Determinism and Social Paradoxes of Explainable Artificial
Intelligence ( XAI )CBEL Report Volume 5, Issue 2

Regular Article

Determinism and Social Paradoxes of Explainable Artificial Intelligence ( XAI )Soichiro Toda 1
Eisuke Nakazawa 2

Abstract
The core philosophical issue of explainable artificial intelligence (XAI) is the philosophical implication and clarifying
the meaning of the term explanation. We call the process of connecting the cognitive suspension that exists between
artificial intelligence (AI) and humans individually “explanation.” If, through explanation, an AI is recognized as amoral agency, then and only then is the AI allowed to act in a way that satisfies the person as an XAI. AIs and humans
have different operating systems to begin with. However, the condition that an AI is a moral actor, which equals a
decision maker, is crucial for the AI to be recognized as an XAI. Furthermore, an XAI as a moral actor eliminates theparadox of infinite regress of explanations in the XAI argument. As an aid to this understanding, we examine therequirements for the social implementation of XAI, using the ethically interesting case of triage as a starting point.
Then, we highlight the practical/philosophical paradox that cannot be resolved: can XAI create a story for explanation? We also discuss the trade-off between “accuracy” and “humanity” provide further topics for future research.
Keywords: explainable artificial intelligence, AI, philosophy, moral agency

1. Introduction

opponent by scoring the next predicted move. Certainly,News of computers that use artificial intelligence (AI)the scoring must be within the range of human under-technology, such as Alpha GO, defeating humans instanding to constitute an explanation. According to Ha-

games has been capturing much attention. However, thegras [1], the development of explainable artificial intel-

following considerations are also true: What if the AI

ligence (XAI) progresses through three main methodol-

suddenly played a Go stone in the corner in its firstogies: 1) deep learning; 2) interpretive models that in-

move? Questions such as, “Why did it make such an un-

corporate causality; and 3) deductive methods that makeorthodox move? There must be some secret, but we haveblack-box models immediately explainable.

no idea what it is...,” would abound.

The explanatory potential of AI contributes to the ad-

Nevertheless, Alpha GO could provide answers tovancement of science. An example is the elucidation of

those questions. For example, Alpha GO may show that

protein structures in biochemistry and physical chemis-

this type of move is the most efficient way to beat thetry: the structure of a protein, which can be formed from

Department of Philosophy, Tohoku University Graduate School of Arts and Letters,
Corresponding Author (E-mail: soichirotoda@gmail.com)
2 Department of Biomedical Ethics, University of Tokyo Faculty of Medicine
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Soichiro Toda, Eisuke Nakazawa

20 different amino acids, has been predicted and contin-

remarkable. An example is the monitoring of the rela-

uously updated using deep learning. However, when antionship between diagnosis, medication, and the pa-

evolutionary interpretation of that structure is required,tient's daily life in psychiatry. It has been reported thatthe black-box nature of AI provides no answer; the AI

in psychiatry (especially in the diagnosis and subse-

merely presents a random catalog of protein structures.

quent treatment of schizophrenic patients), treatment ac-

Undoubtedly, a better understanding of protein structure

cording to situational judgment based on outpatient re-can be achieved through coevolutionary and interpretive

ports has limits [5].

explanations [2] in the same family of protein structures

Therefore, moderation theory is the general responsethan through a mere catalog. Moreover, in 2022, Deep-to the “who does it, toward whom, and in what manner,”

Mind, an AI research company, announced that deepthat is, “the appropriate XAI (and its analyst), to the ap-

learning has revealed nearly all structure types that pro-

propriate person, in an appropriate manner.” However,teins can theoretically take (https://alphafold.ebi.ac.uk).

the question as to who will make the clinical decision

A database of more than 200 million of these proteins(especially end-of-life decisions) remains open. Fur-has been made available to the public at no cost, and if

thermore, “why” generates another level of “because,”

the abovementioned explanations were added, researchand that “because” generates another “because,” with-would accelerate further.

out even making philosophy of action. In this infinite
regress, another very important problem system is

