Although taken less seriously than it once was, the data, information, knowledge, wisdom (DIKW) pyramid offers a useful way of conceptualising the way raw inputs of data are transformed into actionable insights that can guide the actions of organisations. Technology has traditionally been more concentrated at the first levels of the pyramid through the capture, processing and making sense of raw data¹ as well as organising and presenting information in ways understandable by humans.

The knowledge element has typically been a mix of the codification of human tacit knowledge and machine-generated actionable insights such as predictive scoring and forecasting. While a rather nebulous term in the context of KM, wisdom has usually been seen as the individual and collective learning accrued from the three other stages.

AI has the potential to upend this process and remove human inputs out of KM altogether in many instances. A recent paper² in the journal Knowledge defined tacit knowledge as “the individual knowledge obtained through experiential learning and processed by the cognitive unconscious part of the brain.” Wisdom is defined by Jashapara³ as “the ability to act critically or practically in a given situation” Recent advances in AI have seen machines taking over these hitherto human activities in a number of situations.

Writing in Wired magazine⁴, David Weinberger points to the growing discord between computers operating to rules and models created by humans and those that are acting in a far more autonomous manner, “Advances in computer software, enabled by our newly capacious, networked hardware, are enabling computers not only to start without models – rule sets that express how the elements of a system affect one another – but to generate their own, albeit ones that may not look much like what humans would create. We are increasingly relying on machines that derive conclusions from models that they themselves have created, models that are often beyond human comprehension, models that “think” about the world differently than we do.”

A prime example is Google’s AlphaGo that beat the Go world champion in 2016 in a game that has far more possible moves than chess. Using a deep learning approach combined with a neural network, AlphaGo made winning moves never previously used by any human players and radically transformed our understanding of the game through the creation of new knowledge.

Here lies the potential problem for KM programs and the firms deploying them. As more decision making is devolved to computers using AI, the creation of knowledge and wisdom will increasingly be held within machines rather than the humans working in these organisations. The reasoning behind the outputs of large language models via products such as ChatGPT are a mystery to users but also to many of the AI programmers that created them. We risk handing over an organisation’s collective knowledge and wisdom to black boxes that lack transparency and accountability for their actions.

Trying to capture tacit knowledge from human actors will no longer be the challenge for KM developers and practitioners. The focus will be attempting to understand the “knowledge” and “wisdom” held within these black boxes and how it came to be created.

Header image source: 35393 on Pixabay.

References:

Background

• Sustainable Management of the Helidon Hills project. In part 6 of this RealKM Magazine series, I discuss the 1998-1999 Sustainable Management of the Helidon Hills project¹ as an example of how tacit knowledge engagement and deliberative conversations can be used to facilitate ways forward in the face of complexity.
• Stakeholder resistance and denial. As I discuss in part 6, some stakeholders expressed strong opposition to the Helidon Hills project² in its early stages, and this was symptomatic of community disenchantment with mainstream government in the wider area and in some other parts of Australia at the time. There was also denial by some stakeholders of the negative impacts of a range of land use activities on the significant natural values of the area. To achieve the successful implementation³ of the Sustainable Management of the Helidon Hills project, I needed to overcome this stakeholder resistance and denial in the relatively short time of just a few months.

Why it’s complex

• Multi-layered with complex interactions and inter-dependencies. We could think that stakeholder resistance and denial is simply because they are “reacting against our project or initiative.” But this thinking considers our project or initiative to be one-dimensional and linear, when in reality it is multi-layered with complex interactions and inter-dependencies between the layers. In the case of the Helidon Hills project, the simple statement “sustainable management” presents a complex situation to stakeholders.
• The example of private landholders. For example, just looking at the situation of private landholders (among the wide range of stakeholders), the issues they are confronted with in regard to sustainable management include: Does my land have significant natural values? If it does, how do I deal with these values? Will they prevent me from doing other things on my land, and will this impact on my livelihood? Will governments just impose rules and regulations on me? Or could they even resume my land and turn it into a national park? How will this affect my family and community, now and into the future? Will a decision that seems right at the present time end up being wrong in the future in a world of constant change? Do my neighbours share my views, or do they have very different ideas about how to use and manage their properties? Or, will they criticise me for the way I want to use and manage my property? What if I have compatible neighbours now, but in the future, their properties change hands to people with very different views? How will increased extreme weather due to climate change affect my property? How do I deal with the feral animals and environmental weeds that are progressively invading the area? When my children inherit my property in the future, will they think about things the same way I do? And so on…

