Ethical considerations of the GermEval20 Task 1. IQ assessment withnatural language processing: Forbidden research or gain of knowledge?

Dirk JohannßenMIN Faculty,

Dept. of Computer ScienceUniversitat Hamburg

& Nordakademiehttp://lt.informatik.uni-hamburg.de/

{biemann, johannssen}@informatik.uni-hamburg.dedavid.scheffer@nordakademie.de

Chris BiemannMIN Faculty,

Dept. of Computer ScienceUniversitat Hamburg

22527 Hamburg, Germany

David SchefferFaculty of EconomicsNORDAKADEMIE

25337 Elmshorn, Germany

Abstract

The use of Intelligence Quotient (IQ) testing asa measure for intellectual ability is controver-sial. Even though IQ testing is considered tobe among the most valid measures of psychol-ogy, findings and current research sparked adebate over racial or socioeconomic biases, aswell as the label of ‘pseudoscience’ for manysituations that involve IQ testing. The Ger-mEval20 Task 1 asked researchers to inves-tigate NLP approaches for approximating theranking based on IQ and high school scoresfrom so-called implicit motive texts. Quickly,a vivid discourse on whether the shared taskshould be viewed as unethical and forbiddenwas held within the NLP community. In thispaper, we investigate ethical considerationsand arguments against and in favor of such atask and come to the conclusion that this typeof research should be conducted despite theundoubtedly associated ethical issues, as it canshed light on thus-far non-transparent methodsand offers valuable gains of knowledge.

1 Introduction

The use of Intelligence Quotient (IQ) testing asa measure for intellectual ability is highly con-troversial. On the one hand, different IQ mea-sures have been established by psychologists morethan a century ago and are held to be amongthe most valid, stable, and reliable measures inthe whole scientific field of psychology (Benson,2003). Cognitive abilities are influential predic-tors for multiple criteria for professional success(Schmidt and Hunter, 1998; Kramer et al., 2009;Ones et al., 2017). On the other hand, recent stud-ies have shown that many contucted IQ tests arefundamentally flawed as to introduce racial or so-cioeconomic biases (Turkheimer et al., 2003).

Even the term intelligence is ambiguous as wellas the assumed impacts IQ testing has on aptitudediagnostics, as the title of the paper ’intelligence

is what the intelligence test measures’ suggests(Maas et al., 2014), whilst most definitions at leastagree that intelligence is always connected to suc-cessfully overcoming challenges of everyday lifesituations (Rost, 2009).

Since those aspects already are concerns, anywork that involves automation by the use of NLPtechniques and IQ testing information based onnatural language texts understandably raises con-cerns. In case of the GermEval20 Task 1, the re-search has caused more than just concerns, but aso-called shitstorm on the social platform Twitter1.

Figure 1: Images to be interpreted by participants uti-lized for the operant motive test (OMT) on the leftand the motive index (MIX) on the right. The motivesare the affiliation motive (A), the power motive (M),achievement (L), and freedom (F).A 0 stays for the zero/ unassigned motive.

The GermEval20 Task 1 on the Classificationand Regression of Cognitive and Emotional Stylefrom Text (Johannßen et al., 2020)23 was the stum-bling block for a heated debate on this topic.

In short, the task is about researching the valid-ity of so-called implicit motives and their poten-tial to substitute controversial metrics utilized in

1https://www.twitter.com2GermEval is a series of shared task evaluation campaigns

that focus on Natural Language Processing (NLP) for theGerman language. The workshop is held as a joint Confer-ence SwissText & KONVENS 2020 in Zurich.

3https://competitions.codalab.org/competitions/22006

aptitude diagnostics. Metrics like IQ tests, highschool grades, or math tests are commonly used inaptitude diagnostics, but can quickly be utilized ininherently flawed settings. Research on implicitmotives has shown, that they are indicators forlong-time behavior and development, which couldreplace the other metrics (Scheffer, 2004). Forthose implicit motives, participants are asked toanswer questions to ambiguous drawings (Schef-fer and Kuhl, 2013). Those association tasks areless socioeconomical or racial biased and rathershow intrinsic desires than task comprehension(Schultheiss, 2008, p. 439 ff.). However, as thereis no clear and unambiguous rule system for label-ing those implicit motives, they have yet to be un-derstood better (Johannßen and Biemann, 2019).This is, what the shared task aimed: a better under-standing of implicit motives and their role in apti-tude diagnostics by the use of Natural LanguageProcessing (NLP) methods.

The task contains two subtasks. For Subtask 1,participants are asked to reproduce a ranking ofstudents based on different high school grades andIQ scores solemnly from implicit motive texts. ForSubtask 2, participants are asked to classify eachmotive text into one of 30 classes as a combinationof one of five implicit motives and one of six levels(Johannßen et al., 2020).

During the heated debate on the shared task,moderate critics of the task called for the organiz-ers to ’pull the plug’, others went as far as com-paring the task with Eugenics in the Third Reich(even though those critics were clearly out of linewith the constructive and fair debate held by mostresearchers).

Three main debated upon topics emerged fromthe debate, which will be discussed in this paper:i) IQ testing and biases, ii) forbidden research, andiii) reasons for even building such a system.

In Section 2, we will first describe the sharedtask in more detail and provide some backgroundinformation of IQ testing in Section 3. A discourseas to why this is an ethical question and the courseof the emerged Twitter shitstorm and heated de-bate is presented in Section 4. The three points ofdiscussions are in Section 5 (i, IQ and bias), Sec-tion 6 (ii, reasons for building such a system), and7 (iii, is there forbidden research). A final discus-sion in Section 8 concludes.

