Has the time come for a professional data science body to create an accreditation system?
Being a data scientist involves a wide range of diverse skills, such as mathematics, statistics, programming, business knowledge, machine learning, and on and on. And there are a number of routes into the discipline, from university courses, on-the-job training, self-taught & online courses, and data-intense PhDs.
This jumble of skills and routes means no two data scientists look the same. Combined with the variety of domains that data scientists work in, and the innovative nature of the role, it is hard to definitively list the required skills of a data scientist.
Does this ambiguity create a problem? Would an accreditation system help, or would it stifle innovation? And who could possibly be the gatekeeper?
To what degree are data scientists accountable for the impact of their work?
The ethical significance of data science, and the implications for industries and the wider public, is constantly evolving. As data science methods become more common, there are both opportunities and challenges for individuals working in data science (‘practitioners’).
For example, managing privacy, fairness and bias can be difficult and complex when using algorithmic methods. Additionally, public perceptions are still developing around many aspects of data science, including the use of artificial intelligence (AI) in systems and decision making, and ‘big data’ sources about people, such as social media and mobile phone data.
Given this ethical maze, how should a data scientist regard their responsibility to wider society?
Join the discussion with our panel - confirmed so far:
- Danielle Belgrave - Principal Researcher, Machine Learning, Microsoft Research
- David Hoyle – Senior Research Data Scientist, Dunnhumby
- Giles Pavey – Global Director of Data Science, Unilever
- Graeme Phillipson – Senior Research & Development Engineer, BBC
- Magnus Rattray – Director, Institute of Data Science & AI, University of Manchester
The event will be introduced by Jim Weatherall (VP, Data Sciences & AI, AstraZeneca and Vice Chair of Royal Statistical Society Data Science