2. Who Is Explaining, to Whom, and How ?
There is a growing demand for XAI that can provide

where to break the chain of reasons in a humanly understandable way [6].

explanations. The term folk psychological XAI might

Therefore, let us consider two contexts with easy toalso be applied here because it is the words and symbols

analyze but ones we rarely pay attention to them. The

used by humans that persuade humans. Thus, we use

former example is related to human emotion, and the

XAI to refer to technology that can be explained andlatter is related to degrees of explanations.

understood in human language [3]. Importantly, such

The first is related to medical clinical practice. We

XAI also has significant implications for social theories

will list typical diseases (but unrealistic in the actualof science and technology (e.g., [4]), and it will provide

medical practice), but please assume that the following

benefits as AI technology advances. Its advantages

description of the nature of the disease is the correct one

should be fully exploited in fields including medicine,derived by XAI.

disaster response, sports, agriculture, and bioscience.
As illustrated earlier, human lives may one day be in

Ms. A has breast cancer and continues to receive outpa-

the hands of XAI, especially with the use of big data,

tient care but is at a crossroads. According to medical AI,

which continues to attract attention. Although still in thealthough mastectomy reduces the risk of death (a higher

trial stages, the application of XAI to medical big data

5-year survival probability), profiling (AI analysis) of

and the elaboration of its procedures of clinical trials are

Ms. A’s constitution and genes shows that 7 or 8 years

48Determinism and Social Paradoxes of Explainable Artificial
Intelligence ( XAI )CBEL Report Volume 5, Issue 2

after the resection, she is very likely to develop liverdataset that leads to life-and-death issues, which havecancer (for an unknown reason). The probability of liver

been primarily addressed by bioethics. A light XAI, pre-cancer is very high 7 to 8 years after the resection (forcisely because it is light, forces the reconsideration ofan unknown reason). Conversely, treatment with breast

the much-discussed catchphrase of bioethics at the timeconservation has a lower 5-year survival probabilityof its emergence. That is, with AI, we will "decide who

than resection but a very low chance of liver cancer. XAI

dies and who lives.”

has made the decision that mastectomy is the best option
because she must survive first.

The second context is the explanation of a phenomenon of a familiar word, “burning.” Why do flames burnin the way they burn: in some places with a blue-like

How should the XAI be evaluated for making such acolor, in some places with a slightly reddish color, indecision? The most appropriate explanation may be thatsome places without water, sometimes in an orderly

the patient needs to survive first. Most patients would

manner, and sometimes as a disordered flame? This

probably agree with this explanation. If XAI and AI pro-“why” of the phenomenon of combustion is explainedpose the same rationale for mastectomy, what is the dif-late in the education process, usually in high school (of-ference that XAI makes with respect to AI? It is, afterten, in the senior year in high school). Furthermore, we

all, “Ms. A’s narrative” that is necessary for an “ade-cannot explain the phenomenon of combustion withquate explanation,” even if the same conclusion ismathematical or chemical formulas. To explain it, an un-reached. Certainly, there is an upper limit to the amountderstanding of the concept of energy and of chemicalof data that can be input for this task (implementation ofequations of dozens of steps is necessary. If XAI tried to

Mr. A’s narrative into the AI), so it is important how

explain turbulent conflagration [7], many people wouldmuch data is input. However, we must ask what is meant

not care. Furthermore, it could be even said that no ex-by the term “show of skill?”

planation is necessary, except for engineers, because so-

Here, we would like to introduce a distinction be-

ciety operates without an explanation. It might be said

tween “heavy XAI” and “light XAI.” A heavy XAI is anthat XAI is the one that provides the necessary explana-

AI based on a dataset of information that corresponds totion for theoretical and practical parties in this way.

the formation of Ms. A’s narrative by collecting as muchinformation about Ms. A as possible. A light XAI, by

3. Should We Follow AI Triage Decisions?

contrast, is used when Ms. A's narrative is not necessary.