2 The role of implicit motives for theGermEval20 Task 1

Researchers have found indications that linguisticfeatures such as function words used in a prospec-tive student’s writing perform better in predictingacademic development (Pennebaker et al., 2014)than other methods such as GPA values.

The purpose of the GermEval20 Task 1 was toinvestigate, whether firstly, implicit motives can beclassified on a human level and whether secondly,those implicit motives are sufficient for compen-sating flawed predictors utilized during aptitudediagnostical evaluations, such as GPA or IQ scores(Johannßen et al., 2020).

During an aptitude test, participants are askedto write freely associated texts to provided ques-tions on shown images (such as those displayedin Figure 1). Psychologists can identify so-calledimplicit motives from those expressions. Implicitmotives are unconscious intrinsic desires thatcan be measured by implicit methods (Gawron-ski and De Houwer, 2014; McClelland et al.,1989) Those implicit psychometrics are said tobe predictors of behavior and long-term develop-ment from utterances (McClelland, 1988; Schef-fer, 2004; Schultheiss, 2008).

From a small sample of an aptitude test col-lected at a college in Germany, the classificationand regression of cognitive and motivational stylesfrom a German text can be investigated (Johan-nßen and Biemann, 2019).

3 Introductionary words on IQ testing

IQ testing is a form of psychometrical testing,mostly utilized in the area of aptitude diagnos-tics and structural assessments. The term intel-ligence itself is debated upon, as well as thosetypes of tests themselves (see Section 5). Nowa-days, IQ testing is in an academic crisis, forminga paradigm shift towards more precise tests of iso-lated, single skills rather than one defining metric,dissolving misconceptions of IQ testing as beinga sort of personality trait or fixed property of anindividual.

Problems and questions from IQ tests varygreatly and range from the use of language,proverbs, mathematics to causalities or mechani-cal problems, as displayed in Figure 2.

Attention, auditory, visual and tactile percep-tion, language, memory, and executive functionall need to be considered when assessing the IQ

(Christie, 2005). One of the most comprehensiveIQ tests is the Wechsler test. It samples verbaland non-verbal areas of intellectual functioning(Wechsler, 2011).

For each IQ test, the mean of all participantsmarks 100 testing points, with the standard devia-tion adding or subtracting 15 points. As a result,68% of a population range between 85 to 115 IQtesting points.

Since IQ tests can only discriminate abilities be-tween the percentiles from 3 percent to 97 percentof the tested population, very weak or very strongindividuals can not be identified. IQ testing mayonly be valid by identifying and testing a truly rep-resentative peer group. The more homogeneousthe peer group, the more valid the test. As humansare rich in diversity, this criterion is hardly accom-plishable in practice and should thus be accountedfor when interpreting scores.

Furthermore, the cultural and environmental ex-posures of peer groups and individuals are crucial.For many international IQ tests, only individuals,that were exposed to representative use of the En-glish language at all stages of life may be compa-rable to other peers.

Aptitude diagnosticians call for IQ tests to beutilized only to identify possible child weaknessesto purposefully support them with additional edu-cational offers. (Christie, 2005)

Figure 2: Different parts from an IQ test utilized at theNordakademie. Upper left: logical comprehension, up-per right: memory skills, lower left: technical compre-hension, lower right: linguistic comprehension.

4 Discourse – why is this an ethicalquestion?

When there is a need for discussing the ethicalconsiderations of a shared task, there has to be amoral dilemma in the first place. Philosophicalethics is a branch of philosophy, which is con-

cerned with how humans should live and what isconsidered to be just or unjust. Morals, on theother hand, are normative and rather connectedto the activities performed by humans under thepremise of ones’ ethics. Whilst philosophicalethics define a set of ground truths about how tolive in a just way, moralities are implications onwhat to act out in certain situations.

Moral dilemmas are issues of conflicts betweenwhat a person should do and should not do. Inthose cases, where there is neither a choice ofclearly right nor wrong action in a moral situation,the dilemma becomes present (Braunack-Mayer,2001).

As will be described in Subsection 4.1, there isa necessity for aptitude testing in nowadays hu-man interactions, may it be that a scholar is to bechosen, a new employee or a high school grade.Psychologists face the challenge that many cogni-tive processes are difficult to measure and to ob-serve. Oftentimes, psychologists have to rely onbehavioral observations or questionnaires. How-ever, any consciously given response can never re-veal unconscious desires, which is why e.g .im-plicit motives (see Section 1) might be valuable.One downside to the use of implicit motives isthat they yet have to be fully understood and re-searched in terms of their validity. Many metricsare yet not fully understood, even though psychol-ogists are confident in their explanatory powers.

IQ tests have been researched for more than 100years and are said to be among the most stable,valid, and reliable metrics in psychology (Ben-son, 2003). However, recent research has clearlyshown that there is hardly any performed IQ test-ing has been done without introducing harmful bi-ases (see Section 5). As standardized tests are theclosest feasible form of aptitude diagnostics, theycan hardly be completely omitted either. This is astrong moral dilemma.

This very dilemma caused for the GermEval20Shard Task 1 so eagerly debated upon. It bearsthe chance of learning more about implicit mo-tives, which even could compensate IQ testing, re-placing it completely. However, any research onIQ testing-related data bears the danger of biases,pseudoscience, and misuse of approaches or re-sults.

4.1 Aptidude diagnostics and IQ testing inthe NLP research community

When it comes to working with natural languageprocessing methods in combination with IQ tests,there are different disciplines.

One of the most broadly researched disciplinesis benchmarking artificial intelligence (AI) sys-tems by their capabilities of scoring well onhuman-intended IQ tests. Even though data re-sources for performing those AI benchmarks arelimited, small, and poorly standardized (Liu et al.,2019), there are still many experiments on them.The goals of those benchmarks include advancingAI, validating AI systems, investigate intelligencetesting further, and to understand better what hu-man intelligence is.

