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Mastering Communication: The Why, How, and What

Mastering Communication: The Why, How, and What

by Marcin Swierkocki | Jan 14, 2025 | Agile Leadership, Leadership Tips, Short Knowledge Bits | 0 comments

Mastering Communication: The Why, How, and What

Do you prefer to listen to this article? Click below to access our AI-generated audio version!

Mastering Communication: The Why, How, and What

Have you ever heard an employee say something like, “I won’t be able to meet the upcoming deadlines”? While the words may seem straightforward, they often hint at deeper underlying concerns. In this article, I delve into this statement through the lens of two well-known iceberg models, uncovering what lies beneath the surface…..

The iceberg models of Paul Watzlawick (of communication) and William Ury’s/Roger Fisher’s and Daniel Shapiro’s (positions vs. interests) share a common structure that emphasizes the elements of human interaction. Aligning these two models provides a deeper understanding of the interplay within communication strategies.

Watzlawick’s communication iceberg model:

  1. Above the surface – Fact-based communication: The pure content of the communication, such as words, facts or data communicated to the interlocutor. E.g.: ‘I want to complete this project by the end of this week’.
  2. Below the surface – Relational communication: The unspoken and emotional dynamic that underlies the actual content, such as tone, trust, non-verbal signals, and power. E.g.: A leader’s tone of voice suggests frustration or urgency, which influences how the message is received.

Ury and Shapiro’s iceberg model – positions vs interests:

  1. Above the surface – Positions: Explicit messages about what someone wants. Positions are noticeable but do not yet reveal underlying motivations. E.g.: ‘I need a 10% increase in my head count next year’.
  2. Below the surface – Interests: The deeper needs, desires, fears, and motivations that drive a position. Interests often refer to humanistic needs such as autonomy, recognition, or security. E.g.: Asking for a higher amount of workers next year reflects my need for recognition, security, or justice.

Both models emphasize the importance of understanding not only what is visible, but also what lies beneath the surface in human interaction. They agree with each other to the following extent:

Watzlawick’s iceberg model of communication

Ury’s/Shapiro’s iceberg model – positions vs interests

Fact-based communication (above the surface): Focuses on the pure content of the communication, such as words, facts or data.

Positions (above the surface): A clear request or message about what is wanted.

Relational communication (below the surface): Focuses on implicit dynamics such as tone, emotions, trust and the quality of the relationship.

Interests (below the surface): The deeper needs, concerns and motivations driving our positions.

Key findings: What is said is influenced by relational aspects (e.g. trust, power). Ignoring relational dynamics quite often leads to misunderstandings.

Key findings: Specific positions often mask hidden interests. Only an understanding of mutual interests can lead to a more constructive outcome.

Communication models

Practical implementation (1):

1. Communication while staying in touch with your Stakeholders:

Factual communication (based on your position) only serves as a starting point in reaching a solution, while relational communication (tone, trust) determines how your position is perceived. Recognizing deeper interests guides relational communication to build rapport and mutual understanding.

2. Conflict resolution:

Relationship breakdown (the hidden dynamic in Watzlawick’s model) results from long-established positions (evident in the Ury/Shapiro model). Addressing relational issues (e.g. respect, autonomy) generally leads to the discovery of shared/agreed interests.

3. Key questions so as to ‘dive below the surface’:

  • What is being said by the other side? (Positions/facts)
  • How is it being said? (Relationship dynamics)
  • Why is it being said? (Interests/basic motivations).

Practical implementation (2): Conflict in the workplace.

Scenario: An employee says: ‘I am unable to meet the upcoming deadlines’.

  • Watzlawick’s perspective: The actual message is failure to meet a deadline; the relational message may (our assumption) include frustration or fear of being perceived as an incompetent co-worker.
  • Ury’s/Shapiro’s perspective: The position is to refuse to meet a deadline; the interest may be to avoid burnout, being overloaded, or the need for greater clarity on priorities.

Conclusions:

By addressing both relational communication (e.g. showing empathy, and understanding people’s psychological needs) and underlying interests (e.g. redistribution of tasks or a new role in the team), a leader is able to solve issues/problems more effectively than by merely focusing on positions or factual messages.