In this section, we consider triage, which focuses on

The triage problem addressed in the next section will

lifesaving situations, as the topic of discussion. Usually,

apply light XAI.

triage refers to a series of medical actions in large-scale

However, there are many situations in which a lightemergency medical care settings, such as in the event of

XAI will deal with “heavy” decisions and the presenta-

a disaster, in which treatment priorities are determinedtion of reasons for those decisions. The problem of tri-

by assigning a color code to each patient (e.g., blackage is that a light XAI must make decisions from a smallmarks are placed on patients who are unlikely to49

CBEL Report Volume 5, Issue 2

Soichiro Toda, Eisuke Nakazawa

survive). In such a setting, what type of triage algorithm

mention information b instead of information a as theshould be followed? In the first place, what is the philo-reason for the action? Therein lies the black-box prob-

sophical and practical difference between light XAI andlem of XAI.

heavy XAI in triage? Is it merely “degrees of explanations?”

4. Triage, Utilitarianism, and XAI as Moral AgentIn Section 2, we proposed that it is the decision mak-At the root of triage is saving lives and differentiation

er's narrative that determines the lightness of the XAI;

based on utilitarianism. However, do medical practi-

in XAI triage, there is a short time for decision-making.

tioners trust medical AI or XAI enough to differentiate

It is important to note that the shortness of time to decidepatients [10]? A trustworthy AI can save many lives on

is not incompatible with the need for extensive and “de-a utilitarian basis and must be mentioned with XAI [11].

liberative” explanations. That is an algorithmic version

AI agents (robots) are often considered personalities

of cognitive System 1 and System 2 we human have. If

that can be trusted and are moral actors in some contexts

possible, as much of the patient's background as possi-[9]; chatbots are a good example of this [12]. If chatbots

ble should be known; otherwise, the very fact that theare accepted as moral agents, a type of intimacy must be

patient's life was not saved because of a “hasty” decision

assumed [13,14] that includes laughing at each other,made by the AI can be perceived as a defeat for the XAI

being sad, and logically convincing each other. The

by the disaster whether the decisions made are by lightchatbot's feelings are considered and predicted to estab-

XAI or heavy XAI.

lish a conversation with it. Moreover, it can be called a

Even more troubling is the fact that most people will

hypothesized person or a hypothesized moral agency.

ignore the justification for “triage due to lack of infor-

These relationships are referred to as a new theory ofmation” as described above [8]. The autonomy of XAI

mind for human–AI [15].

(the power of XAI in this area will only grow stronger)

However, it would be difficult to agree with the con-is now eroding the proposition that "the final judgment

clusion that the XAI is also a moral actor from the dis-of general responsibility and rational justification is usu-

cussion of intimacy, which is accepted to a certain ex-ally reserved solely for humans [9]. Furthermore, XAI

tent, and an epistemological gap exists [16]. Consider,

explanations contain sufficient content to raise the so-for example, a human being who keeps kicking a robot

cial question of who is really making the decisions? Hu-that seems to be working with a certain purpose (usuallymans should fill in the eroded (gapped) propositions

walking). The robot is given a certain degree of assumed

with a variety of rationalizations (this very task is expla-

personhood because humans “feel sorry” for thenation), but how humans, not XAI, will fill in the gaps

“abused” robot. However, since it is not expected thatwill be a challenge for the future. At the very least an

the robot will proactively act or take revenge on the per-algorithm in AI should be implemented that explains

son who kicked it in a way different from violence to thewhich information is necessary and sufficient for an ex-real humans, it would not easy necessarily to say thatplanation in a short temporal time window. Why did you

the robot is a moral agent. (See the following article by50

Determinism and Social Paradoxes of Explainable Artificial
Intelligence ( XAI )CBEL Report Volume 5, Issue 2

Reuters. https://www.reuters.com/video/watch/idRCV00696E. )itself, especially when it comes to human’s nature argu-

It would also be informative, both in developmental

ment, but simply refers to the ability of XAI to produce

psychology and in XAI research, to observe how babies

out-puts in response to its surroundings; the outputs ap-

or apes react when shown videos similar to this one. To

pear to be calculated from the computability domain in

further the argument, intimacy is what XAI should learn.

computer science.