Another discipline is more closely linked to hu-man behavior and tries to correctly classify or hu-man performances on IQ or comprehension testsaccording to the defined measures of success ofthe test. Different from the benchmarking, wherethe IQ test itself is the research object, this dis-cipline always involves the IQ test and humanperformance. The shared task provides data forsuch studies. Another example of such a task isthe Automated Student Assessment Prize (ASAP)Short Answer (SA) challenge.4 The ASAP-SAwas conducted by the Hewlett Foundation andaimed for predictions of students’ grades basedon short answers given by those students. Eventhough there were ethical discussions beforehandand during the task, the organizers specifically dis-missed those and asked for methodology papersonly. Regarding the ethical concerns, the orga-nizers argued that standardized tests occupy pro-fessionals, which could, were they not manuallyevaluating those tests, able, to craft more sophisti-cated, more individual and more insightful testingprocedures.

The third discipline is a meta-analysis on cir-cumstances connected with NLP on IQ tests.Those studies include e.g. bias research, statisticalevaluations, or behavioral consequences. Ethicalconsiderations, such as this work, can be includedin that discipline as well (Tsvetkov et al., 2018;Hovy and Spruit, 2016).

4https://www.kaggle.com/c/asap-sas/overview/description

4.2 Heated Community Discussion

On December 4, 2020, the first call for participa-tion was released on multiple channels, one be-ing the corpora list5. The original call was entitled’GermEval 2020 Task 1 on the Prediction of Intel-lectual Ability and Personality Traits from Text:1st Call for Participation’. It described the taskin very technical terms with a focus on the NLPmethodology.

A first reaction on the Corpora List consideredthat “[a]s a community, we should think carefullyabout whether it is appropriate to work with IQ testresults as data, and what the applications of this re-search might be.” and “In the United States, thereis considerable evidence that IQ tests are raciallybiased”6.

Besides the first topic of discussion, biases inIQ testing, the direct response to this introducedthe second topic of discussion: “This task seemsirresponsible/poorly conceived to me. Before de-signing such a task, I think it is imperative to con-sider its use cases: When and why would we wantto predict IQ scores or high school grades from thetext?”7.

The discussion continued in an argumentativemanner and a respectful tone. There were roughlyequal amounts of supporters and opponents ofthe task and even though many assumptions weremade – e.g. of whether the data was provided vol-untarily by the aptitude test participants – quickly,panelists came to a conclusion, that too little ofthe underlying circumstances of the task was de-scribed in the first call, to sensibly continue thediscussion.

In the meantime, the discourse continued on an-other channel: Twitter. As shown in Figure 3, con-cerns were raised by an initial tweet.

Other than the well-formulated and balancedconcerns on the Corpora List, tweets are usuallymuch shorter. Many researchers asked for moredetails, others drifted into speculations on not-provided details of the task’s circumstances. Asthe tone got increasingly hostile and demands for’pulling the plug’ and starting petitions against

5https://mailman.uib.no/public/corpora/2019-December/

6https://mailman.uib.no/public/corpora/2019-December/030882.html By Jacob Eisenstein

7https://mailman.uib.no/public/corpora/2019-December/030883.html by Emily M. Bender

8A message on the short messaging service Twitter iscalled a tweet and can be up to 240 characters in length.

Figure 3: A first tweet carried the discussion from theNLP specific corpora list to the more visible and in-ternational social plattform Twitter, raising concernsabout the shared task.8

it arose, the task organizers posted a request forsome time to formulate a call revision. This re-vision was released on the 5th of December, oneday after the 1st call of participation, the organiz-ers released a statement, clarifying motivations forthis task and explaining some of the task’s circum-stances9. Meanwhile, the shitstorm on Twitter hadescalated up to the lowest point of comparisons toNazi Germany and Eugenics, as displayed in Fig-ure 4.

Figure 4: The so-called shitstorm on Twitter went sofar as there were calls for petitions against the task,demands for ’pulling the plug’, as well as Nazi Ger-many and Eugenics comparisons. However, many par-ticipants strived for a sensible, respectful and construc-tive discussion.

Two days after the published 1st call for par-ticipation, the third topic of discussion emergedfrom the discourse: forbidden research. One com-munity researcher wrote a Medium article on thetopic with the title ’Is there research that shouldn’tbe done? Is there research that shouldn’t be en-couraged?’ on the 7th of December10. The or-

9https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2020-cognitive-motive/germvval2020-task1-public-statement.txt

10https://medium.com/@emilymenonbender/is-there-research-that-shouldnt-be-done-is-there-research-that-shouldn-t-be-encouraged-b1bf7d321bb6 by Emily M.Bender.

ganizers published an explanatory public reactionto the heated discussion on their accompanyingtask website, on a Codalab competition website,as well as on Twitter11.

The heated discussion quickly died down afterthe 6th of December. The task organizers revisedtheir task website to include more detailed infor-mation on the tasks’ circumstances, ethical con-sideration and changed the name of the task from’Prediction of Intellectual Ability and Personal-ity from Text’ to ’Classification and Regression ofCognitive and Motivational Style from Text’, asthis is by far more precise in terms of the tasks’goals (as described in Section 1).

5 IQ testing and biases

This section will explore and discuss the first ofthe three topics of discussions that emerged fromthe discourse: i) IQ testing and biases. Effects likemeasurable biases and a training effect will be ad-dressed, as well as measures taken against thosebiases and a general discussion on systems theory.