Marcin Swierkocki

Marcin Swierkocki

Trainer, Coach & MDI Partner

Marcin Swierkocki works as an HR business consultant who has specialized in L&D, change- and project management. He brings over 25 years of international experience in change management and operational development with him. His personal motto is influenced by Viktor Frankl: ‘Between stimulus and response there is a space. In that space is our power to choose our response. In our response lies our growth…’. His personal inspiration comes from his optimistic and positive character and by draining the energy that successfully supporting others gives him.

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Use It or Lose It: How AI and Digital Tools May Be Changing Our Brains

by Florian Biedermann | 26. May 2026 | Leadership and AI, Leadership Tips, Learning Transfer | 0 Comments

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by Meike Hinnenberg | 19. May 2026 | Impuls series, Leadership and AI, Leadership in the digital transformation | 0 Comments

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by Jana Wölfl | 13. May 2026 | Leadership Impact, MDI Spotlight Series, Training Insights | 0 Comments

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by Gunther Fürstberger | 14. April 2026 | Leadership and AI, Leadership Tips, MDI Whitepaper | 0 Comments

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What AI Shows You — and What It Doesn’t

by Meike Hinnenberg | 1. April 2026 | Digital Transformation, Impuls series, Leadership and AI | 0 Comments

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Leading in the Age of AI: How AI Discourse Shapes Responsibility and Power

by Meike Hinnenberg | 18. March 2026 | Impuls series, Leadership and AI, Leadership Tips | 0 Comments

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AI Ethics for Leaders: Why Context and Critical Thinking Matter More Than Ever

by Meike Hinnenberg | 11. March 2026 | Impuls series, Leadership and AI, Learning Transfer | 0 Comments

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A Success Story – When AI Sharpens Human Judgement

by Claude MacDonald, Rafael Ungvari | 6. March 2026 | Customer Story, Digital Transformation, Leadership and AI | 0 Comments

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How Do You Lead People Who Don’t Think the Way You Do?

by Zeca Ruiz | 4. February 2026 | Leadership Impact, Leadership Tips, Learning Transfer | 0 Comments

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How You Deal With Neurodiversity as a Leader

by Iris Kandlbauer | 3. February 2026 | Leadership Impact, Leadership Tips, Short Knowledge Bits | 0 Comments

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Self-Leadership: Harnessing the Power Within

Self-Leadership: Harnessing the Power Within

by Meike Hinnenberg | Dec 17, 2024 | Best Practice, Leadership Tips, Short Knowledge Bits | 0 comments

Self-Leadership: Harnessing the Power Within

Do you prefer to listen to this article? Click below to access our AI-generated audio version!

Self-Leadership: Harnessing the Power Within

In today’s fast-paced and unpredictable world, we often lack control over external circumstances or how others treat us. However, we possess one powerful tool: the ability to choose how we respond.

This doesn’t mean conforming to every situation or forcing solutions. Instead, self-leadership focuses on understanding and managing our thoughts and emotions —skills that can profoundly influence our relationships and leadership abilities.

The Core of Leadership: The “Me” Circle

Imagine leadership development as a ripple effect. At the center lies the self—the starting point for influencing all outer circles: individuals, teams, organizations, and the wider environment. The “me” dimension is foundational.

How we connect with others or design organizational systems stems directly from our inner state. For now, let’s focus on this pivotal core: self-leadership.

Key Dimensions of Leadership we Focus on

Empathy vs. Sympathy: Choosing Connection

Empathy is a cornerstone of self-leadership, fostering connection, while sympathy can unintentionally create distance. According to Brene Brown, empathy encompasses:

  • Perspective-taking: Seeing the world through someone else’s eyes.
  • Non-judgment: Withholding judgment about others’ experiences.
  • Emotional recognition: Identifying and validating others’ emotions.
  • Feeling with others: Truly sharing in their emotional experience.

Leaders often struggle with vulnerability, imperfection, and the uncontrollable aspects of life and relationships. Our biological wiring doesn’t help—we’re programmed to detect and react to potential threats, a survival mechanism that can hinder connection. Recognizing and addressing this is key to empathetic leadership.

