The Tacit Knowledge Blog Series 6/6

Enzo Fenoglio
Enzo Fenoglio

Tuesday, November 22nd, 2022

Tacit Knowledge Ethical and Societal Implications

Organizations should be vigilant about the knowledge management procedures for transferring tacit knowledge to be fair and equitable for human participants during an elicitation process. More specifically, from a societal and ethical standpoint, we can restrict three points of interest that broadly track data, model, and impact.

Another aspect to consider of tacit knowledge is when it collides with intellectual property (IP) and patent law jurisdiction. In this blog, I describe some of these issues.

Data, Model, and Impact

Ethical and societal concerns are inevitable for a cognitive system. Organizations should be vigilant about the knowledge management procedures for transferring tacit knowledge to be fair and equitable for human participants.

Once successfully trained, the cognitive system (blog#5) will operate in an industrial environment to allow new hires for on-site training. All the aspects of knowledge management shall be considered: from knowledge creation to knowledge transfer, from knowledge sharing to knowledge governance. Should the system operate with a conversational AI user interface, impact assessment for the creation and use of the interface shall be conducted by an independent organization before deploying the system. More specifically, from a societal and ethical standpoint, we can restrict three points of interest that broadly track data, model, and impact:

  • Data relates to concerns about what is used and how the data is collected. We note above that we propose using technical documentation and internal reports rather than video and audio assessment regarding what data is used. Notwithstanding this, our approach lends itself to others using this data. That is problematic because collecting emotive data (such as verbal and facial expressions) requires surveillance of staff over long periods. The ethical concern here is consent and the appropriateness of the potential use of emotive data.
  • Model relates to the conceptual and symbolic layer. Here ground assumptions are made, which may be deemed contentious given that behavior analysis occurs. Concerns with bias can be raised regarding the exclusion of various types of unconscious behavior, such as rooted in variations in customs and language use — here, the danger of excluding specific sources of tacit knowledge is of concern.
  • Impact relates to how tacit knowledge is used and the readiness with which the techniques of assessing non-algorithmic factors such as unconscious, unexplained knowledge can be abused. In essence, the rendering explicit of that which is implicit can be used to monitor subliminally and possibly manipulate staff, a concern the EU AI Regulation Act (2021) raises as a critical concern. Accountability, transparency, and good governance mechanisms can address ethical and societal considerations [1].

The vibrant policy and regulatory debates and proposals relating to such systems' ethical and societal implications and management relate to this board demarcation. For example, at the state and federal levels of the United States, in the Algorithmic Accountability Act of 2022, there are both existing and proposed regulations; in the UK, in The Roadmap to an Effective AI Assurance Ecosystem, worker and talent management is a case studied extensively as a critical area of legislative concern (moving beyond surveillance to include mental autonomy and well-being).

Accountability, transparency, and good governance mechanisms can address ethical and societal considerations

– Koshiyama A., et al. - Towards Algorithm Auditing (2021)

Finally, the most advanced regulatory intervention is the proposed EU AI Regulation Act (2021), which categorizes any algorithmic system used in human resources as high-risk, requiring the highest level of governance and assurance. The significance of these developments can be thought of as going beyond engineering validation and efficacy to societal impact.

When Tacit Knowledge Collides with Patent Law

We conclude with a real case about ethical and privacy implications concerning intellectual property (IP) for tacit knowledge as an example of the collision between AI and Patent Law. One traditional method for capturing tacit knowledge is based on internal reports.

The USPTO has recently granted (2019) a patent to IBM about maintaining tacit knowledge for accelerated compliance control deployment by building a tacit knowledge graph or knowledge base comprising a semantic level focusing on knowledge that does not exist yet or is not kept up to date in a traditionally structured, well-defined, coherent set of documents.

We observe that the general idea to capture tacit knowledge into a KG is like the first stage of the cognitive pipeline described in blog#5, given the different definition for tacit knowledge. The definition of tacit knowledge as knowledge that does not exist yet applies more to implicit knowledge than to tacit knowledge because unstructured knowledge is already explicit and can be easily captured by a process, machine, or computer system, as claimed by IBM. But, the definition of tacit knowledge chosen ensures patent claims eligibility since they are not directed towards managing personal behavior, relationships, or interactions between people.

Methods that can be performed mentally or which are equivalent to human mental work are unpatentable, abstract ideas.

[USPTO: Manual of Patent Examining Procedure 2020]

On the contrary, the elicitation method described in blog#5 is not patentable in the US because, according to the Manual of Patent Examining Procedure (MPEP), methods that can be performed mentally or equivalent to human mental work are unpatentable abstract ideas.

Similar restrictions apply in other countries. In the UK, according to the Manual of Patent Practice (2022), it is not patentable a scheme, rule, or method for performing a mental act, playing a game, doing business, or a program for a computer.

The rationale is to avoid patenting a system that will result in certain harmful adverse effects on technology related to concepts performed in the human mind, which can create unintended ethical and privacy challenges despite solving critical business and social problems.

Once again, all these ethical considerations can be addressed through accountability, transparency, and good governance mechanisms but pose severe problems to those organizations that want to use tacit knowledge management principles for their business and protect their intellectual property.

Patent US#16,082A Manufacture of Iron and Steel

English inventor Henry Bessemer is well-known for inventing the first process to mass-produce steel inexpensively from molten pig iron before the open-hearth furnace (OHF): patent US#16,082A, Nov 11, 1856 "Manufacture of Iron and Steel."

The history behind this patent is also an example of tacit knowledge not captured in a patent. What is ironic in this story is that patents are generally meant to capture the tacit knowledge of inventors. This story says precisely the opposite.

The critical principle of Bessemer's steel process is the removal of impurities from the iron by oxidations. The technique is not new and has been used in East Asia for hundreds of years, but not on an industrial scale. Sir Henry Bessemer patented the method in 1856 and licensed it to four ironmasters who could not produce steel of the quality promised and immediately sued him.

After careful investigations of what went wrong, Bessemer realized that the problem was due to impurities in the iron and concluded that the solution was to know when to turn off the airflow in his process to burn off all the impurities; still, just the right amount of carbon remained.

Eventually, Bessemer set up his own company because he was the only one to know how to do it, even if he could not explain it clearly in the patent submission that could not capture all his tacit knowledge of the process. The Bessemer's steel process was in use until 1968, and it was at the origin of the industrial revolution in the UK and the US.

What’s next?

That was the last blog of the series. I hope you have enjoyed this journey and learned how Tacit Knowledge—tradition, inherited practices, implied values, and prejudgments— is a crucial part of scientific knowledge and an essential part of our lives, which makes sense of what we do when learning and acting in the world for ourselves or with the others.

I conclude the series in the words of Michael Polanyi “I shall reconsider human knowledge by starting from the fact that we can know more than we can tell”.


  1. Koshiyama, Adriano, et al. (2021). Towards Algorithm Auditing: A Survey on Managing Legal, Ethical and Technological Risks of AI, ML and Associated Algorithms. SSRN Electronic Journal. 10.2139/ssrn.3778998

Personal views and opinions expressed are those of the author.