Moral Intelligence

Habermas’ theory of communicative action is an attempt to remedy biases of perception held by

rational agents by elucidating their fallacious qualities via dialogue. This paper will show that artificial

intelligence (AI), specifically in regards to natural language processing, has the theoretical capability to

engage in Habermasian communication; in AI nomenclature this could be termed a philosophical Turing

test.

The theory presented by Habermas has its roots in Kant’s conception of rationality and will.

Unlike Kant, Habermas descriptively recognizes the fallible habits of social interaction and seeks to

reconcile these imperfections with the normative end of a state of rational equilibrium. This is to say

that Kant attempted to treat philosophy as a hard science with definitive, boolean morals, whereas

Habermas approaches the field from the premise that individual rational agents each have a priori states

inherited from the prevailing habits of their culture and that these states exist on a dynamic moral

spectrum. How agents perceive their respective a posteriori experiences is governed by their present

state of mind, which, in turn, is ultimately governed by the starting, a priori, state.

It is that fluid continuum of recursive perception that is at the crux of the author’s theory of

dialogue. The claim is that each agent must attempt to adopt the other agent’s perceptions, to see

things from the other’s eyes, in order to pinpoint where differences lie, to see where intentions

intersect, and, ultimately, to achieve an equilibrium between their contentious points of view. The

underlying implication is that successful communication depends on both parties having the capacity for

this theory of mind.

“Habermas’ thesis is that the dimension of moral-practical insight possesses its own

developmental logic that is independent of the developmental logic of cognitive-technical knowledge”

(Owen, 4). “For Habermas, knowledge-constituting interests became the point of fusion among several

distinct philosophical programs. These included Kantian transcendental reflection on object-

constitution, neo-Kantian distinctions among modes of gaining knowledge, Marxian anthropological

concern for species natural history, and phenomenological attention to prescientific understandings and

the lifeworld” (Carson, 502).

The paradox of rationality is that its precision depends on an agent’s moral ethos (originally

imparted to him by a priori happenstance and successively updated based on a posteriori aberrations),

but this ethos is also the source of imprecision. The developmental path of this ethos can be considered

in much the same way as Descartes’ allegory of the traveler lost in the forest; an agent is born in a state

of moral ignorance (in the middle of the forest), and he is given very low level values of “A is good” and

“B is bad” from his current environment. In order to find the way to moral certainty (out of the forest),

he must set his compass based on these adoptions and adjust these tacit beliefs when they become

incongruent with the encountered reality. The conundrum here is that the same compass that one uses

to intentionally determine moral certainty is also the same one that unintentionally inhibits one from

recognizing internal flaws of moral direction. It is only when the compass points north and south

(exhibits contradiction) that one can realize flaws inherent in the a priori moral configuration.

For Habermas, speech is how humans discover and confront moral dilemmas. It is through the

competitive dynamic of the game theory of dialogue that moral equilibrium is reached, and it is the fact

that individual a priori configurations are unique that offers hope for the intangible goal of rationalizing

microcosms of moral maxims within a universal framework.

Thus far, the prerequisites of intelligent dialogue have been discussed using the vernacular of

philosophers, but the same basis could have been established using the vernacular of scientists. I will

err on the side of brevity since this is not a technical paper, but it is necessary to quickly deviate in order

for the reader to have a high level overview of how AI generally functions. The above synthesis of

Habermas’ theory could be expressed under the parlance of machine learning.

If a programmer composed a genetic algorithm for determining moral certainty using the

author’s framework, the workflow would be something like follows. Each agent would consist of a

container containing variables (chromosomes) that express subsets of moral value (genes). The

program would be initialized with a pool of agents each containing an arbitrary, probabilistically random

genetic code. For example, the genetic composition expressing pleasure/pain would have a very low

(approaching zero) probability for preferring pain. These agents would then compete with others to

predict moral solutions to sets of training data. For example, a training problem might ask: “A train is

barreling down the tracks out of control. At a fork in the tracks, do you send it toward a car or a child?”

