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Do you understand how to manage data ethically (and what’s at stake if you don’t)?

On Point

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Ethical data management

In the news

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Pragmatists and visionaries. The field of artificial intelligence needs “blue-sky visionaries” who offer utopian visions of the future and “muddy-boots pragmatists” who want to overhaul biased systems that may spread lies, violate privacy, or are racist or sexist, says author and computer science professor Ben Shneiderman. Women, nonbinary people, and people of color are among the realists who are working to guarantee that there will be diversity and equality when developing the algorithms running people’s everyday lives. [Scientific American]

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Chatbots with no chill. Machines are getting remarkably smarter and speedier. Large language models (also called foundation models) employ machine learning to analyze huge sets of data and recognize word patterns. These models are becoming ever more powerful, and their use has moved beyond text to include images and video. But the technology is advancing so quickly that experts are asking if tighter controls are needed to prevent potential abuse, such as threatening an individual’s privacy by collecting detailed digital information. [FT]

In a 2021 McKinsey survey, only 27% of some 1,000 respondents said that their data professionals actively check for skewed or biased data during data ingestion.

On McKinsey.com

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Why data ethics matters. With data growing exponentially, every company is a data company, say McKinsey senior partner Kayvaun Rowshankish and colleagues. Every organization must therefore establish its own best practices for managing data. But although most have started to address the operational aspects of data management, fewer have considered the ethical aspects. For example, if data sets are breached, sold without consent, or otherwise mishandled, firms can take a significant hit to their reputations and financial health.

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Five potential challenges. McKinsey spoke with about a dozen global business leaders and data ethics experts and uncovered some common data management traps that leaders and organizations can fall into. These include thinking that data ethics doesn’t apply to your organization; thinking in silos (for instance, believing that hiring a few data scientists has got data ethics covered); or chasing short-term ROI at all costs. Explore all five data management traps and strategies for avoiding them.

See data management traps

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by "McKinsey On Point" <publishing@email.mckinsey.com> - 12:43 - 6 Oct 2022

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Do you understand how to manage data ethically (and what’s at stake if you don’t)?

Ethical data management

In the news

In a 2021 McKinsey survey, only 27% of some 1,000 respondents said that their data professionals actively check for skewed or biased data during data ingestion.

On McKinsey.com