diff --git a/03_ethics.ipynb b/03_ethics.ipynb
index 8438501..947d738 100644
--- a/03_ethics.ipynb
+++ b/03_ethics.ipynb
@@ -286,7 +286,7 @@
     "\n",
     "An additional reason why recourse is so necessary, is because data often contains errors. Mechanisms for audits and error-correction are crucial. A database of suspected gang members maintained by California law enforcement officials was found to be full of errors, including 42 babies who had been added to the database when they were less than 1 year old (28 of whom were marked as “admitting to being gang members”). In this case, there was no process in place for correcting mistakes or removing people once they’ve been added. Another example is the US credit report system; in a large-scale study of credit reports by the FTC (Federal Trade Commission) in 2012, it was found that 26% of consumers had at least one mistake in their files, and 5% had errors that could be devastating.  Yet, the process of getting such errors corrected is incredibly slow and opaque. When public-radio reporter Bobby Allyn discovered that he was erroneously listed as having a firearms conviction, it took him \"more than a dozen phone calls, the handiwork of a county court clerk and six weeks to solve the problem. And that was only after I contacted the company’s communications department as a journalist.\" (as covered in the article [How the careless errors of credit reporting agencies are ruining people’s lives](https://www.washingtonpost.com/posteverything/wp/2016/09/08/how-the-careless-errors-of-credit-reporting-agencies-are-ruining-peoples-lives/))\n",
     "\n",
-    "As machine learning practitioners, we do not always think of it as our responsibility to understand how our algorithms and up being implemented in practice. But we need to."
+    "As machine learning practitioners, we do not always think of it as our responsibility to understand how our algorithms end up being implemented in practice. But we need to."
    ]
   },
   {
@@ -363,7 +363,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It is extremely important to keep in mind this kind of behavior can happen, and to either anticipate a feedback loop or take positive action to break it when you can the first signs of it in your own projects. Another thing to keep in mind is *bias*, which, as we discussed in the previous chapter, can interact with feedback loops in very troublesome ways."
+    "It is extremely important to keep in mind this kind of behavior can happen, and to either anticipate a feedback loop or take positive action to break it when you can see the first signs of it in your own projects. Another thing to keep in mind is *bias*, which, as we discussed in the previous chapter, can interact with feedback loops in very troublesome ways."
    ]
   },
   {