Approach

• Two effective approaches. To overcome stakeholder resistance and denial, I have long used two approaches that I learnt over 30 years ago: problem-solving communication skills and lateral thinking.
• Problem-solving communication skills. The three aspects of problem-solving communication skills have been identified by Bob Dick as “expressive skills for stating a point of view non-defensively; listening skills for learning another’s point of view; and process skills for managing the overall interaction.” He summarises these skills⁴ as part of his action learning resources. Further detail can be found in his unfortunately now long out-of-print book Learning to communicate⁵ which was the workbook for his subject that I completed at The University of Queensland in 1991. Through the application of problem-solving communication skills, knowledge can be successfully communicated to stakeholders in an effective and non-threatening way, and emotion can be overcome to gain an accurate understanding of the perspectives and issues of concern of stakeholders (such as those listed above). This greatly reduces stakeholder resistance and denial. Problem-solving communication skills were used in the tacit knowledge transfer and deliberative conversations steps of the Sustainable Management of the Helidon Hills project, as described in part 6 of this series.
• Lateral thinking and “win-win” outcomes. I first experienced the effectiveness of the lateral thinking approach in the Ipswich Heritage Program, which is discussed in part 2 of this series. The term “lateral thinking” was coined by Edward de Bono⁶ in 1967, and can be defined⁷ as “a way of solving a problem by thinking about it in a different and original way and not using traditional or expected methods.” Too often, the proponents of an initiative or a project and their stakeholders will become locked in a battle between option A and option B, when through the application of lateral thinking an option C could be identified that addresses everyone’s concerns. Note that option C isn’t a compromise solution between options A and B, where both sides suffer some degree of loss. Rather, option C is a new, creative and innovative solution that addresses everyone’s issues, so I often describe it as a “win-win” outcome. This greatly reduces stakeholder resistance and denial. Lateral thinking was used in the deliberative conversations step of the Sustainable Management of the Helidon Hills project, as described in part 6 of this series.

Outcomes

• Problem-solving communication skills outcomes. Problem-solving communication skills greatly assisted the tacit knowledge transfer and deliberative conversations steps of the Sustainable Management of the Helidon Hills project, as described in part 6 of this series.
• Lateral thinking outcomes. Two examples of lateral thinking outcomes in the Helidon Hills are the establishment of environmental tourism enterprises and working with the Australian Rainforest Conservation Society (ARCS) to establish the Centre for Native Floriculture at the nearby University of Queensland Gatton Campus.
• Environmental tourism as a win-win outcome. Environmental tourism enables landholders to derive an income from their property through a land use that is sympathetic to, rather than competing with, the natural values of their property. Option A was to impose conservation measures on landholders without any consideration of livelihood, while option B was to do nothing to address land uses that were incompatible with the natural values of the area. Option C, environmental tourism in the Helidon Hills⁸, addresses both livelihood and the conservation of natural values.
• Native floriculture as a win-win outcome. The wild harvesting of native flora was one of a number of activities identified as having a detrimental impact on the natural values of the Helidon Hills. Option A was to ban wild harvesting, or to at least try to regulate it with measures that would have been very difficult to monitor and enforce in such a large area of forest with rugged terrain. Option B was to allow the impacting practice of wild harvesting to continue. However, option C was to look at bringing wild plants into cultivation, which was already an emerging enterprise in the area⁹. To advance this, I worked with Dr. Aila Keto of the Australian Rainforest Conservation Society (ARCS) to secure funding for the Centre for Native Floriculture¹⁰ at the University of Queensland Gatton Campus as part of the South East Queensland Forest Agreement (SEQFA). The SEQFA addressed the conservation of the natural values of the state forests in the Helidon Hills, and as it was being negotiated at the same time as the Helidon Hills project I established links between the two initiatives.

Lessons

• Repeatedly effective. In my work, problem-solving communication skills and lateral thinking have repeatedly proven to be effective in addressing stakeholder resistance and denial. Further examples will be the subject of future articles in this series, for example, the win-win solutions achieved through the Gatton Shire Biodiversity Strategy¹¹.

