- Define the notion of “responsibility” considering one or several perspectives (data, data management, data analytics, data science). How can it be related or not with ethics, and with notions like diversity, inclusion, discrimination? Some useful references are listed in the bibliography section.
- Would it be pertinent to postulate an “ethics manifesto” concerning the conditions in which data collection, analytics and management are performed and their associated purposes?
- Which alternatives to make databases and data driven- emerging technologies more-inclusive and transformative: more effective, not merely more ‘accurate’ and ‘efficient’?
- How much does it cost to be connected? How can we initiate a discussion on the significance of connected versus disconnected entities—such as individuals, scientific papers, and action groups—in acknowledging knowledge production and conducting data analytics to achieve an algorithmic/quantitative comprehension of phenomena?
- Is there an alternative definition of “connection” that can include disconnected nodes? Should we consistently prioritize the most prominent nodes among marginalized groups for being inclusive when analyzing phenomena and developing data-driven solutions? How do we claim « inclusion » in such cases?
- “Data are not neutral or objective, they are the products of unequal social relations, and this context is essential for conducting accurate, ethical analysis” (D’Ignazio and Klein’s Data Feminism, 2020, ch. 6).
- How can we incorporate new “semantics” into data collection and exploitation processes to ensure accountability for the conditions, biases, and partiality of the data, as well as for the subsequent preparation, engineering, and maintenance tasks of the datasets? First steps have been proposed in how can we move forward?
Global Voices in Data: Navigating Responsible Management and Processing with Diverse Perspectives