A distinction needs to be made whether intimacy refers

In sum, It is worth noting that XAI provides explana-

simply to a friendship or is more rooted in a sexual re-tions in different ways depending on the explanatory

lationship [17,18]. If the epistemological gap can bemethod or the rules applied to the input data [21,22].

filled, then the explanation of the XAI will be exactly
the explanation we seek from the XAI.

We will attempt to position XAI based on these “limitations.” By saying “limitations”, we indicate the levels

The next exploration focuses on the black-box natureof input and following limited typical levels of outputs.

of XAI: if XAI explanations are pragmatically familiar

Let us begin by applying XAI to a Chinese room in theto society, is it necessary to dismantle the black box?

Searle style. We do not follow this thought experiment

Conversely, is it necessary to clarify at what point in thefrom its foundation, but the unchanging assumption ischain of reasoning the XAI in question used “because”

that the people in the room do not understand Chinese[19]? In the next section, we argue that the black box ofat all. This is equivalent to someone who wants to use a

XAI is by no means a Searle-style black box [20] but

spreadsheet but knows nothing about the basics of pro-that to make the infinite regress of reasons unquestion-

gramming or computer “grammar.” The Chinese input

able in a black box is to violate the autonomy required

into the room is output as Chinese (whatever the in-of XAI.

put/output is, the person in the room will not recognize
it as Chinese) according to a vast manual. The person in

5. Autonomy from the Perspective of XAI: Various

the room becomes a Chinese speaker based on igno-

XAI and Conditions for Social Demandrance. Now, let us show with an example that the series

The concept of black box (i.e., we cannot understandof operations performed in the "traditional room" areand check every function or algorithm in AI therefore

powerless against the input of value-added sentences.

there is no transparency about the processing for the putputs) , can be likened to the fact that a computer can

Does Mr. A weigh more than 60 kg? (Input, Chinese) (1)manipulated without the knowledge of how CPUs andManual treatment of persons in the room(2)

semiconductors work; it is a function to describe a func-

Yes, Mr. A weighs 65 kg (output, Chinese)(3)tion. This is not to refute functionalism but to merely
show that the “Chinese room” argument [20] is nolonger keeping up with the development of XAI.

The exchange in (1) through (3) is a question of fact,
and the number of steps in the inference is one. If thisAutonomy is not a philosophical or ethical argument"one-shot" (input-processing-output once-only) factual

but rather, political one because it undermine autonomy

question is a factual question, then the Chinese room
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CBEL Report Volume 5, Issue 2

Soichiro Toda, Eisuke Nakazawa

argument seems to be valid. The person in the room fol-autonomy and the Chinese language room. AI autonomy

lows the Chinese rulebook and produces an output.

can be conceptually divided into two categories:

However, what if further value-laden questions and discussion (inputs and outputus) follows?

(5-1) Inherent in the AI (attributed to some person's self,
including fictitious).
(5-2) Epistemological, as defined by the human observ-

Is Mr. A's weight appropriate for a 40-year old?
(Input, Chinese)(1’)

Manual treatment of persons in a room(2’)

Note that (5-1) is intrinsic, such as preferences men-

Yes, 40-year old Mr. A weighs the right amount(output and Chinese)(3’)

tioned earlier, and autonomy (5-2) is defined by the human who observes it. XAI performs the decision content

Do you like Mr. A with the right weight?
(Input)

ing the AI.

(4’)

of decision-making (autonomy (5-1)), and for the as-

The conversation ends here because it is beyond thesessment of epistemological autonomy, it is necessary

range of responses that can be output by the AI. (5’)

to understand the process of decision-making (process

Why do you like/dislike Mr. A? (Input)of understanding) (5-2). Given that XAI is formed by a(6‘)(the conversation also ends here)

large number of modules, the process of understanding
the process is dispersed and expressed at various levels.