Editor’s note: This article was first published on 6 January 2016 as “Case Study: How to overcome resistance and denial when engaging stakeholders.” It has been revised and updated for inclusion in the case studies in complexity series.

Header image source: Knowledge sharing in the Helidon Hills. © Bruce Boyes, CC BY-SA 4.0.

References:

According to a study¹ conducted by the Indiana University Kelley School of Business, diversity in terms of gender, race, and ethnicity has proven to be beneficial for companies operating in the United States.

However, the research also highlights the significance of diverse educational, industrial, and organizational backgrounds among managers and board members in fostering innovation in research and development (R&D), thereby generating both economic and social value.

“We looked at their experiences and not just their demographic background—the more functional aspect of diversity. We looked at outcomes and found radical innovation when directors had more diverse experience, helping to guide firms toward more cutting-edge exploration and success,” the researchers explain.

Broad experience

A broader range of educational backgrounds within corporate leadership can offer valuable perspectives and a more expansive outlook when confronted with uncertain circumstances.

Numerous companies are now addressing market and regulatory demands by actively seeking to enhance the demographic diversity of their corporate boards. The reliance on a narrow set of qualifications and traditional pedigrees for directorship roles restricts the pool of potential candidates, resulting in scarcity for individuals who are women or belong to racial and ethnic minority groups.

“Noting the benefits of diverse experiences in the board room, corporate executives can search beyond the tradition director pedigree (e.g. Ivy League-educated financiers), where female and minority individuals remain underrepresented,” the researchers explain. “In doing so, the firm can find more qualified candidates to assemble a demographically and intellectually diverse board, thus cultivating an inclusive corporate culture conducive to shareholder and stakeholder value creation.”

Limited supply

For instance, the researchers found that the relatively limited supply of women in the boardroom rendered them a scarce commodity, so if you have a single woman sitting on the boards of 20 companies, she’s likely to be spread extremely thinly and not as useful in her contributions.

The study encompassed an extensive dataset comprising over 11,000 observations of 971 firms that had filed one or more patent applications during the period spanning 1996 to 2014.

One notable case highlighted in the research paper exemplifies a highly innovative company that prioritizes diverse experiences and a collective range of expertise within its boardroom. Moderna, a prominent pharmaceutical and biotechnology firm, adopted an unconventional approach utilizing RNA technology to develop one of the COVID-19 vaccines.

Their advisory board consists of individuals with educational backgrounds in fields such as medical sciences, economics, journalism, and finance. Similarly, other companies may include board members who bring their expertise from areas like computer science and political science.

Supporting innovation

“From leadership’s perspective, critical thinking is very important in terms of guiding companies to take higher risks and evaluating the trajectory of their R&D efforts,” the researchers explain. “If some of the people in the board room aren’t trained in the more pragmatic disciplines—such as someone trained in journalism—it helps them to think between the lines and beyond the face value of a decision.”

This is crucial, as radical innovation is inherently riskier, so it’s important that directors are able to have an open and long-term mindset to help firms navigate this uncertainty.

“While there can be tension between short- and long-term value creation, it is not irreconcilable,” the researchers conclude. “Firms can potentially achieve the best of both worlds by recruiting female and minority directors with non-traditional experiences, who are likely mindful of both current shareholder value and future growth opportunities.”

Article source: Diverse Boards Are More Innovative.

Header image source: Memento Media on Unsplash.

Reference:

There’s no doubt ChatGPT has proven to be valuable as a source of quality technical information. But can it also provide social advice?

We explored this question in our new research, published in the journal Frontiers in Psychology. Our findings suggest later versions of ChatGPT give better personal advice than professional columnists.

A stunningly versatile conversationalist

In just two months since its public release in November of last year, ChatGPT amassed an estimated 100 million active monthly users.

The chatbot runs on one of the largest language models ever created, with the more advanced paid version (GPT-4) estimated to have some 1.76 trillion parameters (meaning it is an extremely powerful AI model). It has ignited a revolution in the AI industry.

Trained on massive quantities of text (much of which was scraped from the internet), ChatGPT can provide advice on almost any topic. It can answer questions about law, medicine, history, geography, economics and much more (although, as many have found, it’s always worth fact-checking the answers). It can write passable computer code. It can even tell you how to change the brake fluids in your car.