Cultivating “Response-Ability”

While we can’t control external events, we can control how we react—our “response-ability.” Tools like deep, conscious breathing and regular meditation can help calm the brain’s alarm system, creating space between stimulus and response. Gratitude practices can shift our focus toward the positive, reducing the weight of perceived threats.

These habits empower leaders to make decisions thoughtfully, even amid complexity and conflicting needs.

Leadership Decision-Making: Navigating Complexity

Making decisions as a leader is inherently challenging. It’s impossible to satisfy everyone, and every decision invites criticism. Understanding this reality helps us navigate conflict, a normal part of organizational life

Decisions will always carry trade-offs, but by including diverse perspectives and maintaining self-awareness, leaders can strive for the best possible outcomes.

Embracing Your Inner Team

Self-leadership also involves recognizing and harmonizing the many voices within us—our “inner team.” Each voice represents a perspective or trait, such as the Perfectionist, the Empathetic, or the Fearful. Listening to and integrating these voices strengthens decision-making and communication.

Embracing your inner Team

Practical Exercise:

Start by asking yourself a question, like, “Should I address this conflict?” Visualize the various inner voices involved and assign them roles or characters. For instance:

  • The Perfectionist: Insists on flawless execution.
  • The Fearful: Warns of potential risks.
  • The Rational: Seeks logic and balance.
  • The Angry: Advocates fiercely for boundaries.

Identify which voices are louder or more resistant and allow each to speak. Then, appoint an “inner team leader” to mediate, ensuring all perspectives are heard and integrated into a coherent plan.

In my own inner team, I balance voices like the empathetic, the strong, the fearful, and the rational. Each one has value, and my inner leader works to harmonize them, fostering collaboration and guiding decisions that reflect the best of all perspectives.

Conclusion

Self-leadership isn’t about perfection—it’s about awareness and intentionality. By cultivating empathy, practicing response-ability, and embracing the wisdom of your inner team, you can make decisions with clarity and confidence.

When we lead ourselves effectively, we empower others to thrive, creating ripples of positive change that extend far beyond the “me” circle.

Meike Hinnenberg

Meike Hinnenberg

Learning & Development Consultant

Meike Hinnenberg is a trainer at MDI Management Development GmbH and specializes in communication, conflict management, diversity & inclusion, and lateral leadership.

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Use It or Lose It: How AI and Digital Tools May Be Changing Our Brains

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by Gunther Fürstberger | 14. April 2026 | Leadership and AI, Leadership Tips, MDI Whitepaper | 0 Comments

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What AI Shows You — and What It Doesn’t

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Leading in the Age of AI: How AI Discourse Shapes Responsibility and Power

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by Meike Hinnenberg | 11. March 2026 | Impuls series, Leadership and AI, Learning Transfer | 0 Comments

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A Success Story – When AI Sharpens Human Judgement

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Efficiency with AI in Leadership Development

Efficiency with AI in Leadership Development

by Gunther Fürstberger | Oct 31, 2024 | Best Practice, Leadership and AI, MDI Inside | 0 comments

Efficiency with AI in Leadership Development

Do you prefer to listen to this article? Click below to access our AI-generated audio version!

Efficiency with AI in Leadership Development

Perhaps the first AI hype is already over, but it paves the way for a more realistic view. AI transformation is progressing at different speeds in different industries.

However, the training industry is among the pioneers: machine learning has enormous potential to make human learning more efficient, effective, and cost-effective. Since 2023, we have been experimenting intensively with artificial intelligence as a provider of leadership development and are happy to share our experiences.

AI in everyday business: a fixed routine in meetings

For a year and a half, AI has had a permanent place in every company management meeting. At least one current topic is supplemented by an LLM (Large Language Model) with a perspective or recommendation.

This practice has proven itself and enables more informed decisions with minimal time investment. Experiments with automatic logging, on the other hand, have proven less successful.

In August 2023, we launched AICO (Artificial Intelligence Co-Trainer), which has since been supporting us with energy exercises, music selection, transfer tasks, and as a knowledge source for participant questions in the seminar. Shortly after that, we tested the first ChatGPT role-playing game in a seminar and were impressed by the positive participant feedback. Initially, we provided the prompts as a transfer task.

