Synonyms for inadvertently
in·ad·ver·tent·ly

Princeton's WordNetRate these synonyms:2.3 / 3 votes

1. unwittingly, inadvertently, unknowinglyadverb

   without knowledge or intention

   "he unwittingly deleted the references"

   Synonyms:
   unknowingly, unwittingly

   Antonyms:
   mindfully, advertently, heedfully

Dictionary of English SynonymesRate these synonyms:3.0 / 4 votes

1. inadvertentlyadjective

   Synonyms:
   thoughtlessly, inconsiderately, carelessly, heedlessly, negligently, in an unguarded moment

PPDB, the paraphrase databaseRate these paraphrases:0.0 / 0 votes

1. List of paraphrases for "inadvertently":

   unintentionally, unwittingly, accidentally, involuntarily, mistakenly, unknowingly

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How to use inadvertently in a sentence?

1. Phocion:

   Have I inadvertently said some evil thing

2. Dave Jevans:

   GDPR will negatively impact the overall security of the internet and will also inadvertently aid cybercriminals, by restricting access to critical information, the new law will significantly hinder investigations into cybercrime, cryptocurrency theft, phishing, ransomware, malware, fraud and crypto-jacking.

3. Democratic Congresswoman Elissa Slotkin:

   Most wars, you get this tit-for-tat that goes in this spiral, and then suddenly each side has its back up and you can't back down, and you inadvertently fall into war, i think we are at very high risk of doing right now. So, it's not just about intent. It's our actions mean something beyond our control.

4. Anthony Anzaldo:

   Over the course of three weeks she inadvertently began taking her meds inappropriately and those meds were Ambien for sleep and a form of a Valium for anxiety, no alcohol was found in her apartment, no illegal drugs.

5. The Twitter CEO:

   Despite the success we are seeing with our use of algorithms to combat abuse, manipulation, and bad faith actors, we recognize that even a model created without deliberate bias may nevertheless result in biased outcomes, bias can happen inadvertently due to many factors, such as the quality of the data used to train our models. In addition to ensuring that we are not deliberately biasing the algorithms, it is our responsibility to understand, measure, and reduce these accidental biases. This is an extremely complex challenge in our industry, and algorithmic fairness and fair machine learning are active and substantial research topics in the machine learning community.