Events during the year 2017 proved that an old discipline has not lost its relevance. Hermeneutics, the methodology and study of interpretation, is becoming increasingly important to distinguish signals from noise, facts from opinions and perspectives, and to distinguish verity from illusion or deception.

Our oberservations

  • At the beginning of this year, Kellyanne Conway coined the term ‘alternative facts’ to defend the attendance numbers at President Trump’s inauguration. Alternative is a step further than fake news as it rejects the idea of objective state of affairs (objective metaphysics) and assumes a radical kind of perspectivism (ontological relativism).
  • In 2017, widespread criticism against societal and juridical misconducts, such as #OscarSoWhite, #MeToo, and BlackLivesMatter, gained further momentum. What is more, this year, they have led to actual political discussions and handling (e.g. more diversity in this year’s Oscar nominations). However, the analyses and outcomes of these debates lack consensus: some claim that genderswapped roles are the solution to ‘Hollywood’ while Afro-American right activists claim that #MeToo is hypocrite because it was largely white women who revealed their sexual  harassment. The debates are still ‘fluid’, and the fundamental problems and moral imperatives are still moving towards a stable equilibrium (the iron is still hot, but not yet struck).
  • Politics is becoming increasingly ‘spectacular’: although political discourse has always been about persuasion and framing, social media has increased this tendency, leading to an ‘ocular democracy’ in which image and impression have become more important than verity.
  • Data, about which we have written before, is exploding: we are creating so much data a day. Compared to the whole period from the dawn of civilization to 2003, 90% of all the data comes from the last two years. We have written about it before how data and AI systems have become new sources of religion.

Connecting the dots

Hermeneutics is etymologically derived from Hermes, an Olympian God who brought the word of Zeus to the mortals to earth in Greek mythology. It means that every interpreter, those who apply hermeneutics to texts, verbal and non-verbal communication, translate the words of God or the divine Scripture to
mankind. Although it has lost this religious connotation, hermeneutics is still the study to discover or disclose what is hidden and meant in the message. However, there is a strong perception that information that is reduced to its ‘data form’ in combination with computerized learning analytics, such as
artificial intelligence, can solve most of our problems and questions of our time, and that hermeneutics has become redundant.
However, it is a misconception to perceive data as the answer to most of our debates. First of all, from data alone we cannot infer values: from state of affairs (things that ‘are’) we cannot jump towards moral imperatives (how
things ‘ought’ to be). We cannot analyze what we ought to do with #MeToo or BlackLivesMatter from analyzing data alone. To fight these misconducts we need values, something which the philosophical tradition of hermeneutics
provides. Furthermore, because of the inductive nature of reasoning is based on data, we might overlook ‘Black Swans’: because you have observed 100 birds that are white, it does not mean that all birds are white. In this way,
relying on data and opinions of others might prevent us from seeing the next revolution.
Moreover, data is still no information: abstract coded strings do not actually inform us about anything. Instead, data might actually mirror our preconscious conception of the world, thus we interpret data from this world view.
As such, these data are interpreted in a way that the world starts mirroring our world view, hence reinforce our beliefs. This is what German philosopher Martin Heidegger calls the ‘hermeneutical circle’. In this way, we should
be skeptical whether data and AI can provide sufficient answers to our most demanding problems, such as climate change, poverty, social unrest and unhappiness, and of course moral and existential problems (like ‘why are
we here?’). Sociologist Max Weber developed this idea further, and he stated that the method of ‘Verstehen’ should always be used for explaining social phenomena, compared to ‘Erklären’, which is the method of the natural
sciences. For example, understanding political and social behavior is something different from explaining why it takes time X for object A to reach place B, because the latter is about intentional and meaningful behavior,  whereas the former is the movement of dead matter.
The first needs perspective, empathy, and a comparative-historical approach, while the latter requires same mathematical equations. We have written before how social phenomena are susceptible to contagion, hence how one event can seize all attention and lead to a snowball effect across a wider range of topics and issues. 2017 has been such a year in which many of these socio-political
debates and discussions have erupted and gained momentum simultaneously. That will increase demand for trustworthy partners that understand and explain these issues for us. Depending on the matter and problem, new teachers, media
companies, experts, political leaders, business ideas, heroes, gurus will
rise to provide new narratives, alternative perspectives, and new guidelines.

Implications

  • New hermeneutical jobs in combination with quantitative data analysis will emerge. For example, someone who manages your portfolio of genes and future biological modifications, taking into account your world view and personal preferences; ethical councilors for your lifestyle and consumption decisions, like groceries, clothing, traveling; or manachine coaches to mediate the bridge between the meaning of computerized output and human intentional communication.
  • Platforms will face a trade-off to generate meaningful data: platforms with a more precise business proposition will generate more data that can be more easily transformed into information but lacks scope, while general platforms have a large reach but generate ‘abstract’ data that requires much interpretation.