5.1 There is a socioeconomical bias in IQtesting

Minorities can be discriminated by a biased dueto unequal environmental circumstances and mea-surements in non-representative groups (Rushtonand Jensen, 2005).

Firstly, the term intelligence in intelligence test-ing is highly misleading, as there is no well-defined concept of intelligence. Rather than in-telligence, IQ tests are said to measure the skill ofthe specific tasks employed in an IQ test.

In what IQ scores measure, they are thought tobe one of the best measures in the scientific field ofpsychology. Those skills are often, what aptitudediagnostics define as relevant for many modernskill-oriented jobs (validity). They furthermorestay relatively stable across different stages of life,starting with the age of 8, and with time (stabil-ity). The stay consistent when performed with thesame setups but different observers and observees(reliability). Lastly, parts of IQ tests correlate witheach other, suggesting an inference of both, envi-ronmental and genetic factors (Plomin and Deary,2015).

11https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2020-cognitive-motive/germvval2020-task1-public-statement.txt

However, there are strong controversial signsof this genetic pool thought. The Flynn effect,which states that the IQ scores among the popu-lation grow by 3 points each decade, by far toofast for it to be connected to evolution, shows this.This effect is rather caused by skills acquired fromenvironmental changes (Flynn, 1987). E.g. Wheninvestigating the development of refugees, theirearly environmental circumstances are subopti-mal. However, later positive environmental fac-tors can compensate for those early difficulties(Dweck, 2017).

Not so much race, but socioeconomics isthought to be the difference (Turkheimer et al.,2003). It shows that IQ potential is determinedby genes, but whether this potential develops isdependent on the environment (who is rich canachieve anything). Thus, only if it was ensuredthat both individuals enjoyed the same good envi-ronment, an IQ score would truly say anything.

IQ tests are good measures of innate skill abil-ity if all other factors are held steadily, which is,in fact, impossible. The differences in IQ scoresacross minorities and the majority, as it is present,points to a very serious issue: Inequality of op-portunity. It is this socioeconomic bias, whichleads to unequal opportunities especially in coun-tries where there is a rich diversity amongst thepopulation.

5.2 Standardized tests are trainable

As stated before, terms like intelligence or cogni-tive ability are misleading, when describing apti-tude diagnostics and IQ testing. Those tests mea-sure a pre-defined set of skills.

In the area of psychology, there are certain prin-cipals for experiments and procedures, that havedeveloped over many years. One of those princi-pals is that participants always need to know whatis being tested beforehand. It needs to be clear,what results of a procedure or test are, and howthose results relate to the participants.

Another principle is that most experiments andtests in psychology are only truly objective, if par-ticipants did not yet complete this particular testand do not know the exact type of testing that thewhole experiment or parts of it will set up.

As soon as there is knowledge or accustomingof the underlying testing procedure, participantswill most likely perform more in the direction ofwhat they think would maximize a reward, then

they would if they did not take a test yet. In otherwords, IQ tests are trainable. The more often par-ticipants perform IQ tests that are thought to be thenorm of a given point in time (in reference to theFlynn effect), the higher participants are thoughtto score on those tests.

However, this implication does not just holdfor the IQ tests themselves but goes further. Asstated before, IQ tests do not test intelligence butskill. As those skills are connected with manyskills required by modern jobs and with manyskills trained and taught in schools – e.g. to thinkin abstract terms, pattern recognition, and basicmath skills like the Fibonacci series), well edu-cated and often home-schooled individuals tend totrain skills more and thus perform better on intel-ligence testing procedures, even though high skillson any non-related task can not be guaranteed.

5.3 IQ Tests Discriminate Minorities

It is this bias, which leads to unequal opportuni-ties especially in countries where there is a richdiversity among the population. Intelligence test-ing has had a dark history. Eugenics during thegreat wars e.g. in the US by sterilizing citizens 12

or in Germany during the Third Reich and Eugen-ics (Reddy, 2007) are some of the most gruesomeparts of history.

But even in modern days, the IQ is misused. Re-cently, IQ scores have been used in the US to de-termine which death row inmate shall be executedand which might be spared. Since IQ scores showa too large variance, the Supreme Court has ruledagainst this definite threshold of 70 (Cooke et al.,2015). However, Sanger (2015) has researchedan even more present practice of ’racial adjust-ment’, adjusting the IQ of minorities upwards totake countermeasures on the racial bias in IQ test-ing, resulting in death row inmates, which orig-inally were below the 70 points threshold, to beexecuted.

There is an ethical necessity to carefully view,understand and research the way intelligence test-ing is conducted and how those scores are – if atall – correlated with what we understand as ’in-telligence’, as they might be mere cognitive andmotivational styles. Further valuable research canbe conducted to investigate connections betweenother personalities. Racial biases are measurable,

12https://supreme.justia.com/cases/federal/us/274/200/

variances are great and many critics state that IQscores reflect upon skill or cognitive and motiva-tional style rather than real intelligence as it isbroadly understood.

5.4 Wrong Wording: ’Cognitive ability’As stated before, the term intelligence is highlymisleading. Furthermore, for the first subtask, wewere trying to reproduce, what is being utilizedas a selection mechanism, where the IQ testing isonly part of the test. High school grades are an-other, matched to implicit motive tests. For thesecond subtask, implicit motive texts were to beclassified directly.

Both of those tasks aimed for researching an un-derlying pattern or truth to implicit motives, whichhave been researched and show no inherent bias.Furthermore, high school grades in Germany con-sist of 60% participation in class. Thus the wholeterm cognitive ability in the 1st call for participa-tion of GermEval20 Task 1 was nonsensical andplain wrong but was revised quickly after first con-cerns were raised.