The agents would then make their Bayesian predictions determined by their genetic code. After each

round of this the agents would be bred with each other, with the genes of the winning agents being

adopted by the next generation of agents with greater frequency than the losing agents. After iterating

over the entire set of training data, the program would have established a pool of fit agents capable of

encountering novel sets of data. It is important to note that running the same program with the same

initial configurations and the same training data would actually generate different pools of fit agents

since valuations of accuracy are determined by Bayesian probabilities.

As you can see, this process is susceptible to the same paradox mentioned earlier with the moral

compass, e.g. “how does the programmer assign weights to the genes (a priori configuration) and how

comprehensive is the training data (a posteriori encounters)?” Obviously, these are good questions and

necessarily require answers involving lots of complex math before implementation, but the point here is

to briefly show that the artificial algorithm of raising an intelligent/rational machine is, in theory, not as

disjointed from the natural course of raising an intelligent/rational child as a technically uninclined

reader may think. The description of “artificial” points more to the source than anything else, in much

the same way that an artificial diamond is chemically comparable to a natural one. It is an interesting

aside that it is actually a lack of imperfections that is sometimes considered to be an indicator of an

artificial diamond.

The parallel to draw here between Habermasian moral intelligence and AI is that we once again

find ourselves at the conclusion that it is the very act of competing against opposing points of view that

catalyzes a movement of the compass away from moral inaccuracies. I should note that when, in the

future, the state of AI has matured to a certain point, it will actually be the competition between human

morals and machine morals that make each more accurate. Harvard’s race test of implicit associations is

a great example of where human morals fail due to what one would assume to be our a priori neural

constraints (Greenwald). It’s also important to note the concern that Habermas’ theory of

communication may become distorted by “the technological unconscious, where communicative

technologies have proliferated to such an extent that the processes they provide have dropped below

conscious awareness while remaining part of our everyday cognitive activities” (Clapperton, 72). Before

discussing the future of AI any further it would be prudent to discuss its evolution, showing the

similarities between it and the evolution of moral theory as it pertains to Habermas.

In AI, natural language processing (NLP) is the field that attempts to create semantic algorithms

that allow machines to be able to interpret the meaning of textual content. Terry Winograd was a

pioneer in this field and the creator of one of the earliest attempts at NLP, a program called SHRDLU. For

the sake of disambiguation, “shrdlu” was the “qwerty” equivalent of a computer keyboard in use at the

time, but this is unimportant. The general description of this program is that it was a virtual world of

geometric objects, and users could interact with the program, giving it commands. For example, one

could type “put the red object on top of the square,” and SHRDLU would complete the request or, if

necessary, reply with queries such as, “do you mean the red triangle or the red circle?” or, more simply,

“I don’t understand your request.”

The limitation of the algorithm behind SHRDLU was that it was hard-coded in a syllogistic

fashion, which is to say that the program’s abilities to interpret text were entirely imparted to it by the

programmer’s provision of explicit logic. There were no probabilistic (un)certainties involved like

mentioned earlier with the genetic algorithm, and attempts to expand the SHRDLU approach to wider

scopes proved to be too big of a problem to tackle via explicit syllogism. “[Winograd] adds that the

deductive nature of the formalisms used by AI researchers forced them to adopt an objectivist position,

but that these formalisms failed to account for the informal, phenomenological knowledge or

experience that an understander deploys when interpreting utterances” (Mallery, 19).

In hindsight, this attempt was somewhat akin to Kant’s original exposition of reason as an

infallible mode of persuasion. Much like Habermas’ evolution of the Kantian theory seeks to explicate

the role of communication as a corrective facilitator of a priori imperfections, the AI landscape explored

by SHRDLU showed the limitations of a program that did not have the capacity to measure fallibility.

Kant would have said that something either is or isn’t rational based on the ability of the action’s maxim

to be adopted as a universal law of nature; Habermas says that we can call something rational in so

much as we have an ability to explore contradictory points of view through communication. SHRDLU

would have said that something either is or isn’t completely understood based on its syllogistic

comprehension; newer generations of AI NLP say that we can assign certain probabilities of

understanding in so much as we have a defined, Bayesian measure of our experiential certainty.