As described above, since the Chinese room does not

However, two problems emerge as follows:

have preferences, it cannot answer value-added questions (i.e., value-laden questions or moral questions),(a) The problem of formulating a theoretical coping pol-such as "Do you like Mr. A?” More importantly, value-

icy for the "infinite regress of explanations of expla-

added decision-making requires the implementation ofnations of explanations of explanations..." between

self.

modules of XAI.

If the XAI could answer subjective, respondent-spe-(b) The practical question of how and to what extent

cific questions in a retestable and reproducable manner,

those who observe the process of breakdown of thethat would be a great progress for XAI research field. In

decision-making by XAI must request a breakdown

the "Moral Turing Test” [23], XAI must implement a(the problem of arbitrary stops).

preference for someone (or something), and there aremany questions that cannot be answered without a pref-

From these considerations and thought experiments,

erence. However, that is the same as analyzing the sci-a conceptual framework for conducting the preliminary

ence of someone’s arbitrary intentions and empathic

experiment aforementioned is needed. For example, theabilities to self and the consequences of their decisions

issue of reproducibility requires consideration: when

[22,23].

looking at the response of XAI at times t1 and t2, otherWe must also mention the relationship between
52

conditions being equal, the response obtained should be

Determinism and Social Paradoxes of Explainable Artificial
Intelligence ( XAI )CBEL Report Volume 5, Issue 2

the same. However, the paradox here is as follows: when

Moreover, these interrelationships run through thehumans reason and make decisions at times t1 and t2,

narratives that are input into the XAI. In Section 2 wethe final responses may differ in the reasoning process.

introduced and distinguished between heavy and light

This is linked to "humanness," “fluctuations [24–26],”

XAI. Heavy XAI performed narrative exchange at theand “perturbations [27],” which we discuss below.

output–input layer and layered them as much as possible.

However, if XAI does not make identical decisions at t1

Thus, difficulties appear when XAI makes its character-and t2, that is, if reproducibility is not ensured, social

istic reason rise from those layers. The information ob-

implementation will be difficult. Imagine that an inno-

tained is very important, but its cost is enormous. Fur-cent suspect in a certain case is waiting for the sentence

thermore, the more complex the layered network in theto be handed down. Then, along comes an "ambiguous

implemented XAI, the more possibility—or contin-(human-like)” XAI. As for a defendant, (S)he will thinkgency—is involved in social decisions. The more com-the sentence is not fair and unlucky. There is a possibil-

plex the network of layers in XAI, which requires social

ity that the suspect’s life was determined by physicalimplementation, the more possibility in social decision-fluctuations (or perturbation). Still, the sentence is liter-

making, making social implementation difficult. This isally beautifully laid out and logical by a plausible expla-

the paradox in the social implementation of XAI.

nation.
Again, the explanation in the Chinese room thought

6. Toward Social Implementation of XAI: Arbitrari-experiment is the primary role of the XAI accompany-

ness and Anthropocentrism

ing the AI (the person in the room). Even if that XAI has

To address the paradox in the social implementationa learning function, its ability to use its knowledge toof XAI, we argue that it is useful to introduce arbitrari-

make social decisions depends on the physical fluctua-

ness and anthropocentrism into XAI. First, arbitrarinesstions of the XAI, which mimics human cognition.

of XAI implies that

Knowledge and the representation of knowledge are notalways in the mind of the individual [28]. The individual

according to the algorithm, the data is decoded to thehere is a human being, but this paper assumes that XAI

point where it is human-interpretable, resulting in analso makes such representations of knowledge and pre-“arbitrary suspension” of explanation on the part of thedicts the associated effects on its surroundings. Whenhuman.

value-added questions were asked, it was confirmed that
it is the interrelationship between the multilayered con-

Second, introducing anthropocentrism into XAI

sequences and reasons for each output that matters.

would modify the paradox in the social implementation

However, it cannot be left to XAI with its probabilistic

of XAI. In this paper, XAI has been positioned initiallyfluctuations to make decisions that affect a person’s life.

as a so-called AI, an agent based on deep learning. Its

However, have there not been attempts to implement

algorithms, however, turn it into an agent that empha-

XAI in such situations?