5.5 Relation to German socioeconomicstructure

There is a broad understanding that intelligencetesting is especially prone and biased towards theenvironments and circumstances in which theywere developed. As a result, the tests designed inWestern, white societies are problematic when uti-lized for testing richly diversified cultures (Vahidi,2015).

Unfortunately, the German education system isknown to have a strong socioeconomic bias, whichleads to a vast under-representation of people witha migration background in higher education (Diehland Fick, 2016; Fernandez-Kelly, 2015).

This, paradoxically, leads to the data of the Ger-mEval20 Task 1 being less prone to the influenceof such biases, with respect to the ground popu-lation of the underlying data. Even though thereis no information on names, nationalities, or otherdemographic data, as it is forbidden in Germanyto record personal information according to EUlaws. However, e.g. pictures of graduation yearsindicate that there is little diversity amongst thosegraduates, as displayed in Figure 5.

5.6 Challenging IQ testing biases in GermanyDuring the long history of research on the field ofIQ testing, many mistakes were made and investi-

Figure 5: The exemplary 2014 year of graduationfrom the NORDAKADEMIE illustrates the cul-tural homogenity, as the vast majority of graudatesare white (https://www.shz.de/lokales/elmshorner-nachrichten/lasst-die-huete-fliegen-id19354606.html). In Ger-many, a strongly biased socioeconomical filter isalready present at the high school level.

gated. Aptitude diagnosticians have spent decadesto challenge and correct the strong socioeconomicbiases, that were present in most of the earliest IQtests. Nowadays, there are many different variantsand approaches of IQ testing.

In Germany, there is little diversity amongst pri-vate college applicants. Even though researchersat the NORDAKADEMIE try to actively chal-lenge those socioeconomic biases by employingimplicit motives, that are known to be less biasedthan other metrics in the field of aptitude diagnos-tics, the employed IQ test also accounts for the lit-tle diversity of the participant population.

The NORDAKADEMIE utilized the IST 200R intelligence structural test by Liepmann et al.(2007), which was normalized on high schoolgraduates13. Since only about a third of studentsattend high school in Germany, the basic popu-lation of this IQ test accounts for the little diver-sity of most applicants at the NORDAKADEMIE,which already experienced a socioeconomic filter.Even though this filter is a discrimination already,the employed IQ test objectively accounts for thetype of the basic population that takes the test andthus challenges this bias.

13In Germany, the secondary school tier consists of threetypes of schools: The Hauptschule (practice-oriented voca-tional education), the Realschule (theory-oriented vocationaleducation) and the Gymnasium (high school, preparations forpursuing a college education). Only about 30% of graduatesgo to college in Germany (Fernandez-Kelly, 2015)

5.7 Systems theory by Luhmann:communication is the relation of systemsand none can be transmitted withoutnoise

The systems theory by Luhmann is a philosophicaland sociological communication theory, that de-scribes agents of an environment not as instancesbut in their relations to other agents. Communi-cation, according to Luhmann, is the constructingprinciple of an environment and not just a meretool. An agent is understood as an autonomouspart of this environment, which offers its innerstructure as a matter of communication to otheragents (Gorke and Scholl, 2006).

However, as the channel model of communica-tion by Shannon (1948) describes, there is no com-munication between agents (sender and receiver)without being obscured and disturbed by noise.

One environment or system is science. Everyscientific discipline can be described as an agentin this environment. Whenever there is incompleteknowledge of the inner state of an agent, any typeof communication between those systems gets ob-scured by noise and thus assumptions of those in-ner states can range from approximations to mereguesses. In any case, the assumptions are flawed.

Applied to the GermEval20 Task 1 and the eth-ical dilemma of IQ testing at hand, it can bestated, that since the scientific field of applied NLPdoes not comprehend the inner state of the scien-tific field of psychology and aptitude diagnostics,assumptions of the implications, limits, and ef-fects of IQ testing from any non-psychological re-searcher must be viewed with caution – especially,if no correspondence has been undergone, as truthis the interaction between correspondence, con-sensus and consistency (Sahakian and Sahakian,1993).

6 Resons for building a system thatGermEval20 Task1 proposes

This section deals with the topic ii) of discussion:why should such a potentially dangerous system,as proposed by the GermEval20 Task 1 organizers,be build in the first place? Difficulties and possiblemisuses are the subjects of this section, as well assome background on the implications of research-ing implicit motives.

6.1 Short text classification is difficult andvague

Short text classification is a very difficult task. Themost widely used method is the keyphrase extrac-tion (Zhang et al., 2018). However, the implicitmotive theory asks for annotators to examine thenarratives of texts rather than single keywords orkeyphrases (Scheffer and Kuhl, 2013). Thus, themost promising method for short text classifica-tion is not applicable for implicit motives texts,and therefore, it is doubtful that the mere focus onan automatic classification procedure creates validresults.

6.2 The task reflects an established practicein Europe

It can be debated upon, whether researchersshould focus on theoretical tasks or if a very prac-tical focus is legit. The NORDAKADEMIE isa university for applied sciences and the wholecontext of the GermEval20 Task 1 aims for re-searching implicit motives in the very application-oriented field of aptitude diagnostics14.

Even if there are very good and strong ar-guments against aptitude diagnostics, assessmentcenters, the consideration of socioeconomicallybiased high school grades or personal job inter-views, it is a very common practice in Germanyand Europe to examine all of those approaches fordecisions on whom to employ.

Mainly companies in Europe employ IQ testsfor selecting capable applicants. In the UnitedKingdom, roughly 69 percent of all companies uti-lize IQ. In Germany, the estimate is 13 percent(Nachtwei and Schermuly, 2009).