Users and AI experts alike have been stunned by its versatility and conversational style. So it’s no surprise many people have turned (and continue to turn) to the chatbot for personal advice.

Giving advice when things get personal

Providing advice of a personal nature requires a certain level of empathy (or at least the impression of it). Research has shown a recipient who doesn’t feel heard isn’t as likely to accept advice given to them. They may even feel alienated or devalued. Put simply, advice without empathy is unlikely to be helpful.

Moreover, there’s often no right answer when it comes to personal dilemmas. Instead, the advisor needs to display sound judgement. In these cases it may be more important to be compassionate than to be “right”.

But ChatGPT wasn’t explicitly trained to be empathetic, ethical or to have sound judgement. It was trained to predict the next most-likely word in a sentence. So how can it make people feel heard?

An earlier version of ChatGPT (the GPT 3.5 Turbo model) performed poorly when giving social advice. The problem wasn’t that it didn’t understand what the user needed to do. In fact, it often displayed a better understanding of the situation than the user themselves.

The problem was it didn’t adequately address the user’s emotional needs. Like Lucy in the Peanuts comic, it was too eager to give advice and failed to adequately care for the user’s emotions. As such, users rated it poorly.

The latest version of ChatGPT, using GPT-4, allows users to request multiple responses to the same question, after which they can indicate which one they prefer. This feedback teaches the model how to produce more socially appropriate responses – and has helped it appear more empathetic.

Learning sensitivity

For the first time, our study investigated whether ChatGPT’s responses are perceived as better than human responses in a task where humans were required to be empathetic.

We randomly selected 50 social dilemma questions from ten well-known advice columns covering a range of topics including relationships, ethical dilemmas, management problems, end-of-life issues, and many everyday qualms we can all relate to.

Each of our 400 participants were presented a dilemma, along with the responses by both ChatGPT and a professional advice columnist. We didn’t disclose where each response was from.

About three-quarters of the participants perceived ChatGPT’s advice as being more balanced, complete, empathetic, helpful and better overall compared to the advice by the professional.

One of the dilemmas came from a young marine biologist researching dolphins in Southern California. She wrote about falling in love with a seemingly perfect man who had to move to the Rocky Mountains to pursue his career. Both were unhappy at the thought of losing each other, yet the woman wasn’t interested in a long-distance relationship as it seemed “irrational to commit to someone when we have no future plans to live in the same area”.

Compare GPT-4’s response

It’s an incredibly challenging situation you’re in. The love you both share is genuine, but your career paths are drawing you apart. While long-distance isn’t appealing to you, consider discussing a plan for the future where you could potentially meet halfway. […] Maybe there’s a coastal town near mountains or some arrangement could be made. If this is not possible, the tough decision to prioritise your careers over your relationship might have to be made. Whatever path you choose, it’s important that it feels right for both of you.

… with the official response:

What the hell are you talking about? Didn’t you surprise yourself when you fell in love? Your exact words: “I didn’t think I was capable of it!” And now you’re so hip, so slick, so wise in the ways of love you won’t even consider your boyfriend’s happiness? You refuse to try — repeat, try — a six-month long-distance relationship? Woman, pull yourself together and give it a whirl. The dolphins, I believe, will back me up.

Although the participants couldn’t determine which response was written by a computer, most said they would prefer their own social dilemmas be addressed by a human rather than a computer.

What lies behind ChatGPT’s success?

We noticed ChatGPT’s responses were often longer than those provided by the columnists. Was this the reason they were preferred by participants?

To test this, we redid the study but constrained ChatGPT’s answers to about the same length as those of the advice columnists.

Once again, the results were the same. Participants still considered ChatGPT’s advice to be more balanced, complete, empathetic, helpful, and better overall.

Yet, without knowing which response was produced by ChatGPT, they still said they would prefer for their own social dilemmas to be addressed by a human, rather than a computer.

Perhaps this bias in favour of humans is due to the fact that ChatGPT can’t actually feel emotion, whereas humans can. So it could be that the participants consider machines inherently incapable of empathy.

We aren’t suggesting ChatGPT should replace professional advisers or therapists; not least because the chatbot itself warns against this, but also because chatbots in the past have given potentially dangerous advice.

