F.e.a.r Deutsch Patch

[Fonda Stacer]

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So Deutsche bank's double digit $49tn exposure number is quite common in the industry. Key thing to note is 'notional derivative exposure' is not a reflection of 'credit risk'. In the news that floats around, people assume and talk as if the entire notional exposure is a credit risk. Due to position netting and the credit conversion factors, the actual credit risk to Deutsche Bank on those $49tr is quite tiny!

That being said, yes, deutsche bank's balance sheet is pretty weak and notional exposure is also somewhat important as it indicates the scale of operational risk deutsche is exposed to. So, larger the derivative portfolio, larger is the 'possibility' of errors and things going wrong. But using Notional derivative exposure number as a tool to engineer fear is not appropriate as that double digit number is in line with the industry players and it is not an unusual thing.

Also, Deutsche had failed the Annual Stress test conducted by the regulators in 2015, 2016 and 2018. But this year in 2019, last month, it passed the test. Stress test are quite heavy and banks are evaluated extensively. Had there been something severely wrong it wouldn't have received the green signal last month from regulators especially when it failed in 2015, when it was in a much better shape.

And yes, it had also knocked off thousands of jobs in the last couple of months, going on a complete restructuring. It has taken a lot of initiatives to eliminate unprofitable segments(Prime Finance, Electronic Equities etc). So if at all the whole market takes a hit, deutsche should alone not be the source as many fear/display it to be but rather possibly because of loaded up shorts sellers.

There were also articles yesterday talking about Deutsche's 2019 Q2 Loss of 3.15bn euros. Heaviest qrtly loss in 4 years. But the thing is, this huge number is due to the restructuring charges and golden parachutes. Infact the bank's CEO mentioned that a substantial part of the restructuring cost(3.4bn euros) is already digested in the Q2 and if excluded, the bank would be profitable.

In line with the existing advantages, as well as the disadvantages, of AI products, it does not seem surprising that some people have a very open-minded opinion and accept the emergence of AI products and acknowledge their advantages for humans. In contrast to this, others seem to be ambivalent or even sceptical and fearful regarding the rise of AI products (LINK Institut (2018) as cited in Statista [15]). This leads as far as to prominent influencers in the field, such as Stephen Hawking and Elon Musk, publicly stating that progress in AI research has the potential to end humankind [16, 17].

Given the varying attitudes towards AI, the main aim of this study is to develop and introduce a short, reliable, and valid measure to assess individual differences in the attitude towards AI. As AI is a worldwide phenomenon, it is important to present and test such a measure in various languages and across cultures. Therefore, German, Chinese, and English versions of the self-report measure will be subject to this study. The factorial structure of this measure, including invariance analyses across samples from Germany, China, and the UK, will be investigated. Moreover, potential gender differences in this measure will be examined. Thereby, it is tested whether the previously reported effects of more positive attitudes in males found with regard to technology acceptance, a field closely related to AI, extend to the current measure [18, 19]. Lastly, the associations of the newly developed measure with items on the willingness to use several specific AI products (self-driving cars, Siri, Alexa, Pepper, and Erica) in Germany and China will be investigated for validation. In detail, we expect a more positive attitude towards AI to be associated with a higher willingness to use such AI products. More negative attitudes should be associated with lower willingness to use such AI products.

The studies were conducted online in Germany, China, and the UK via the SurveyCoder tool ( ). The local ethics committees at Ulm University, Ulm, Germany and the UESTC, Chengdu, China approved the present studies. There was no separate proposal submitted to Goldsmiths, University of London, London, England. All participants gave informed electronic consent prior to participation.

Shortly before this research was conducted, a media debate was occurring in Germany focussing on the question on whether or not the rising impact of AI in daily life would result in a loss of jobs and/or would have some form of detrimental effects on humankind. In this debate, it became obvious that there are groups of people who do not openly embrace these technologies, while others use services, such as Alexa and Siri, very comfortably and without concern. The argument that AI will be the cause of many job losses was mentioned frequently in the context of reasons to fear AI. On the contrary, the opinion that people can live and work more effectively by using AI products was discussed in the context of acceptance of AI (see also representative study in Germany by Marsden [20]). Moreover, several AI products already in use in everyday life were discussed (e.g. Siri and Alexa). In brief, the key points of argument of this debate were the basis for the construction of the proposed scale assessing the attitude towards AI (ATAI scale) and additional items measuring the willingness to use specific AI products.