Academia has the responsibility to research thebenefits of society. Even though the organizers ofthe GermEval20 Task 1 do not focus on IQ test-ing but the implications of implicit motives, sinceIQ testing is part of the conducted practice in Ger-many and Europe, there is an academic respon-sibility to research its implications. Furthermore,science nowadays is called upon making efforts to-wards findings that are closely related to everydaysociety, as Bormann (2013) points out.

6.3 Purpose of the task

Especially the early reactions of the shitstorm de-scribed in Section 4.2 were shaped by misconcep-tions, incomplete information and misleading as-

14https://idw-online.de/de/news492748

sumptions what the GermEval20 Task 1 was aboutand what was it not about:15

“[...] I would worry about any research projectwhose organisers chose to include ”prediction ofintellectual ability” in the very title. Presumably acareful choice for a big research project. [...]”

This becomes apparent as some Tweets raisedconcerns mostly based on the headline of the 1stcall rather than its content, whereas the 1st callpaired with provided websites did not includemuch of background information either.

However, as the organizers stated in their taskcompanion paper (Johannßen et al., 2020) andmore prominently on their revised companionwebsite: “Any research performed on this aptitudetest or the annually conducted assessment cen-ter (AC) at the NORDAKADEMIE is under thepremise of researching methods of supporting hu-man resource decision-makers, but never to createfully automated, stand-alone filters”16.

The defined goal of the GermEval20 Task 1Subtask 1 was to reproduce a ranking of stu-dents based on the sum of z-standardized highschool grades and IQ scores solemnly based onprovided implicit motive texts (Johannßen et al.,2020). Those ranks were not calculated to indicatethe superiority of single individuals over others.From an aptitude diagnostical view, this would notmake sense. E.g. a student that might achieve ahigh IQ score and high German high school gradesbut worse math and English grades might have ahigher overall rank compared to a student whosemetrics are all above average but without any es-pecially high ones. Yet companies might prefersomeone who is above average in every aspectover anyone, that might have high grades in onesubject but very low ones in other (Hell et al.,2007).

Moreover, the critics of this shared task andthe organizers themselves have criticized the broadconsideration of IQ scores and high school gradesin Germany and the EU, as they discriminateagainst minorities with their socioeconomic bias.

15https://mailman.uib.no/public/corpora/2019-December/030896.html by Mike Scott.

16https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2020-cognitive-motive.html

7 Forbidden research

This section explores the topic of discussion iii):Is there research that should not be done? Is thereforbidden research? As these are questions forbroad fundamental debates, we will only focus onthose aspects, that appear most connected to theGermEval20 Task 1.

7.1 Knowledge is not harmless

The first general principle to acknowledge is thatknowledge is not harmless. There are many ex-amples of theoretical research being utilized fordestructive follow-up research or dangerous uten-sils directly. Exemplarily, Alfred Nobel did notintently dynamite to be used for war, but rather formining. Historians assume that Nobel included apeace dedicated Nobel Price in his last will is dueto his invention being misused for war purposes17.

This is an example of a so-called dual use ofinventions. When inventions intentioned for civiluses is misused without the consensus of the in-ventor for military purposes, this is called dualuse. Williams-Jones et al. (2014) describe dual usemore generally as being used for good and bad ei-ther intentionally or unintentionally by the inven-tors.

Furthermore, the authors describe the dilemmaof this dual use, as there is rarely any impactfulresearch that could not be considered dual use.Most meaningful findings could be utilized for thegood and the bad. Moreover, at times it is noteven possible to imagine the negative or bad dualuse of one’s inventions, as further research has notbeen conducted yet and novel products have yetnot been seen (Williams-Jones et al., 2014).

One infamous example of dual use that wasnot necessary imaginable is nuclear energy and itscharacteristics, which has to lead to a lot of scien-tific progress (e.g. research on cancer treatments),civilian use (e.g. nuclear energy), but also greatdestructions and threats (e.g. nuclear weapons)(Tucker, 2012, p. 74 ff.).

7.2 NLP can easily be misused forpseudoscience

IQ scores are prone to pseudoscientific settingsand are not easily distinguishable from seriousand sophisticated settings, thus masking the over-all utility of IQ testing.

17https://www.nobelprize.org/alfred-nobel/alfred-nobels-thoughts-about-war-and-peace/

Some participants of the heated discussion ofthe GermEval20 Task 1 criticized this task forbeing “dangerously pseudoscientific”. To under-stand, what this criticism refers to, one must firstunderstand pseudoscience. Hansson, a Swedishphilosopher, first differentiate science from pseu-doscience in that scientists enjoy common raisond’etre to provide the reader with the most epistem-ically warranted statements (Hansson, 2013, p. 62ff.) by employing known and broadly respectedmethods for finding those statements.

Furthermore, Hansson describes the correspon-dence between different scientific fields and disci-plines that are interconnected. No given statementviolates statements made by other disciplines andfields.

As for pseudoscience, authors are mostly di-vided as to which characteristics define pseudo-science. However, two major characteristics ap-pear to be agreed upon by most authors: i) Non-science posing as science and ii) doctrinal compo-nents (Hansson, 2017).

For pseudoscience to be posing as scienceparamount effort is undertaken to mask statementsas being made with those scientific principles,even if they are not. As science offers advantagesof describing true phenomena and reality, pseu-doscientists strive for acceptance by readers withstatements, that normally would not hold the thor-ough process of scientific work.

For pseudoscience to be of deviant doctrine,the pseudoscientists put sustained effort to pro-mote standpoints different from those that havescientific legitimacy. Thus, pseudoscientists disre-gard major principles of scientific work, like cor-respondence, consensus, and consistency, as wellas transparent methodology, replicability or inter-subjectivity (Sahakian and Sahakian, 1993).