sizes a very human, qualia-like element that also makes
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CBEL Report Volume 5, Issue 2

Soichiro Toda, Eisuke Nakazawa

mistakes in its explanatory reasoning. For example, in

There is no doubt that this is an issue worthy of consid-

explaining why music is played in a palliative care de-eration in the philosophy of XAI. In the story suffi-

partment, the agent (XAI) is human, and the way it ex-

ciency theory, stories and plots that do not require ex-plains is also recognized as human. Moreover, the pre-planation are also considered. For example, it may besent generation, when con-fronting the XAI, want suchconcluded that in a large rectangle, a triangular figure

an explanation. However, if people ask themselvesappears to be chasing a round figure, and that, from awhether they would use or trust such XAI in practice,cognitive science point of view, even an infant would

the answer is no, especially in causal explanations [29].

perceive a story/plot. A cognitive-philosophical per-

It would be agreeable to have an XAI that is human-like

spective on chatbots may answer this question (see Mi-

in its explanations, that provides a straight-forward an-

zukami [31] for details). We agree with Mizukami but

swer, and that is accurate. However, there is a trade-off

believe that it is important to separate storytelling/plot-

between humanity and accuracy as the goal of XAI.

ting from moral agency: there will be occasions when it

This may lead to the question, “Does XAI have to beis necessary to think of chatbots or human-centric XAI

human?” From the discussion in Sections 1 and 2, wein the context of storytelling/plotting without moral

con-firm that XAI (in this paper) aims to answer diffi-

agency.

cult questions in everyday language. According to ourWorse, the story sufficiency theory makes the level ofposition in this paper, in between the "human" agent andexplanatory content of XAI and its prediction increas-the "AI-like" (i.e., black box) agent is algorithmic bias,

ingly difficult. Stories/plots are unnecessary in medical

discrimination, and dogmatism; humans know thatpractice, for example, where tumor detection is para-

those are morally wrong. It is also an important mission

mount. Conversely, in cognitive science, storytelling is

to objectify discrimination by AI from a social psycho-

the primary task of XAI because the decision-making

logical perspective and to evaluate the appropriate "dis-process is central in the famous "Sally and Anne” exper-

tance" between humans and XAI in databases. How caniment [32].

such an appropriate distance be achieved? We cannot

If the above assumptions are appropriate, there arereconcile humanity and accuracy, as dis-cussed, if wethree issues to be concerned about. First, XAI will have(rightly) side with either side. However, as long as peo-

to be individualized (division of labor), and if tailor-

ple remain human (and are forced to shoulder the humanmade XAI [33] is not realized, the problem that under-

in a human-centric society), the object of XAI is to belies this pa-per—social implementation of XAI—is not

human. The more it has its own story/plot, the moreplausible because the implementation of general (uni-credibility (trustworthy or not) it will gain. Moreover,versal) XAI is virtually impossible. Second, the individ-

we are compelled to shoulder XAI with anthropocen-

ualized division of labor will be difficult to achieve: ittrism (i.e., human-centric society) [30]. In other words,

will require individually tailor-made profiling of the in-humans have an innate nature to give XAI a narrative

finite number of profiles in XAI's addressees, and ulti-(we henceforth call this the “story sufficiency theory”).

mately XAI will become "too heavy”. Accompanying

54

Determinism and Social Paradoxes of Explainable Artificial
Intelligence ( XAI )CBEL Report Volume 5, Issue 2

that, a problem of cost would appear. Finally, the ulti-

also thank Takayuki Kira (Faculty of Law, Aichi Uni-

mate goal of XAI is to help people (and patients) tellversity) and Tomohisa Sumida for their important sug-

their own stories and use them in treatment and educa-

gestions. The authors would like to express their grati-tion, or in other words, to help them create narratives.

tude to all of them and to acknowledge that all respon-

Thus, the direction of the explanation is reversed. The

sibility for the text in this paper rests with the authors.

following question must now be answered: "Can multiple moral agents in an XAI be simultaneously account-

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