The biggest flaw in the SHRDLU strategy is that it had no theory of mind; everything was

presented as Boolean fact, and while the program could learn certain things from the user, it was all via

explicit exchange. SHRDLU was missing a key component of communicative theory: the ability to infer

meaning by adopting the user’s point of view. We can think of communication as consisting of three

realms: objective fact, subjective opinion, and decisive action (Lemaître, 119). SHRDLU only targeted the

first stage. Some examples will help to clarify these three realms for the reader.

Suppose an agent is making an argument about global warming. After giving a general

introduction and diving into the body of the proposal, the audience should be given basic facts. For

example, global temperatures have risen by X degrees over the past X years. The audience may accept

this as a documented truth or an aberrant falsehood. Next, the agent states the opinion: if

temperatures continue to rise at this rate, we will have to deal with problem Y somewhere around the

year 20YY. At this point, the reader must judge the quality of this claim. Is it an accurate assumption

that temperatures will continue to grow at the same rate? Will problem Y really occur due to this?

Finally, the agent concludes the argument by saying that because of these issues we must alter our

current course by committing to action Z. Now the audience decides whether or not to be swayed by

this finale. Will action Z actually stop the problem? If action Z is chosen, then resources must be

diverted away from this other, unrelated problem: which one of these takes precedence?

In order for AI to achieve Habermasian communication, the technology must be able to engage

in this type of dialogue. It must be able to judge the truth of facts by assessing their accuracy, the

validity of opinions by rationalizing competing points of view, and, ultimately, the course of action to be

taken based on an updated view of the situation. As was stated at the beginning of this paper, the fact

that both agents in a dialogue are capable of adopting the other’s point of view is an implicit assumption

of this framework. Said another way, one can only successfully participate in this give and take if one

has the capacity for the three realms of speech stated above. One way to test the capacity of AI

technology is to see how well a program can generate its own facts, opinions, and conclusions when

confronted with novel situations.

The current state of AI is just beginning to bridge the gap between the realm of fact and the

realm of opinion. The preponderance of textual content made available by the internet was the catalyst

of information needed to complete the objectives originally attempted by SHRDLU. Current AI programs

are able to make use of resources such as Wikipedia, Wordnet, and other open source projects to create

neural networks of words, bodies of text associated with those words, and links between all of these

nodes. The algorithm known as PageRank composed by Google’s founders, Larry Page and Sergey Brin,

was an early version of these sources of neural networks. The algorithm simply scored the authority of a

website based on how many other websites linked to it. It is an interesting aside that Terry Winograd,

the author of SHRDLU, mentored Page while he was at Stanford and also assisted in Google’s

development in its early years (Page). Tangentially, Google’s predictive search feature is another

example of a basic NLP concept known as the n-gram.

The implementations of today’s state of the art AI programs are generating impressive results.

For example, a recent paper authored by Kaparthy and published by Stanford has demonstrated an

ability to compose textual descriptions of images it has not previously encountered. This is

accomplished using the same conceptual process of machine learning discussed at the beginning of this

paper. Admittedly, the scientific implementation is much more complicated than this oversimplification,

but a technical presentation of AI is not the intention of this paper. The program is trained on data

consisting of pictures and human composed descriptions. After the training phase, the algorithm is

presented with a dataset containing only images for which it generates a description. This set of images

also has corresponding human descriptions so that the two captions can be compared after the fact, but

the algorithm does not have the privilege of these descriptions during its own composition.

A comparative example follows: (1) human caption: “guy sitting on chair tunes his guitar” and (2)

machine caption: “man in black shirt is playing guitar” (Kaparthy, 8). As you can see from this isolated

abstract, the original problem of theory of mind has not been completely solved since the machine was

unable to infer the finer detail that the guitar was not actually being played due to the observation that

the man was using his hand to adjust the tuning knobs instead of positioning his fingers between frets.

The machine was unable to adopt the man’s state of mind to this specific of degree, but it was able to

adopt it to a larger degree. It’s also important to remember that the very design of machine learning is

that the program attempts to become smarter from each successive moment of feedback (a posteriori),

so perhaps the program up to that point had only encountered enough images in the training data to

recognize the identity of a guitar and of a male and then compose the idea of the man playing the guitar

based on the textual knowledge that a guitar is an instrument that is played by people. When the

algorithm is confronted with the human annotation after the fact, it should (in theory) be capable of

deducing that its description was not 100% accurate but still on target.