As for the GermEval20 Task 1, critics saw eithernon-scientific work being presented as scientificone or a doctrine, disregarding established meth-ods from corresponding scientific fields, whichare NLP and psychology. The main argumentsfor calling the shared task pseudoscience is mostlikely the view, that since IQ testing is viewed bymany researchers as biased and unprecise, evenasking for machine learning systems would bepseudoscientific. They view the methodology asnot being reconcilable with established ones.

Furthermore, discussion participants criticizedthat a shared task holds a scientific premise, which

Figure 6: Critics used sarcasm to express their view ofthe shared task being methodologically flawed. This,paired with the scientific framing that GermEval offerslead to accusations of pseudoscience. The point of thetask, to research implicit motives, appears to have beenmissed by some.

they did not view appropriate for a task, that is –in their view – methodologically flawed.

However, as shown in Section 6 on the pointof discussion ii), many critics mistakenly assumedthat the task is about building an automated sys-tem for ranking students or classifying IQ scores,whilst, in turn, it is only about researching the im-plicit motive theory, as the response on the corporamailing list shows:18

“[...] lending legitimacy to the use of similartools that are used as a pseudoscientific mantle todisguise (essentially) the automation of racial/eth-nic/cultural discrimination and biases [...]”

7.3 Marketplace of ideasEven in case of the criticism on the GermEval20Task 1 setup, automation or IQ testing being le-git and point to issues, there are still strong ethicalarguments for educational institutions against giv-ing in, when broadly and publicly being exposedto social media sanctions and calls for “pulling thetask”19.

One of those arguments is the marketplace ofideas, which was first discussed by John StuartMill in his 1859 book On Liberty (Mill, 2011).The marketplace of ideas is an analogy to the freemarket and assumes, that when ideas, statements,and thoughts are presented with almost perfect in-formation – that is, on a transparent, replicable

18https://mailman.uib.no/public/corpora/2019-December/030885.html by Yannick Versley.

19https://medium.com/@emilymenonbender/is-there-research-that-shouldnt-be-done-is-there-research-that-shouldn-t-be-encouraged-b1bf7d321bb6 by Emily M.Bender.

and reliable basis –, only the truth will emergefrom this available marketplace. Gordon (1997)describes the marketplace of ideas as a metaphor,where people speak and exchange ideas freely.Freely means, that there is as little interferencefrom the government and the society as possible.

Reflected upon the GermEval Task 1, there isa violation of this philosophical and the liberalprinciple of freely spoken ideas. Whilst the gov-ernment did not interfere with ideas presented,the society did in the way of strong social mediapressure and sanctions, not discussing the idea it-self but rather demanding the idea to be stoppedwithout professional discourse (at least on Twit-ter, as discussions on the corpora mailing list, weremostly argumentative20).

At times, ideas, scientific interests, and projectsmight provoke criticism due to a Zeitgeist, even ifthey are legit and worth exploring. Darwin (1859)was heavily criticized and even got his novel workon his theory of evolution banned. Criticism onhis theory not only arose from religious organiza-tions but even from respected and well establishedfellow researchers.21 However, if the marketplaceof ideas would have been applied to Darwin, histheory and findings would have been openly dis-cussed with all forms of scientific research, ar-guments, and findings, leaving it to the audienceand research community to determine, whether histheory holds for the moment. If his ideas, however,would have been banned, as suggested by somecritics of the GermEval20 Task 1, there would nothave been an open debate. Furthermore, if Dar-win’s theory was utterly wrong, it would not havebeen able to compete and thus vanished.

7.4 Knowledge cannot be restrained

As Grashon (1983) describes, multiple researchersannounced to leave science, after having discov-ered the knowledge of isolating DNA fragmentsfor the first time. They feared that this discoverywould lead to political and social pressure. One ofthose scientists even formed a group, categoricallypressuring any scientific work on this genetic field.Nonetheless, DNA sequencing has continued to beresearched.

There are implications, that – at least basic –research discoveries can not be fully prevented or

20https://mailman.uib.no/public/corpora/2019-December/

21https://en.wikipedia.org/wiki/Reactions to On the Origin of Species

stopped, as the so-called multiple discovery or si-multaneous invention principle calls for them to bemade. This multiple discovery principle is the hy-pothesis, that most discoveries are made indepen-dently by multiple scientists at the same time, of-ten internationally. The Nobel price committee of-ten recognizes this hypothesis by rewarding multi-ple scientists who, at that time, did not collaboratedirectly.

This hypothesis is thought to be observable,since discoveries, theories and scientific tools en-able practicing scientists of a field to now makediscoveries. As the circumstances are ideal in aninternationally spread research community, simul-taneous inventions are made possible. One exam-ple is radar technology, which was discovered bymultiple countries independently and at the sametime (Galati, 2015). Thus, many believe the sup-pression of scientific progress is not possible.

On the other side, Martin (1978) argues, that adevelopment of science and technology emergingfrom that science independent from the thoughtsand desires of single scientists and pressure fromsociety are historically incorrect. In his article, theauthor argues with selected examples, namely nu-clear power, food additives, transport policy, ge-netic engineering, and automation – all of whichare characterized as technologies, having emergedfrom basic research and having experienced pres-sure and concerns from the research communityand society. What the author does not argue about,is the value of basic research itself. He states thatthe path of scientific and technological develop-ment is not usually predictable beforehand. Fur-thermore, Martin notes that concerns over scien-tific and technological development has almost al-ways to do with applications and implications forthe wider society.