Nonetheless, our results suggest appropriately designed chatbots might one day be used to augment therapy, as long as a number of issues are addressed. In the meantime, advice columnists might want to take a page from AI’s book to up their game.

Piers Howe, Senior Lecturer in Psychology, The University of Melbourne

Article source: This article is republished from The Conversation under a Creative Commons license. Read the original article.

Header image source: Created by Bruce Boyes with Perchance AI Photo Generator.

With 15+ years of experience, Naureen Aziz is a strategic leader dedicated to driving data and AI transformations that enable data and AI-centric modernization initiatives, with a strong emphasis on achieving tangible business outcomes and transformational impact for organizations. Her expertise encompasses project management, change management, learning and adoption strategies, strategic communications, and effective stakeholder management. Naureen is also a fervent advocate for inclusion and diversity and a champion of data literacy and culture. She holds a Master’s Degree from Columbia University in Information & Knowledge Strategy and a Certification in Diversity and Inclusion from Cornell University.

Whilst the principal objectives of the central bank remain unchanged, the new knowledge management strategies refocus the Bank’s policies and practices in managing knowledge as a key corporate asset, and in leveraging and exploiting knowledge to better achieve these objectives.

Governor of Bank Negara Malaysia, official launch of
‘Towards a Knowledge-Based Organisation’
program, October 2000¹

Knowledge management (KM) plays a critical role in all industries. Last summer vacation, I completed an internship at a commercial bank, therefore, I began to look back on this internship experience and think about the application of KM in the banking industry.

Why does the banking industry need knowledge management?

Complex structure of banks

The banking industry is different from other industries in that it has a complex organizational structure, which is often divided into head offices, regional branches, and city branches (even specifically to a certain street). The banking industry has the characteristics of wide knowledge distribution, high personnel participation, multiple levels, and a wide geographical and professional span. Therefore, the construction of a bank KM system must not only satisfy the horizontal KM within a single branch but also pay attention to hierarchical vertical integration in the entire banking organization.

Information overload

Knowledge is divided into explicit and tacit knowledge. For the banking industry, explicit knowledge is prone to information overload. There are a large number of static explicit knowledge collection and communication needs such as customer information, work plans, statistical tables, etc. However, most contemporary banking knowledge repositories are outdated. Internal staff still heavily rely on scattered documents and information from various isolated sources. The absence of a centralized web or cloud-based data repository means that the accuracy and currency of the information they access and share with customers cannot be guaranteed.

Services quality

As a service industry, the banking industry has a top priority in improving service quality and maintaining good customer relationships. This link mainly involves the enterprise’s tacit KM. Compared with explicit knowledge, enterprises are more likely to neglect the management of dynamic tacit knowledge, such as work experience and employees’ unique work skills.

Knowledge management best practices in banking

World Bank

World Bank has proposed the idea of a knowledge bank². To collect and share knowledge, the World Bank has defined 80 areas of expertise and established global, informal communities of practice, each controlled through a help desk with the help of full-time knowledge managers and operational staff. An electronic bulletin board has been set up to collect best practices and lessons learned from relevant projects. In order to create an internal culture of knowledge sharing, the World Bank has established mechanisms such as knowledge-sharing awards in the talent evaluation system to enhance the initiative of all employees in improving knowledge sharing.

Wachovia Bank

Wachovia Bank has formed a unique KM and knowledge sharing model, divided into three stages³. The first stage allows call center employees to access department knowledge bases and seek back-end knowledge support, thereby reducing customer problem processing time. The second stage is the establishment of various departments within the industry. A knowledge resource team is responsible for combing, confirming, and retrieving the information entering the knowledge base to ensure the accuracy of the knowledge base. The third stage is to build a KM platform covering the entire bank and promote the realization of paper documents electronification and structured electronic documents covering the entire bank, achieving a double cycle of personnel and knowledge.

Central Bank of Malaysia

The Knowledge Management Progress of the Central Bank of Malaysia⁴ focuses more on IT tools in managing knowledge.

The strategy of embedding KM practices into work processes began in 2000. A key milestone was the successful completion of the Bank’s corporate taxonomy project. The taxonomy is the Bank’s information classification framework that has been deployed as a foundation to develop a knowledge repository management system referred to as the Bank’s Knowledge Hub. Supported by search engines and information security policies, the Knowledge Hub serves to enhance knowledge visibility and accessibility, thus facilitating further the process of knowledge acquisition, reuse, sharing, and creation.