AI transformation roadmap: planning at the company level

In January 2024, we launched a company-wide AI transformation roadmap. This means that we plan how AI can be used in key areas of the company every quarter to ensure that the entire organization is involved in this development.

In our OKR (Objectives and Key Results) planning meeting for the second quarter, we determined that no offer would be sent to customers without an AI component that covers at least 10% of the learning journey. Customers will still have the option to book the AI components that were previously free of charge.

AI transformation roadmap: planning at the company level

AI Lab: A role-playing center for real-world scenarios

In the third quarter, we plan to set up an AI lab on our website where all applications can be tested and used in a bundled form. This lab includes a role-playing center for various leadership communication situations. Participants take on the role of the leader, while the AI takes on the role of the employees.

A keyboard is no longer necessary – the role play is done by speaking and listening. After completion, the leaders receive targeted feedback that refers to the learned communication models. The role play can be repeated as often as desired, not only in the seminar but also when the situation arises in real life.

The future of learning: from 2D avatars to “superpowers”

Just as e-learning has replaced knowledge transfer in training over the last 20 years, AI has the potential to revolutionize skills development today. Currently, role-playing on the phone is still purely auditory, but soon visual avatars will be integrated as role-playing partners.

AI applications with 3D avatars for virtual reality glasses already exist. However, we suspect that 2D avatars will initially be used as learning partners because they are easier to use. These avatars are already supported by so-called “superpowers”. For example, Hume.ai gives AI coaches empathy and enables feedback on the current emotional state of the role-playing participant using voice analysis.

Real avatars as knowledge messengers and the next step in learning transfer

We are experimenting with “real avatars” of trainers who are available to participants as knowledge messengers after the seminar. This new type of learning transfer support enables agile learning when needed. Despite all efforts, learning transfer has often been a neglected topic in the industry.

Our leadership agent project (“LAgent”) is proving more challenging: we are developing an AI agent that can proactively pursue short-and long-term goals together with the leader. However, the LLMs currently available are still limited in their memory function, which would be necessary for responsible co-leadership. But this is precisely where development is progressing rapidly, and it is becoming increasingly easier to develop AI agents.

Conclusion: AI transformation in learning and development

Overall, the AI transformation in the area of learning and development is a fascinating field and is contributing significantly to the development of organizations and, by extension, society.

Mag. Gunther Fürstberger

Mag. Gunther Fürstberger

CEO | MDI Management Development International

Gunther Fürstberger is a management trainer, author and CEO of Metaforum and MDI – a global consulting company providing solutions for leadership development. His main interest is to make the world a better place through excellent leadership. He has worked for clients including ABB, Abbvie, Boehringer Ingelheim, DHL, Hornbach, PWC and Swarovski. His core competence is leadership in digital transformation. He gained his own leadership experience as HR Manager of McDonald’s Central Europe/Central Asia.  At the age of 20 he already started working as a trainer.

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Step by Step to Success with AI-Powered Solutions

Step by Step to Success with AI-Powered Solutions

by Marina Begic | Oct 31, 2024 | Best Practice, Digital Transformation, Leadership and AI | 0 comments

Step by Step to Success with AI-supported Solutions

Do you prefer to listen to this article? Click below to access our AI-generated audio version!

Step by Step to Success with AI-supported Solutions

A successful AI transformation in human resources development does not happen overnight. It is a process that requires a strategic approach and an agile implementation. How do you get employees on board? Combining both poles.

Sound familiar? Employees and colleagues approach you and tell you about the latest AI apps they are trying out. Many of them mention, some do not, that they do not even know whether they are allowed to use AI tools in the company. Your company may be working on a policy right now. There may not yet be a company-wide regulation or it may not yet be known. Don’t worry, you’re not alone in this attitude; 80% of companies are also waiting.

The importance of AI integration for employee motivation and competitive advantage

This could be fatal, on the one hand, because the initial motivation of employees, which is so often desired from the point of view of personnel development and leadership, could be curbed as a result. On the other hand, it could put them at a competitive disadvantage. A possible first-mover advantage shrinks with every day that passes without working on optimization using AI.

