Exploring the changing nature of communication
By: Danielle Boccelli
Tags: NLP, Communication, Data Science, AI, Language

Exploring the changing nature of communication

An Argument for Verified Humans (Part II)

In Part I of this series (linked below), inspired by a quote from a guidebook called Deep Learning with PyTorch (further below), I embarked on a journey toward propositions for (1) verified humans on social media platforms and (2) transparency regarding the use of language models to generate text by discussing the unimportance of the semantic classification of the actions and capabilities of machines. In this article, I will explore the changing nature of communication with human language to build toward these propositions by further discussing the quoted text.

The authors of Deep Learning with PyTorch claim that the question of whether computers are intelligent might not be important, and I believe that much is true, but only with regard to semantics. Specifically, any discussion of the words used to describe what it is computers do when they do the things they do is probably little more than an exercise in pedantry. However, as further noted in the quote, there is an important caveat to consider: In the past, tasks related to human language could be performed only by humans. Herein, I will show that, regardless of whether machines are intelligent, this caveat is non-negligible when considering machine-generated text.

The nature of interpersonal communication

Humans communicate effectively with each other, thanks to their shared mental models of the physical world and social context. These models foster reciprocal trust by making contextual knowledge transparent; they are also crucial for explaining how decision-making unfolds.²

First, let us explore the nature of human communication by considering the example of two humans interacting. Interestingly, even the simplest such encounters are subject to innumerable dynamic factors due to the presence of what I will refer to as internal and external information streams, where an internal stream (e.g., body language, tone) stems from the persons in the conversation, and an external stream (e.g., background noise) stems from the environment. Further, note that, while internal information streams may help to convey the speaker’s message, external information streams may distort it.

(As an aside, vocabulary seems neither an internal stream nor an external stream, acting more as a basis, i.e., vocabulary is to the metric system what spoken utterances are to specific measurements in metric units. However, unlike the metric system, which is standardized so that, for example, one meter is equal to every other meter, a speaker’s vocabulary acts as a semi-standardized basis that is both shared and unique due to the denotative and connotative aspects of words, respectively. As such, vocabulary can both help and hinder the conveyance of the speaker’s meaning.

For example, for two people who share (1) a native language, (2) regional, cultural and generational dialects, and (3) knowledge of a certain industry’s jargon, there may be wide overlaps in both the denotative meanings of and connotative associations with individual words. Thus, in such conversations, the vocabulary serves as a mostly standardized basis, leaving little room for ambiguity of meaning. In contrast, for two people conversing in a lingua franca that neither use with native proficiency, there may be many unshared connotative associations due to foundational knowledge of other cultures and languages and limitations in denotative meaning that lead to malapropism and out-of-vocabulary words, both of which may hinder the exchange of information.)

Despite the inherent complexity of human communication, in general, such encounters proceed within the bounds of certain social norms of human decency that vary based on the pair’s relationship and the context in which the interaction is set (which includes culture and mode of communication). I do not believe that an exhaustive list of such norms could be produced for any given culture — or for any given pair, for that matter — but as an example, consider that, during a conversation, when asked a question, a person will usually try to provide the answer that he or she deems most appropriate, rather than yelling incomprehensible gibberish, because to do so — to yell gibberish—would surely and unnecessarily sully the relationship between the pair, in a way opposite that of saving face.

So upon entering such interactions, each person can assume that the other is likely to adhere to these norms — even if there is no spoken agreement or written requirement to do so. If the norms are not respected, then the offending person can be said to have breeched the trust of the other, which may have certain repercussions or influence future interactions between the pair. In other words, people are held accountable for their behaviors during interpersonal interactions by a (lowercase) social contract that requires certain standards of communication to be maintained.

The curious case of text-based communication

Second, let us consider the specific case of text communication, wherein each person plays both writer and reader in turn. In some ways, this case is even simpler than that described above, as only information presented as written language can be considered by the reader; however, this very simplification also introduces a complication: Because information cannot be carried by any means other than text, the reader can use only text to decipher the writer’s intended meaning. Consequently, the intended meaning — now limited in its vehicles of conveyance — is more likely to be misunderstood by the reader (as often occurs when sarcasm is employed in written conversation). Therefore, during a written conversation, there is an inherent disparity between the writer’s intention and the reader’s interpretation that cannot be ameliorated by auxiliary data sources.