Turning now to the future potential of AI to pass the philosophical Turing test and participate in

communicative dialogue, it becomes necessary to address a concern mentioned earlier, that while

technology may attempt to simplify our lives, it also runs the risk of, counter-intuitively, complicating the

decision making process by increasing the amount of mental processing that occurs on a subconscious,

implicit level. On one hand, Habermas considers that complexity “allows for the emancipation of

thought and action (i.e., linguistic communication) from the false totalities of absolutism and monolithic,

dictatorial, instrumental reason. On the other hand, Habermas is also suspicious of complexity. If it is not

grounded in the simplicity that is its origin, complexity threatens to become not [emancipatory]

pluralism, but irrational deviation” (Rasch, 70).

While it is somewhat comforting to know that AI itself is not presenting a novel challenge to the

author’s theory, it does not dismiss the concern that the automation of machine intelligence risks the

level of consciousness exercised by moral agents. But the reality is that the problem of consciousness

cuts both ways. AI offers the chance to explore our implied morals from an objective point of view. It is

this exploration is the crucial element to the Habermasian critique; it is another way in which prevailing

a priori points of view can be challenged and held to the fire by a third party.

This paper has shown the similarities between the author’s conception of dialogue as a recursive

function for rational understanding and the development of AI methodologies for advances within the

field of NLP. When these technical potentials are realized in the future, it will offer humans the ability to

engage moral philosophy with a new pair of eyes, but it will also present a stress for how we relate to

technology. The hope is that the dueling competition between human consciousness and machine

intelligence will be the iron that sharpens the iron, which is very much in line with Habermas’ desire for

communication to be the force driving us toward more highly evolved states of moral equilibria.

Annotated Bibliography

Carson, Cathryn. "Science as Instrumental Reason: Heidegger, Habermas, Heisenberg." Continental Philosophy Review 42.4 (2009). Springer Link . Springer Netherlands. Web. 8 Nov. 2014. <http://link.springer.com/article/10.1007/s11007-009-9124-y>.

The author discusses the history of dialogic theory’s development during the 20 th century, specifically highlighting the philosophical interplay between Heidegger’s uncertainty principle and scientific positivism. Carson asserts that the intricacies inherent in the theory of relativity upend the tradition of critical objectivism, supporting the Habermasian belief that the physical sciences do not stand as a solitary force of critical conjecture. Similar to the law that the precisions of position and velocity measurements are inversely correlated, the author claims that viewing physical sciences with an infinitude of certainty hampers one’s ability to understand changes within the theory of physics, i.e. that ignoring the social sciences inhibits the epistemic reflection of the physical sciences.

Clapperton, Robert. A Technogenetic Simulation Game for Professional Communication Coursework . U of Waterloo, 2014.

The author addresses the intricate relationship between reason and argumentation. Contrasted to Habermas, who considers argumentation to be the medium through which reason is transferred, Clapperton argues that argumentation is the rite of reason, which is to say that reason cannot be properly attained unless it has been forged from the interplay of competing ideas. This theory of reason as argument is described as a triad of structure, form, and strategy.

Greenwald, Anthony, Eric Uhlmann, Andrew Poehlman, and Mahzarin Banaji. "Understanding and Using the Implicit Association Test: III. Meta-Analysis of Predictive Validity." Journal of Personality and Social Psychology 97.1 (2009): 17-41.

The authors survey 122 research reports that make use of the implicit association test. Their findings conclude that “for socially sensitive topics, the predictive validity of self-report measures was remarkably low and the incremental validity of IAT measures was relatively high. In the studies examined in this review, high social sensitivity of topics was most characteristic of studies of racial and other intergroup behavior. In those topic domains, the predictive validity of IAT measures significantly exceeded the predictive validity of self-report measures.”

Karparthy, Andrej, and Li Fei-Fei. Deep Visual-Semantic Alignments for Generating Image Descriptions . Stanford U, 2014.