In addition to the motivation, some have no interest in the topic of AI and think they can “sit it out” or are unable to exert any influence anyway. So how can we get everyone on board? After all, as we know, we move faster when we’re all rowing in the same direction.

AI Readiness Check: assess the starting position.

Before companies embark on the AI transformation of their HR development, it makes sense to assess their current AI readiness. An AI readiness check helps to evaluate the initial situation and discuss it together. This AI readiness check provides, for example, results in the categories of strategic orientation, know-how, culture, resources, and data after a 10-minute self-assessment.

With a common starting point, it is easier to identify opportunities for integrating AI into the L&D strategy. Such a readiness check can be used in any area of the company.

AI Transformation (1000 x 600 mm)

Agile approach – AI Autonomy Day

Organize an AI Autonomy Day: Provide everyone with a day a year to work on any topic in the field of AI. Provide as little guidance as possible. Also, provide a small budget for any licenses. It is important to note that, by the GDPR, no personal or company data should be transferred to unprotected LLMs.

Trying it out can happen individually, in a team, across departments/hierarchies/teams. Provide a communication platform and make it transparent which topics are being worked on. Set a date for presenting the results. Invite relevant stakeholders and decide together what will be done with the results. Repeat the process every six months or even every quarter.

To take it to a strategic level, we recommend setting up an AI transformation roadmap. This roadmap will track the progress and plans for each area. You can decide whether to share the roadmap with everyone individually or by team/area. The results and the maturity level of the project should be recorded by the company management.

This tool offers you the following advantages:

  • Clear vision and strategic direction
  • Improved communication and alignment with stakeholders
  • Controlled resource and time management
  • Risk reduction and adaptability
  • Focused innovation and development of use cases
  • Structured approach – a clear process gets the ball rolling

To find out what leverage AI can have in your company, you need to have a basic knowledge of AI and the (technical) possibilities of AI (weak AI vs reactive AI vs AI with limited storage capacity) . On the other hand, you need to know your processes very well. Those who already have a detailed process map have a clear advantage here.

As a first step, evaluate the AI potential based on your processes and divide your processes into three process types. Then evaluate the savings potential of the activity to determine your starting point with the greatest potential.



Weak AI vs. Reactive AI vs. AI With Limited Storage Capacity

Weak AI: performs tasks, does not learn new skills (voice assistants, generative AI: image generation, text generation, research & diagnosis)

Reactive AI: does not build up memory, reacts to stimuli (spam filters, recommendation systems based on historical data such as Netflix, Amazon)

AI with limited storage capacity: stores knowledge, learns from it and makes forecasts (weather, finance, self-driving cars, chatbots)

(Source: https://ki-campus.org/videos/ki-in-unserem-alltag?locale=de)

Further training & communication

To use the topic of AI profitably and responsibly for everyone in personal development, in the team, and the company, you cannot avoid acquiring a basic knowledge of AI yourself and motivating others to do the same.

Join communities, exchange ideas with peers, and take small steps. But take them consistently and lead by example. Support your colleagues and managers with training, workshops, exchanges, and networking opportunities. Create personalized wikis and AI assistants (e.g. using GPT Builder) to make knowledge more accessible.

 

Marina Begic

Marina Begic

Head of Business Development – Digital Transformation Driver

Marina has been working on new, effective learning methods and the future of corporate learning for over 15 years. In her current role, she is responsible for Digital Business Development at MDI, where her focus is not driven by the current buzzwords, but primarily on the feasibility of digital transformation for clients such as Erste Group, Lenzing, Semperit, Deutsche Bahn, Andritz AG, Uniqa, Mayr-Melnhof, Frequentis, RHIM. Her greatest strength is bringing loose ends together, which she impressively demonstrates time and time again with her big picture view and multi-dimensional approach. Her greatest passion is to provide learners not only with an experience, but also with real, lasting value for their real challenges.

