We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Amanda Casari - Thar Be Dragons: Ethical, Legal, and Policy Challenges when Measuring Open Source
Explore the ethical, legal, and policy challenges of measuring open source, including data usage, community trust, and marginalized voices, as experts navigate the complexities of open source ecosystems.
- Ethical challenges in open source research include the need to balance community trust with institutional review board (IRB) requirements, handling conflicting interests and identities within communities, and respecting the autonomy of marginalized populations.
- The lack of a universal legal standard for open source data and licenses poses difficulties for researchers.
- There is no single way to measure open source adoption, and baselines for understanding the ecosystem are lacking.
- Ignoring the people involved and their identities in favor of technical solutions can perpetuate harm.
- Communities may sacrifice autonomy and decision-making power when joining larger organizations.
- The concept of “open” in open source is not just about access, but also about how data is used and who has control over it.
- Pointing to a book or resource is not enough; we need to understand the assumptions and limitations of the information we use.
- Mapping out the open source ecosystem is a daunting task due to the complexity of data and networks involved.
- institutionalize change by implementing policies that support marginalized communities and advocate for regulatory change.
- Disentanglement of public and private interests is crucial in open source research.
- Cultural and legal norms around data use, ownership, and sharing are essential for understanding the dynamics of open source ecosystems.
- Manifestly illegal upload of code removes ambiguity, but need to be aware of the implications on open source ethics.
- Lack of acknowledgment and recognition of the value of marginalized communities and their contributions can lead to harm.
- See GDP’s Phillip’s book on data ethics.