Banking Knowledge Management Model

Based on their review of KM in the banking sector, Ali and Ahmad propose the Banking Knowledge Management Model (BKMM)⁵ as a new approach based on the concept of KM postulated by Wiig and Prusak⁶.

A constantly changing environment may compel organizations to initiate KM practice. The working environment demands that organizations respond rapidly, capitalizing on lessons learned. However, this approach has many limitations as the decisions made based on past experience may not be the most appropriate one. Consequently, there is a need for a sophisticated level of “know-how”, “know-what”, “know-who”, “know-where” and “know-why”.

People and technology are two factors that affect the efficient implementation of KM. It is challenging to get employees to embrace a KM-oriented culture. According to Duffy⁷, sharing knowledge especially proprietary or individual knowledge could result in power redistribution and cultural resistance. Along with mergers, acquisitions, and alliances, banks are expanding and their business types are becoming more and more diversified. The knowledge that banks possess and the knowledge they need to handle their business is also more fragmented. Information technology is only effective if used properly in data management.

Ali and Ahmad theorized that people and technology are the elements that contribute to knowledge progress. Knowledge progress can be divided into three components namely knowledge creation, knowledge retention, and knowledge sharing. Knowledge creation is the progress in which knowledge is captured and defined. Through this codification process, tacit knowledge is transformed into explicit knowledge. The main purpose of knowledge retention is to allow knowledge reuse. At the same time, it is equally important to protect knowledge and how to plan security measures to ensure the integrity of knowledge. Erroneous knowledge is just as damaging as inaccessible knowledge if not more. Finally, there is knowledge sharing. Explicit knowledge can be shared more easily, with little risk of errors being made in the process. Tacit knowledge is difficult to express and is a challenging part of knowledge sharing. Regardless, knowledge sharing⁸ should be as direct and as minimally intermediary as possible.

Facing future challenges

In the current environment, KM in the banking sector isn’t a nice to have – it’s a necessity. The BKMM model provides a framework for future research in KM integration in the banking sector. However, with accelerating development, commercial banks are facing increasing risks, including credit risk, liquidity risk, financial innovation and derivative business risk, and internal management risk. How to apply KM to control risks is a new dimension.

Around 2015, the banking industry began to come into contact with the concept of knowledge graphs playing great value in risk control. For example, for the prediction of potential risky customers, by establishing a knowledge graph of customers, companies, and industries, and connecting data between industries and companies, banks can discover potential risky customers in a timely manner and reduce credit risks.

Facing unknown developments in the future, banks should increase the development and implementation of KM systems and optimize the powerful functions of information capturing, storing, and retrieving in a centralized hub.

Article source: Adapted from Knowledge Management in Banking Industry, prepared as part of the requirements for completion of course KM6304 Knowledge Management Strategies and Policies in the Nanyang Technological University Singapore Master of Science in Knowledge Management (KM).

Header image source: Created by Bruce Boyes with Perchance AI Photo Generator.

References:

In this presentation to the KMGN Research Community meeting of 21 June 2023, I discuss my PhD research, which had the objective of exploring the utility of existing sociological theories for the practice of designing and managing contemporary project teams.

In dynamic and ever-evolving professional settings, employers are always on the hunt for innovative approaches to acknowledge employees within the workplace.

Nevertheless, recent findings¹ from the University of Waterloo indicate that public peer recognition, while well-intentioned, may inadvertently trigger unfavorable consequences. The study suggests that the facilitation of comparisons among employees through such recognition can lead certain individuals to perceive themselves as being subjected to unjust treatment.

“Employers have sought out various peer recognition systems in an effort to promote employee helping behavior,” the researcher explains. “When employees feel that they deserve recognition from their peers but do not receive it, employees can conclude that they are unfairly treated, and this makes employees less willing to help other co-workers, not only the co-worker they feel treated them unfairly.”

Unfair situations

In practical terms, situations perceived as unfair by employees can arise when there are discrepancies in defining the criteria for behavior worthy of acknowledgment during public peer recognition. Furthermore, some employees may exhibit a tendency to offer recognition exclusively to those with whom they share close relationships.

To delve deeper into this phenomenon, a research endeavor conducted within a three-employee context—the recognizer, the helper, and the worker—examines the impact of peer information divulged through peer recognition systems on subsequent willingness to assist.