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Navigating the Ethical Challenges of Algorithmic Decision-Making

Navigating the Ethical Challenges of Algorithmic Decision-Making

by Raphaela Pouzar | Aug 27, 2024 | Leadership and AI, Leadership in the digital transformation, Short Knowledge Bits | 0 comments

 

Navigating the Ethical Challenges of Algorithmic Decision-Making

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Navigating the Ethical Challenges of Algorithmic Decision-Making

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Navigating the Ethical Challenges of Algorithmic Decision-Making

Artificial Intelligence (AI) is transforming our world, from personalized recommendations to advanced medical diagnostics. However, as AI becomes more integrated into our lives, a critical issue has emerged: algorithmic bias. This bias occurs when AI systems are trained on flawed or incomplete data that lacks diversity, leading to unfair or discriminatory outcomes and often perpetuating existing inequalities.

What is AI Bias?

AI bias arises when algorithms reflect and amplify societal prejudices embedded in their training data. For example, suppose an AI system is trained predominantly on data from one demographic group. In that case, it may perform poorly for others, leading to unfair decisions in areas like hiring, healthcare, and law enforcement.

The Dual Nature of AI

AI offers immense benefits, but these are accompanied by significant risks. In healthcare, for instance, AI can assist in diagnosing diseases, but if the training data lacks diversity, the AI may misdiagnose conditions in underrepresented groups, exacerbating health disparities.

Global Implications: Data Colonialism

AI bias isn’t just a local issue—it has global ramifications. In many cases, data from the Global South is used to train AI systems that benefit the Global North, a practice known as data colonialism. This exploitation deepens global inequalities, as those in the Global South often do not share in the benefits of AI technologies.

 

Global Implications: Data Colonialism

Addressing AI Bias

To mitigate AI bias, it is essential to adopt a responsible approach to AI development. This involves recognizing the duality of AI—its potential for both benefit and harm—and taking proactive steps to minimize risks. Here are some key strategies:

  1. Diverse and Representative Data: Ensuring that AI systems are trained on diverse and representative datasets is crucial. This includes data from different demographic groups, geographic regions, and socio-economic backgrounds to minimize bias.
  2. Transparency and Accountability: AI systems should be transparent, with clear explanations of how decisions are made. Additionally, there should be mechanisms for accountability, allowing users to challenge and understand decisions that negatively impact them.
  3. Ethics by Design: Integrating ethical considerations into the design and development of AI systems is vital. This includes ongoing assessments of how AI systems impact different groups and the potential risks involved.
  4. Global Collaboration: Addressing AI bias requires global collaboration, with input from diverse stakeholders, including those from marginalized communities. Policymakers, industry leaders, and civil society must work together to create regulations and standards that ensure AI development is fair and inclusive.

Conclusion: A Path Forward

As we move forward into an increasingly AI-driven world, it is crucial to remain vigilant about the biases embedded in these technologies. Only by acknowledging and addressing these issues can companies ensure that AI serves as a tool for positive change, rather than perpetuating existing inequalities.

Murikah, W., Nthenge, J. K., & Musyoka, F. M. (2024). Bias and ethics of AI systems applied in auditing – A systematic review. Scientific African, 16, e02281. https://doi.org/10.1016/j.sciaf.2024.e02281

Ukanwa, K. (2024). Algorithmic bias: Social science research integration through the 3-D Dependable AI Framework. Current Opinion in Psychology, 58, 101836. https://doi.org/10.1016/j.copsyc.2024.101836

Caro-Burnett, J., & Kaneko, S. (2022). Is society ready for AI ethical decision-making? Lessons from a study on autonomous cars. Journal of Behavioral and Experimental Economics, 98, 101881. https://doi.org/10.1016/j.socec.2022.101881

Arora, A., Barrett, M., Lee, E., Oborn, E., & Prince, K. (2023). Risk and the future of AI: Algorithmic bias, data colonialism, and marginalization. Information and Organization, 33(3), 100478. https://doi.org/10.1016/j.infoandorg.2023.100478

Raphaela Pouzar

Raphaela Pouzar

Marketing Team Assistant

Raphaela Pouzar, a Bachelor of Business Administration student at IMC Krems, works at MDI as a marketing assistant alongside her studies. Additionally, she is currently pursuing an AI certificate from Harvard Business School.

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