The items of the ATAI scale and the items about specific AI products were developed in German language, translated into Chinese and English, and then back translated into German independently by two bilingual researchers (forward and backward translations). Following this, the German original and the back translations were carefully checked for compatibility and the translations were adjusted if necessary. Additionally, native speakers checked the translations for linguistic and grammatical correctness.

All analyses were implemented using the statistical software R version 3.5.2 [21], R-Studio version 1.1.463 [22], and several R-packages such as car [23], dplyr [24], effsize [25], gmodels [26], GPArotation [27], Hmisc [28], lavaan [29], ppcor [30], psych [31], and semTools [32].

Accordingly, correlations of the ATAI scales with age were calculated by means of Pearson correlations to check whether age should be treated as a control variable in further analyses. Differences between males and females, countries, and the gender by country interaction effect on the two ATAI scales were investigated using a multivariate multifactorial ANCOVA with age as control variable (see Sect. 3) and subsequent multifactorial ANCOVAs.

To validate the ATAI scale, correlations between the scores in the two ATAI scales and the items on the willingness to use specific AI products were calculated in the German and Chinese samples. As some of the items assessing the willingness to use specific AI products did show a marked deviation from the normal distribution in accordance with the criteria by Miles and Shevlin [33] in at least one of the (sub-)samples, we opted for partial Spearman correlations with age included as covariate (see Sect. 3). These correlations were also calculated separately for males and females in the German and Chinese samples.

Lastly, all analyses were also calculated based on a sample excluding all univariate outliers in the ATAI scales (N = 1 in the German sample, N = 11 in the Chinese sample, N = 0 in the sample from the UK), classified by the formula defined by Tukey [34]. The results did not differ meaningfully depending on whether outliers were in- or excluded. Therefore, we decided to present results including all participants, including those who were classified as univariate outliers.

The PCAs revealed two eigenvalues greater than 1 in each sample (Germany: 2.26, 1.06; China: 2.38, 1.20; UK: 2.31, 1.11), allowing for the extraction of two components. The item loadings on the oblimin rotated components reflected the wording of the items. Specifically, items reflecting positive attitudes towards AI (items 02, 04) loaded positively on one component (more specifically, on the second component with smaller eigenvalues), while loading from weakly positively to negatively on the other component (more specifically, the first component with higher eigenvalues). Items that represent negative opinions towards AI (items 01, 03, 05) showed the opposite pattern.

Descriptive statistics of the ATAI Acceptance and Fear scales derived from the PCAs are presented in Table 3 for each sample separately. Descriptive statistics of the items on the willingness to use the specific AI products are presented in the Supplementary Material alongside descriptive statistics on the items on whether participants actually use(d)/interact(ed) with specific AI products.

Partial Spearman correlations (controlled for age) between the ATAI Acceptance and Fear scales and the items on the willingness to use specific AI products for the German and Chinese samples are presented in Table 4.

As can be seen in Table 4, the ATAI Acceptance scale correlated substantially with all items on the willingness to use specific AI products in both samples from Germany and China. These correlations were all positive. In contrast, the ATAI Fear scale correlated negatively with these items in both samples from Germany and China. The observed associations were weaker compared to the correlations between the ATAI Acceptance scale and the items on the willingness to use the specific AI products.

As seen in Table 5, also in the male and female German and Chinese samples, there were moderate to strong positive correlations of the ATAI Acceptance scale with the items on the willingness to use specific AI products. On the other hand, there were weak to moderate negative correlations of the ATAI Fear scale with these items (several of the correlations were not significant). There was only one exception: The correlation between the ATAI Fear scale and the item on the willingness to use Pepper in the German male sample was just above zero.

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