Let us assume that the disparity between interpretation and intention is intuitively acknowledged by those participating in the textual conversation. Through this acknowledgment, which is essentially an acknowledgement of the limitations of written language to convey meaning, a person may become more likely to accept minor incongruencies between the original message and the response provided by the recipient of that message. Consequently, the meaning of the response can be inferred in the context of the original message. (I do not believe this assumption is unrealistic, as I regularly acknowledge the ambiguity of language, and thus the possibility of my incorrect interpretation of written words, when inferring meaning from messages — especially upon seeing how my own words can hit in an unintended way and thereby provoke a misaligned response.)

During a text-based conversation, this intuitive acknowledgement helps to move the conversation forward. For example, in contrast to a face-to-face conversation, wherein one person can ask the other to repeat a previous utterance without significantly delaying the conversation, in a text-based conversation, there is often a non-negligible time lag between messages because the communicators may not be active (i.e., ready and willing to respond) at all times. Thus, to provide a written request for clarification could stall the exchange of information. In addition, it would give the person with the questionable response (A) the chance to speak again without the person requesting clarification (B) providing input on the matter being discussed, i.e., person B requests that person A take another turn, which seems unfair in an adolescent mom-said-take-turns kind of way. Furthermore, because a text conversation produces a log of exchanges that can be used as a context from which to infer meaning, the persons communicating may resist asking for clarification, preferring instead, under risks of stagnation and of being skipped, to reread previous messages.

And so, in text-based conversations, each message often acts as a full, discrete response to the previous message and as the ground truth from which the writer’s meaning can be inferred.

Interactions between computers and unsuspecting humans

Finally, let us consider an interaction similar to that devised by Alan Turing for his Imitation Game⁴, wherein a human player must determine if he or she is interacting with a human or a computer via conversation alone; however, unlike in the Imitation Game, in this scenario, the opponent is definitely a computer, and the human is entirely unaware that a game being played (i.e., the human has no reason to believe a priori that the opponent could be a computer). This situation occurs each time a person engages with a customer service instant messaging (IM) app of ambiguous humanity, but it can also occur on social media, where the automatonity of the bot may be even less obvious.

Let us consider this example from the perspective of the computer, where the computer receives messages (inputs) from and provides responses (outputs) to the person. In this case, the computer has to be able to understand human language as inputs well enough to produce passably human responses as outputs. This task is nontrivial, but depending on the course and duration of the conversation and the savvy of our human player, it is accomplishable with current methods — unlike a task such as a face-to-face conversation, for which a machine would not only have to compose a response but sound like a human, look like a human, and move like a human.

Because the person has no reason to believe that the opponent is a computer, as until very recently, all beings using human language were human, he or she will participate in the conversation according to the standard norms of human decency dictated by the cultural context in which the conversation is taking place. Furthermore, because the conversation is carried out via text, the person will likely grant the computer’s (output) response some leeway when considered in the context of the original (input) statement. Therefore, because the computer’s output is represented by only written language in a text conversation, an unsuspecting person may be easily convinced that a machine is a person who intended to convey the written message and who is accountable for the message according to the norms of the society.

This type of I/O disparity is perhaps best explained by a phenomenon known as the ELIZA effect, which is the tendency of humans to read too much into the messages produced by machines, even if they are aware of the mechanical origins of the text, but especially if they are not. In a toy example of the ELIZA effect, a person who is thanked by an ATM after completing a transaction may actually feel a sense of appreciation. While the text in this example is probably harmless, the ELIZA effect may (1) increase in influence as chatbots become more sophisticated and (2) become more difficult to manage as language models become less interpretable, as supported below:

ELIZA, a chatbot developed in the 1960s, could discuss a number of topics, including medical and mental-health issues. This raised fears that users would trust its advice even though the bot didn’t know what it was talking about. Yet until recently, most chatbots used rule-based AI. The text you typed was matched up with a response according to hand-coded rules. This made the output easier to control. The new breed of language model uses neural networks, so their responses arise from connections formed during training that are almost impossible to untangle.³

Therefore, because tasks related to human communication were formerly performed only by humans, it is possible that many people — especially those who are unaware of the text-generation abilities of modern large language models — can be convinced that a machine-generated text was produced by a human, as when a human reader consumes a text, the text is usually assumed to have been written by a human writer who intended to convey the message in the text and who can be held accountable for the message if it requires elaboration or if it does not adhere to certain social norms.

In a way, when a machine engages in a conversation with a human, it (or, as a proxy, its programmer) acts in bad faith with regard to the above-discussed (lowercase) social contract because, as I will elaborate on in Part III, a machine cannot be held accountable for the messages it produces due to both its inability to have human desires and our inability to assume that a computer-generated text can have a thesis.