At times, the research could have assumed whatnegative impact a discovery or invention couldhave on society, as Nobel, which invented the dy-namite mainly for supporting mining, could haveimagined the use for military purposes. Nonethe-less, the individuals utilizing dynamite to buildweaponry are rather to blame than Nobel himself,even if he greatly regretted, that his discovery wasused for such22.

22https://www.nobelprize.org/alfred-nobel/alfred-nobels-thoughts-about-war-and-peace/

7.5 Pushing scientists out of academiaWhilst the US has spent 4,545.7 Mio. dollars(Pece, 2020) in research and development (R&D)of computer sciences and mathematics, the USDepartment of Defense possessed a R&D budgetof 52,973.3 Mio. dollars, which is more than 40%of the total US R&D budget. Some of the mostinfluential advancements in computer science hasbeen researched behind closed doors for militarypurposes such as the RSA cryptosystem, whichwas already invented by the GCHQ four years be-fore the later patented peer-reviewed method23 orthe predecessor of the internet, the ARPANET,which was developed by the U.S. Airforce in 1969(O’Neill, 1995).

Some private companies possess comparablylarge R&D budgets as well: Alphabet, the par-ent company of the Google corp. spent 26,018Mio. dollars on R&D 24. Even though the mostrecent scientific advancements were made open-sourced and have been peer-reviewed, such as thebidirectional encoder representations from trans-formers (BERT, (Devlin et al., 2019)) and Ten-sorflow 1.0.0 (Fujita et al., 2017, p. 564), earlierdevelopments, such as the Google PageRank al-gorithm, which was kept hardly reproducable, de-spite even patents describing the basic procedure(Lindberg, 2008).

One causality and risk of violations of the mar-ketplace of ideas is that researchers, which experi-ence pressure, might leave the public academia topursue research in the private sector, which doesnot necessarily publish research to be reviewed,discussed, and criticized by the public. This couldlead to knowledge monopolies, as well as fraudu-lent or misconducted research.

This is further reflected by the recent develop-ment, that influential technology companies havecaused a so-called AI brain drain, meaning, thatmany countries experience the emigration of AIresearchers. A national brain drain is observablefrom the public research sector and academia toprivate firms due to higher salaries, greater fund-ing, and at times more academic freedom (Kunze,2019).

8 Discussion

Whenever basic research leads to new technolo-gies and applications, there is a risk of misuse.

23https://www.wired.com/1999/04/crypto/24https://abc.xyz/investor/

As humans nowadays produce a vast amount ofdigitally available textual resources, research ofNLP applications could quickly lead to question-able and possibly dangerous results. The Ger-mEval20 Task 1 has rightfully sparked a heateddebate upon the ethical considerations of this task,as it not only involves NLP methods but further-more aptitude diagnostics, psychometrics, and IQtesting – all of which can and have been misused.

However, as we have shown in this paper, thethree main topics of discussions, i) IQ testing andbiases, ii) reasons for building such a system andiii) forbidden research, have positively be evalu-ated in terms of their ethical indications.

IQ tests are very prone to biases. As the datawas collected from a small university of appliedsciences in Germany, the peer groups are heteroge-nous, no score can be reverse-engineered from theavailable data and as the main point of the taskis not to automate IQ testing but research implicitmotives, we believe the discussion to have losttrack of what the task is truly about.

This also leads to the second topic of discussion.We have shown that in Germany, high schoolsalready function as destructive socioeconomicalfilters that discriminate against minorities. Im-plicit motives have shown to be by far less biasedand more neutral. If they were better understoodand validated, aptitude diagnostics could finallymove away from bias-prone metrics as high schoolgrades or IQ scores.

Lastly, we have shown calls to forbid most re-search topics to not only be misleading, but harm-ful. In a marketplace of ideas, only truth canemerge. The past has additionally shown thatprogress is hardly containable. Moreover, pub-lic shaming and condemnation of research ideascould lead to them moving to the private sector,which already has a lot of innovative power with-out the necessity to present, discuss and have ideascriticized by peers – all of which are some key-stones of scientific work.

We view this ethical discussion as partly un-objective but have also seen a valuable discoursefrom most of the participants. It is right to viewany research project critically. However, it is al-ways important to closely investigating what a re-search idea is truly about and to honor scientificfreedom, as forbidding certain ideas puts this free-dom at stake.

9 Acknowledgements

Firstly, we want to thank the NLP community,which participated in the important discourse ofthis task. Especially the fellow researchers fromthe corpora list gave valuable input and points ofview.

Furthermore, we want to thank Emily M. Ben-der, which first raised reasonable concerns of thetask and extended the discourse to Twitter, as wellas to Medium, where she formulated two posts,summarizing and evaluated main reasons for thoseconcerns. Also, we would like to thank her forparticipating in the KONVENS 2020 Ethics andNLP panel. Additionally, we want to thank Twit-ter users, which also took place in the discourse,providing even more and broader insights in socialimplications of the task.

We want to thank Michele Loi, who wrote a pro-found ethical assessment of the task provided uswith objective and neutral ethical arguments andagreed to participate in a constructive NLP + soci-ety session alongside Emily M. Bender and DirkJohannßen on the SWISSTEXT & KONVENSconference.

We want to thank the SWISSTEXT & KON-VENS committee for supporting our task, neu-trally investigating the ethical impacts of it andprovided expertise and aid in determining thetask’s risks and chances.

We want to thank our colleagues and lab mem-bers, that supported us with advice and help inkeeping an overview and objective point of view.

Last but not least, we want to thank the partic-ipants of the shared task for staying open-mindedand interested in the research, providing foundedempirical evidence of whether such a task is solv-able and discussing its impacts on the researchcommunity and society.

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