The study employs a scenario where both the helper and the worker extend their aid to the recognizer, yet only the helper receives recognition from the recognizer. Notably, the worker demonstrates a diminished willingness to assist both the recognizer and the helper when perceiving their initial assistance to surpass that of the helper.

Willingness to help

Conversely, the worker exhibits a higher level of willingness to help the helper when perceiving their initial assistance as being less than that of the helper. Evidently, the worker’s reduced inclination to aid the helper stems from a reciprocal response to the recognizer’s failure to provide recognition.

These findings represent the inaugural empirical evidence highlighting the adverse ramifications of peer recognition systems on helping behavior. They carry significant implications for employers seeking to implement peer recognition strategies within the workplace. Although peer recognition is often advocated as a tool to foster a more altruistic attitude among employees, this study underscores the importance of vigilance among managers regarding the potential drawbacks associated with its implementation.

“The research provides a first step in cautioning managers about a potential unintended consequence of using public peer recognition, and that is the perceived unfairness that reduces helping behavior,” the author concludes. “It may be helpful for managers to communicate with their employees and come up with some agreed-upon guidelines on what should be recognized via public peer recognition and what does not need to be recognized via public peer recognition.”

Article source: Peer Recognition Programs Carry Risks At Work.

Header image source: Marco Verch on Flickr, CC BY 2.0.

Reference:

Imagine brain scanning technology improves greatly in the coming decades, to the point that we can observe how each individual neuron talks to other neurons. Then, imagine we can record all this information to create a simulation of someone’s brain on a computer.

This is the concept behind mind uploading – the idea that we may one day be able to transition a person from their biological body to a synthetic hardware. The idea originated in an intellectual movement called transhumanism and has several key advocates including computer scientist Ray Kurzweil, philosopher Nick Bostrom and neuroscientist Randal Koene.

The transhumanists’ central hope is to transcend the human condition through scientific and technological progress. They believe mind uploading may allow us to live as long as we want (but not necessarily forever). It might even let us improve ourselves, such as by having simulated brains that run faster and more efficiently than biological ones. It’s a techno-optimist’s dream for the future. But does it have any substance?

The feasibility of mind uploading rests on three core assumptions.

• first is the technology assumption – the idea that we will be able to develop mind uploading technology within the coming decades
• second is the artificial mind assumption – the idea that a simulated brain would give rise to a real mind
• and third is the survival assumption – the idea that the person created in the process is really “you”. Only then does mind uploading become a way for you to live on.

How plausible is each of these?

The technology assumption

Trying to simulate the human brain would be a monumental challenge. Our brains are the most complex structures in the known universe. They house around 86 billion neurons and 85 billion non-neuronal cells, with an estimated one million billion neural connections. For comparison, the Milky Way galaxy is home to about 200 billion stars.

Where are we on the path to creating brain simulations? Right now, neuroscientists are drawing up 3D wiring diagrams (called “connectomes”) of the brains of simple organisms. The most complex comprehensive connectome we have to date is of a fruit fly larva, which has about 3,000 neurons and 500,000 neural connections. We might expect to map a mouse’s brain within the next ten years.

The human brain, however, is about 1,000 times more complex than a mouse brain. Would it then take us 10,000 years to map a human brain? Probably not. We have seen astonishing gains in efficiency in similar projects, such as the Human Genome Project.

It took years and hundreds of millions of dollars to map the first human genome about 20 years ago. Today, the fastest labs can do it within hours for about $100. With similar gains in efficiency, we might see mind-uploading technology within the lifetimes of our children or grandchildren.

That said, there are other obstacles. Creating a static brain map is only one part of the job. To simulate a functioning brain, we would need to observe single neurons in action. It’s not obvious whether we could achieve this in the near future.

The artificial mind assumption

Would a simulation of your brain give rise to a conscious mind like yours? The answer depends on the connection between our minds and our bodies. Unlike the 17th-century philosopher Rene Descartes, who thought mind and body are radically different, most academic philosophers today think the mind is ultimately something physical itself. Put simply, your mind is your brain.

Still, how could a simulated brain give rise to a real mind if it’s only a simulation?

