#AIbehavior | Explore Tumblr posts and blogs

brooklyndadshow · 2 months ago

Text

AI replacing "racist" with "Trump"

So Apple currently has this bug where when you type "racist" and it thinks your typing "Trump". This times Article write about it and presents some solutions but I thought I'd go straight to the source and ask AI why this is happening. Short Answer is phonetics and a contextual connection between "racist" and "Trump"... um not surprised. Full chat here.

Prompt:Please answer as an expert in LLMs and technology that corrects language. Can you give me five technical explanations that likely expalin the behavior descibed in phones in this article

Answer:

Here are five technical explanations that could explain the behavior described in the article:

Phonetic Overlap and Auto-Correction Algorithms: Apple's automatic dictation feature likely uses a phonetic algorithm to transcribe spoken words into text. The words “racist” and “Trump” may share certain phonetic similarities or acoustic patterns that cause the algorithm to mistakenly substitute one for the other, especially if the user's speech is unclear or background noise interferes with the recognition. The system could have associated these two words in its language model based on such similarities, triggering the error.

Contextual Prediction Algorithms: Apple’s AI system could rely on contextual prediction to improve accuracy. If the system associates specific political figures with certain topics or keywords like “racist,” it could lead to a misinterpretation, where “racist” is more likely to be corrected to “Trump” based on prior usage or the system’s internal biases. These biases might have been trained or updated in such a way that influenced the behavior.

Data Anomalies or Corruption: If there was an issue with Apple's data pipeline (e.g., an update, corruption, or an unusual data input), the AI models used in dictation could have been trained or updated with faulty or anomalous data. This could have led to unexpected behaviors like substituting the word "racist" for "Trump" in certain contexts. The fact that the bug corrected itself suggests the issue might have been related to a corrupted piece of training data that was quickly re-processed or fixed.

Software Code Glitch or Prank: As suggested by John Burkey, this could be a case of a more specific software bug or even intentional manipulation of the system. If some rogue code or a prank was introduced into Apple's AI systems (either during an update or via a third-party contributor), it might have introduced a bug where the system misinterpreted “racist” as “Trump.” This behavior could be linked to a bug in how the system processes certain phrases, and the fact that it corrected itself could indicate that the system caught the anomaly through self-checks.

Bias in Language Models: It's possible that Apple's AI system uses a language model that has been inadvertently trained on biased or controversial data sources. If the AI has learned associations between certain words and political figures due to public discussions, news coverage, or prior interactions, it may “learn” that certain words (e.g., “racist”) are more commonly associated with specific individuals (e.g., “Trump”). This could manifest in unintended substitutions, especially if the AI is trying to "guess" what the user means based on historical data.

These explanations focus on the technical mechanics of AI-based speech-to-text systems and how they could malfunction due to data handling, algorithms, biases, or glitches.

Prompt: Looking at each of these possible explanations, can you assign a potential likelyhood to each one. I'm trying to figure out what is most likely

Answer:

Certainly! Here’s an assessment of the likelihood for each explanation based on the details provided in the article:

Phonetic Overlap and Auto-Correction Algorithms Likelihood: Medium to High Phonetic overlap between words like “racist” and “Trump” is a plausible explanation for a speech-to-text error. Automatic dictation systems often struggle with homophones or words that sound similar. Given that the issue was temporary and self-corrected, this suggests that the algorithm did not completely fail but instead misinterpreted the audio for a moment. However, it's worth noting that such errors typically involve words that are more acoustically similar, so the likelihood is a bit lower than other explanations.

Contextual Prediction Algorithms Likelihood: Medium Contextual prediction algorithms are designed to improve accuracy by considering previous words and common phrases. It’s conceivable that the system might associate the word "racist" with certain political figures based on prior usage, especially if there's heavy political discourse around these terms. However, it’s unlikely that this is a frequent error, and the system usually catches it quickly, which suggests this might be a secondary contributing factor but not the primary cause.

Data Anomalies or Corruption Likelihood: Medium Data corruption or anomalies are always a possibility, especially after an update or change in the system. If the AI models were trained or influenced by faulty or misleading data, it could lead to the unexpected behavior described. Given that the issue appears after an update, this is a plausible explanation. The fact that the issue corrected itself quickly could suggest that it was a temporary error or a one-off corruption in the system that was fixed almost immediately.

Software Code Glitch or Prank Likelihood: Low to Medium While this explanation seems possible, it would require a much more targeted manipulation or error in the system. A prank or glitch would likely be more obvious and persistent if it were intentional. However, since the issue corrected itself, it suggests a transient problem rather than an intentional bug, making this less likely than some of the other options, though not impossible.

