Data science is a quickly growing technology as organizations of all sizes embrace artificial intelligence (AI) and machine learning (ML), and along with that growth has come no shortage of concerns.
Copyright: veturebeats.com – “What are data scientists’ biggest concerns? The 2022 State of Data Science report has the answers”
The 2022 State of Data Science report, released today by data science platform vendor Anaconda, identifies key trends and concerns for data scientists and the organizations that employ them. Among the trends identified by Anaconda is the fact that the open-source Python programming language continues to dominate the data science landscape.
Among the key concerns identified in the report was the barriers to adoption of data science overall.
“One area that did surprise me was that 2/3 of respondents felt that the biggest barrier to successful enterprise adoption of data science is insufficient investment in data engineering and tooling to enable production of good models,” Peter Wang, Anaconda CEO and cofounder, told VentureBeat. “We’ve always known that data science and machine learning can suffer from poor models and inputs, but it was interesting to see our respondents rank this even higher than the talent/headcount gap.”
AI bias in data science is far from a solved issue
The issue of AI bias is one that is well known for data science. What isn’t as well known is exactly what organizations are actually doing to combat the issue.
Last year, Anaconda’s 2021 State of Data Science found that 40% of orgs were planning or doing something to help with the issue of bias. Anaconda didn’t ask the same question this year, opting instead to take a different approach.
“Instead of asking if organizations were planning to address bias, we wanted to look at the specific steps organizations are now taking to ensure fairness and mitigate bias,” Wang said. “We realized from our findings last year that organizations had plans in the works to address this, so for 2022, we wanted to look into what actions they took, if any, and where their priorities are.”[…]
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Source: SwissCognitive