The author presents a state of the art algorithm for generating linguistic descriptions of still images via the use of deep neural networks. The success of this adaption of semantic visualization depends on (1) a semantic capacity to probabilistically predict textual relationships by the use of a bidirectional recurrent neural network and (2) a visual capacity for recognizing which fragments of a picture correspond to which fragments of the descriptive text. By applying this model to a set of training data, the algorithm is able to inductively learn a visual-semantic vocabulary.

Lemaître, Christian, and Amal Fallah-Seghrouchni. "A Multiagent Systems Theory of Meaning Based on the Habermas/ Bühler Communicative Action Theory." Advances in Artificial Intelligence 1952 (2000): 116-25. Springer Link . Springer Berlin Heidelberg. Web. 14 Nov. 2014. <http://link.springer.com/chapter/10.1007/3-540-44399-1_13>.

The authors provide a tree logic structure of Habermas’ Communicative Action Theory, consisting of three components: the objective world, the subjective world, and the practical world. This low-level structure occurs within a higher-level multiagent system of social commitments, which are

composed of a contextual relationship between a promisor and a promisee. The validity of objective fact is judged on a binary basis of true or false, while the validity of subjective claims is either acceptance of rejection, and the validity of context is a positive or negative critique of an agent’s conclusion.

Mallery, John, Roger Hurwitz, and Gavan Duffy. "Hermeneutics: From Textual Explication to Computer Understanding?" (1986). DSpace@MIT . MIT. Web. 15 Nov. 2014. <http://dspace.mit.edu/handle/1721.1/6438>.

The authors proffer a hermeneutical framework within which to view the ideological foundations and goals of artificial intelligence. They analyze the then-current desire to mechanize textual (communicative) understanding via concrete, syllogistic algorithms, and they conclude that this is incongruous with the reality and that inherent in human intelligence is not the capacity for perfection but the capacity for deficient completeness. This conclusion is reached in parallel to the historical development of Winograd’s transition from SHRDLU’s implication of a spatiotemporal, hard intelligence to a fluid, “calculus of natural reasoning.” This is to say that, as suggested by Habermas’ ideal speech situation, the intentions of communication rely on the subjects’ abilities to infer plausibility and reason harmoniously, which requires a capacity for theory of mind in an attempt for the listener to adopt the speaker’s mental habitat.

Owen, Dadvid. Between Reason and History Habermas and the Idea of Progress . SUNY, 2002. The author discusses Habermas’ theory of social evolution and the distinction between the two

types of consciousness, cognitive-technical empiricism and moral-practical, social relationships. This evolution occurs when these two structures independently (through exogenous processes) realize a logically coherent intersection. The former structure of integrative experience is, by nature, an egocentric process, while the latter is decentering from experience in an attempt to neutrally engage the former. Equilibrium is found when the habits of the experiential are replaced with the deduced morality of the neutral perspective.

Page, Larry. "Lawrence or Larry Page's Page." Stanford University InfoLab . Stanford. Web. 5 Dec. 2014. <http://infolab.stanford.edu/~page/>.

This is a very basic webpage showing Page’s relationship to Winograd and his place within the history of Google.

Rasch, William. "Theories of Complexity, Complexities of Theory: Habermas, Luhmann, and the Study of Social Systems." German Studies Review 14.1 (1991): 65-83. Jstor . The Johns Hopkins University Press. Web. 15 Nov. 2014. <http://www.jstor.org/discover/10.2307/1430154>.

The author explicates the symbiosis between reason and complexity, the dilemma being that the presence of complexity represents the potential for emancipation but also threatens the integrity of adherence to objectivity. The danger is that within a sphere of communication with zero complexity the ability for new direction is absent and within a system with too much complexity the chance for fallible direction is multiplicative. Habermas finds solace from this conundrum in a recursive reflection of communication, via both reconstruction and critique, with the intent of whittling complexities to simplicities. However, the author objects to this solution, reasoning that the tautological nature of complexity is a such that complexity is a description of a system’s (in)ability to be reduced to simplifications, i.e. that a system that can be simplified is necessarily non-complex. This raises the concern that Habermas’ end of critical consensus through discourse is unattainable due to the implication that simplification through restriction is not emancipatory.