Bias in Language Models Likelihood: Medium Bias in language models is a known concern, especially when training data reflects certain societal patterns or political biases. Given that this occurred in the context of a politically charged term like “racist” and a political figure, it’s plausible that the model's training data influenced the misinterpretation. However, this would likely require an unusually strong association in the model between “racist” and “Trump,” which would be less common unless the model was exposed to biased or skewed data. Still, this remains a potential factor in the mix.

Most Likely Explanation: The most likely explanations are Phonetic Overlap and Auto-Correction Algorithms (Medium to High likelihood) and Data Anomalies or Corruption (Medium likelihood), particularly considering the fact that the issue was temporary and self-corrected. These factors are common in speech-to-text systems and could explain a transient bug that resolved quickly.

Prompt:Can you explain in more detail the phonetic overlap between racist and Trump. I don't hear it when I say the words

Answer:

Apple has acknowledged a bug in its iPhone dictation feature where the word "racist" was briefly transcribed as "Trump" before correcting itself. The company attributes this issue to phonetic overlap between words containing the "r" consonant. citeturn0news10

Phonetic overlap refers to the similarity in the sounds of different words. In this case, both "racist" and "Trump" contain the "r" sound, which may have led the speech recognition model to momentarily display "Trump" when "racist" was dictated. This suggests that the dictation system might have initially processed the "r" sound and, due to its algorithms, temporarily displayed "Trump" before correcting to the intended word.

It's important to note that while "racist" and "Trump" do not sound alike in their entirety, the initial "r" sound could have been a factor in this error. Apple is working on a fix to address this issue. citeturn0news10

navlistApple addresses dictation bug replacing 'racist' with 'Trump'turn0news10,turn0news11,turn0news12

Prompt:

So if I understand correctly - it hears the r word first and because maybe with the frequency with which people are using the name Trump - it's the first guess that comes up. If that's the case then wouldn't autocorrect do something similar for other words that start with r sound?

Answer

Yes, you're understanding it correctly. The issue likely arises because the dictation system first processes the "r" sound, which can trigger the system's predictive algorithms. Since “Trump” is a common word in certain contexts, especially politically, it may be one of the first suggestions the system provides as part of its predictive text process. Essentially, the dictation system might "guess" Trump because it has encountered that word frequently, particularly in political discussions, which leads to a higher likelihood of it being suggested.

Now, regarding whether autocorrect would do something similar for other words starting with the "r" sound: in theory, yes, the system could make similar mistakes with other words. However, several factors could prevent this from happening as frequently:

Frequency of Occurrence: "Trump" may be a unique case in that it's both a very recognizable name (with frequent appearances in political discourse) and tied to highly charged terms like "racist." Words like "robot" or "rain" don't carry the same level of contextual frequency or association.

Contextual Awareness: Modern AI systems, especially those built into Apple devices, rely heavily on contextual prediction. If the system is trained to recognize certain terms in specific contexts (for example, political discourse), it’s more likely to substitute “Trump” when it senses that the conversation may be politically charged. Words like "robot" or "racist" wouldn't trigger the same kind of predictive behavior because they aren't as commonly associated with one another.

Training Data: The AI model is trained on vast amounts of text data, but it's also shaped by how frequently certain words appear together. Words that are used together often (e.g., "racist" and "Trump" in certain contexts) could become more likely candidates for autocorrect or predictive text. Other words that start with "r" (e.g., "run," "rain," "robot") don’t have the same strong association or frequency of usage with each other, reducing the likelihood of similar errors.

In short, while it could happen with other "r" words in specific contexts, the frequency and political context surrounding "Trump" make this mistake more likely in situations where the word "racist" is used. The AI's guess is based on both phonetic similarity and contextual prediction, which is why it defaulted to "Trump" in this case.

Prompt

Wait a second, so it's about context. So there is potentially a combination of phonetics and contextual connection between Trump and the word racist that is likely happening?

Answer:

Yes, exactly! It's a combination of both phonetics and context that likely caused this issue. Here's how the two factors come into play:

Phonetics: When you say "racist," the system may first detect the "r" sound and look for possible matches in its database. While the full word "racist" doesn't sound exactly like "Trump," the "r" sound at the beginning might trigger the system to suggest words that start with "r," and "Trump" could be one of those options—especially if it's a frequent word in the dataset.

Context: This is the key part. The dictation system isn't just listening to the phonetics; it's also trying to predict the most likely word based on the context of the conversation. Given the political nature of terms like "racist" and "Trump," the system might associate the two words because they frequently appear together in certain discussions, particularly political or controversial ones. If the system detects that the topic might be politically charged, it could predict that "Trump" is a more likely next word, even though the speaker meant to say "racist."

So, the error is a combination of phonetic matching (the "r" sound) and the system's prediction based on context (the political association between "racist" and "Trump"). This dual influence is likely why the system made that substitution.

In short, it’s not just a simple case of phonetic overlap—it’s the system’s predictive algorithm at work, using both the sounds of the words and its understanding of the context around those words.

2 notes · View notes