The COVID modeling of Imperial College’s epidemiologist, Neil Ferguson, which was the basis for the lockdown of Britain was found to be totally unreliable and ‘buggy mess’ - unreadable, non-reproducible, and sensitive to assumptions and initial conditions. But the impact was huge and could not be undone.
Data scientists are helping organizations determine optimal routes, risk, identity, education level etc. The number, depth, and scope of the decisions is increasing, and so is the impact. This leads to several questions:
- How should data scientists think about their responsibility?
- Do values have a role? Where should they draw the line?
- What can experiences in other domains tell us about the journey ahead?
- How should organizations evolve to carry the burden of decision making?
- What kinds of systems and processes enable more responsible Machine Learning?
Panelist Suchana Seth, Fellow, Berkman Klein Center, Harvard University
Previous session: The previous session was held on 17 June. Summary of the session is available on https://hasgeek.com/fifthelephant/making-data-science-work-3/
About the curators: Venkata Pingali and Indrayudh Ghoshal of Scribble Data have curated this session. Scribble Data is a Bangalore/Toronto startup, active in the data community.
About the series producer: The Fifth Elephant is platform for practitioners working with data (engineering, to application of data science for different use cases) to showcase their work and to collaborate.
For further inquiries, contact 7676332020 or write to fifthelephant.editorial@hasgeek.com
Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more