Over time, attention has shifted towards preventing questionable research practices and promoting responsible ones. However, data on the prevalence of responsible research practices, especially open methods, open codes and open data and their underlying associative factors, remains scarce.Methods: We conducted a web-based anonymized questionnaire, targeting all academic researchers working at or affiliated to a university or university medical center in The Netherlands, to investigate the prevalence and potential explanatory factors of 11 responsible research practices.Results: A total of 6,813 academics completed the survey, the results of which show that prevalence of responsible practices differs substantially across disciplines and ranks, with 99 percent avoiding plagiarism in their work but less than 50 percent pre-registering a research protocol. Arts and humanities scholars as well as PhD candidates and junior researchers engaged less often in responsible research practices. Publication pressure negatively affected responsible practices, while mentoring, scientific norms subscription and funding pressure stimulated them.Conclusions: Understanding the prevalence of responsible research practices across disciplines and ranks, as well as their associated explanatory factors, can help to systematically address disciplinary- and academic rank-specific obstacles, and thereby facilitate responsible conduct of research.

Responsible and Accountable Data Science

Over the past few years, there has been an explosion of data science as a profession and an academic field. The increasing impact and societal relevance of data science is accompanied by important questions that reflect this development: how can data science become more responsible and accountable while also responding to key challenges such as bias, fairness, and transparency in a rigorous and systematic manner? This Patterns special collection has brought together research and perspective from academia, the public and the private sector, showcasing original research articles and perspectives pertaining to responsible and accountable data science.