Merge pull request microsoft#685 from microsoft/leestott-patch-1

Revise data consumption and ethics discussion in README

Author: leestott

1-Introduction/02-ethics/README.md

@@ -10,7 +10,7 @@ We are all data citizens living in a datafied world.
 
 Market trends tell us that by 2022, 1-in-3 large organizations will buy and sell their data through online [Marketplaces and Exchanges](https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/). As **App Developers**, we'll find it easier and cheaper to integrate data-driven insights and algorithm-driven automation into daily user experiences. But as AI becomes pervasive, we'll also need to understand the potential harms caused by the [weaponization](https://www.youtube.com/watch?v=TQHs8SA1qpk) of such algorithms at scale.
 
-Trends also indicate that we will create and consume over [180 zettabytes](https://www.statista.com/statistics/871513/worldwide-data-created/) of data by 2025. As **Data Scientists**, this gives us unprecedented levels of access to personal data. This means we can build behavioral profiles of users and influence decision-making in ways that create an [illusion of free choice](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) while potentially nudging users towards outcomes we prefer. It also raises broader questions on data privacy and user protections.
+Trends suggest that by 2025, we will generate and consume over [180 zettabytes](https://www.statista.com/statistics/871513/worldwide-data-created/) of data. For **Data Scientists**, this explosion of information provides unprecedented access to personal and behavioral data. With it comes the power to build detailed user profiles and subtly influence decision-making—often in ways that foster an [illusion of free choice](https://www.datasciencecentral.com/the-pareto-set-and-the-paradox-of-choice/). While this can be used to nudge users toward preferred outcomes, it also raises critical questions about data privacy, autonomy, and the ethical boundaries of algorithmic influence.
 
 Data ethics are now _necessary guardrails_ for data science and engineering, helping us minimize potential harms and unintended consequences from our data-driven actions. The [Gartner Hype Cycle for AI](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) identifies relevant trends in digital ethics, responsible AI, and AI governance as key drivers for larger megatrends around _democratization_ and _industrialization_ of AI.