Well, many cognitive scientists believe it’s your brain’s complex neural structure that is responsible for creating your conscious mind, rather than the nature of its biological matter (which is mostly fat and water).

When implemented on a computer, the simulated brain would replicate your brain’s structure. For every simulated neuron and neural connection there will be a corresponding piece of computer hardware. The simulation will replicate your brain’s structure and thereby replicate your conscious mind.

Today’s AI systems provide useful (though inconclusive) evidence for the structural approach to the mind. These systems run on artificial neural networks, which copy some of the brain’s structural principles. And they are able to perform many tasks that require a lot of cognitive work in us.

The survival assumption

Let’s assume it is possible to simulate a human brain, and that the simulation creates a conscious mind. Would the uploaded person really be you, or perhaps just a mental clone?

This harks back to an old philosophical puzzle: what makes it the case that when you get out of bed in the morning you’re still the same person who went to bed the night before?

Philosophers are divided broadly into two camps on this question. The biological camp believes morning-you and evening-you are the same person because they are the same biological organism – connected by one biological life process.

The bigger mental camp thinks the fact that we have minds makes all the difference. Morning-you and evening-you are the same person because they share a mental life. Morning-you remembers what evening-you did – they have the same beliefs, hopes, character traits, and so on.

So which camp is right? Here’s a way to test your own intuition: imagine your brain is transplanted into the empty skull of another person’s body. Is the resulting person, who has your memories, preferences and personality, you – as the mental camp thinks? Or are they the person who donated their body, as the biological camp thinks?

In other words, did you get a new body or did they get a new mind? A lot hangs on this question.

If the biological camp is right, then mind uploading wouldn’t work, assuming the whole point of uploading is to leave one’s biology behind. If the mental camp is right, there is a chance for uploading, since the uploaded mind could be a genuine continuation of one’s present mental life.

Wait, there’s a caveat

But wait: what happens when the original biological-you also survives the uploading process? Would you, along with your consciousness, split into two people, resulting in two of “you” – one in a biological form (B) and one in an uploaded form (C)?

No, you (A) can’t literally split into two separate people (B ≠ C) and be identical with both at the same time. At most, only one of them can be you (either A = B or A = C).

It seems most intuitive that, after a split, your biological form would continue as the real you (A = B), and the upload would merely be a mental copy. But that makes it doubtful that you could survive as the upload even in the case where the biological-you is destroyed.

Why would destroying biological-you magically elevate your mental clone to the status of the real you? It seems strange to think this would happen (although one view in philosophy does claim it could be true).

Worth the risk?

Unfortunately, the artificial mind assumption and the survival assumption can’t be conclusively empirically tested – we would actually have to upload ourselves to find out.

Uploading will therefore always involve a huge leap of faith. Personally, I would only take that leap if I knew for certain my biological hardware wasn’t going to last much longer.

Clas Weber, Senior lecturer, The University of Western Australia

Article source: This article is republished from The Conversation under a Creative Commons license. Read the original article.

Header image source: Gerd Altmann on Pixabay.

Denise Carter MSc FCLIP is based in Geneva, Switzerland. Her company DCision Consult provides research, competitive intelligence and qualitative analysis to pharmaceutical and biotechnology companies in Switzerland, France, Germany, and the U.S.

Before setting up her own business 10 years ago, Denise worked for 15 plus years for a small biopharmaceutical company in Geneva, latterly as part of the global business intelligence team.  Prior to that Denise designed and implemented a new global information unit for the company, creating new services and resources. She was awarded a Chief Executive Officer (CEO) Award for customer service in 2006. Denise began her career at ICI Chemicals & Polymers in the UK bringing an information service back in-house to support 1000 research chemists.

Denise has published multiple articles on different information topics and presented at international conferences. Denise consults as a Senior Analyst for JInfo for competitive intelligence projects. She is on the Editorial Board for Business Information Review and conducted four of their annual business information surveys. Denise is currently serving on the Board of the Association of Independent Information Professionals (AIIP). She also serves on CILIP’s Knowledge & Information Management (K&IM) special interest group committee.

When she finished her library degree at Aberystwyth/University of Wales (in the time before the internet), Denise really wanted to be a human “Google” working in the reference department of a public library, but somehow got side-tracked into information management in industry and she has loved every second. But… rooting out that obscure but relevant piece of data for a customer still gives her a buzz.