🤖〽️Prompt Engineering: Copilot Unabridged (Part 76: Accountability in the Age of Machine-Written Code)

Prompt Engineering Series

Prompt: "write a post of 600 words on who should be held accountable for the decisions taken by machines when code is written by machines as well?"

Introduction

As Artificial Intelligence (AI) continues to evolve, we are entering a new frontier - one where machines not only make decisions but also write the code that governs those decisions. This shift raises a profound ethical and legal question: Who should be held accountable when things go wrong?

When a machine writes code that leads to a faulty decision - say, a misdiagnosis in healthcare or a discriminatory hiring algorithm - the traditional chain of responsibility becomes blurred. If no human directly authored the logic, can anyone be held liable?

The Rise of Machine-Generated Code

Machine-generated code is no longer science fiction. Tools like GitHub Copilot, OpenAI Codex, and other generative AI systems can produce functional code based on natural language prompts. These systems are trained on vast repositories of human-written code and can synthesize new logic with remarkable speed and accuracy.

But they are not infallible. They can replicate bugs, embed biases, or misinterpret intent. And unlike human developers, they lack moral judgment, contextual awareness, and accountability.

The Accountability Vacuum

When a machine writes code and another machine executes it, we face a vacuum of responsibility. There’s no single human decision-maker to blame. Instead, accountability must be distributed across several layers:

• Developers: configure and supervise AI coding tools
• Organizations: deploy and monitor machine-generated systems
• Tool Creators: design the AI models that generate code
• Regulators: define standards and enforce compliance
• Users: provide input and feedback on system behavior

This layered model acknowledges that while machines may write code, humans still shape the environment in which those machines operate.

Developers as Curators, Not Creators

In this new paradigm, developers act more like curators than creators. They guide the AI, review its output, and decide what to deploy. If they fail to properly vet machine-generated code, they bear responsibility - not for writing the code, but for allowing it to run unchecked.

This shifts the focus from authorship to oversight. Accountability lies not in who typed the code, but in who approved it.

Transparency and Traceability

To assign responsibility fairly, we need robust systems for transparency and traceability. Every piece of machine-generated code should be:

• Logged: With metadata about who prompted it, when, and under what conditions.
• Audited: With tools that detect bias, security flaws, and ethical risks.
• Versioned: So changes can be tracked and errors traced to their origin.

These practices create a paper trail that helps identify where human judgment failed - even if the code itself was machine-authored.

Can Machines Be Accountable?

Some argue that as machines become more autonomous, they should bear some form of accountability. But this raises philosophical and legal dilemmas. Machines lack consciousness, intent, and moral agency. They cannot be punished, rehabilitated, or held liable in any meaningful way.

Instead, accountability must remain human-centric. Machines may act, but humans must answer.

A New Ethical Framework

To navigate this complexity, we need a new ethical framework - one that recognizes:

• Intent vs. impact: Even if no harm was intended, impact matters.
• Oversight vs. authorship: Responsibility lies with those who supervise, not just those who create.
• Systemic accountability: Errors often reflect systemic failures, not individual negligence.
• This framework helps us move beyond blame and toward responsible governance.

Conclusion: Humans Behind the Curtain

Even when machines write code, humans remain behind the curtain. They design the systems, set the parameters, and choose what to deploy. Accountability must reflect this reality.

In the age of machine-generated decisions, we don’t need scapegoats - we need stewards. People who understand the power of AI, respect its limitations, and take responsibility for its outcomes.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 75: Developers and the Weight of Machine Decisions)

Prompt Engineering Series

Prompt: "white a post of 600 words on who should be held accountable for the decisions taken by machines"

Introduction

As Artificial Intelligence (AI) systems become more autonomous and influential, the question of accountability looms large. When an AI system makes an erroneous decision - whether it denies a loan unfairly, misdiagnoses a patient, or causes a self-driving car accident - how much responsibility falls on the developers who built it?

This isn’t just a technical issue. It’s a moral and legal challenge that forces us to rethink the boundaries of human agency in a world increasingly shaped by machine logic.

Developers: Architects of Intelligence

Developers are the architects of AI systems. They design the algorithms, select training data, define objectives, and implement safeguards. Their choices shape how machines “think,” what they prioritize, and how they respond to uncertainty.

When an AI system makes a mistake, it often reflects a flaw in one of these foundational layers. For example:

• Biased training data can lead to discriminatory outcomes.
• Poor model design may cause misclassification or faulty predictions.
• Lack of explainability can make it impossible to trace errors.

In these cases, developers bear significant responsibility - not because they intended harm, but because their decisions directly influenced the machine’s behavior.

The Limits of Developer Responsibility

However, it’s important to recognize that developers operate within constraints. They rarely act alone. AI systems are built in teams, deployed by organizations, and governed by business goals. Developers may not control:

• The final application of the system
• The data provided by third parties
• The operational environment where the AI is used

Moreover, many errors arise from emergent behavior - unexpected outcomes that weren’t foreseeable during development. In such cases, blaming developers exclusively may be unfair and counterproductive.

Shared Accountability

A more nuanced view is that responsibility should be shared across the AI lifecycle:

• Stakeholder: Role in Accountability
• Developers: Design, implementation, testing
• Data Scientists: Data selection, preprocessing, model tuning
• Organizations: Deployment, oversight, risk management
• Regulators: Standards, compliance, legal frameworks
• Users: Proper use, feedback, escalation

This shared model recognizes that AI decisions are the product of a complex ecosystem - not a single coder’s keystroke.

Transparency and Traceability

One way to clarify developer responsibility is through algorithmic transparency. If developers document their design choices, testing procedures, and known limitations, it becomes easier to trace errors and assign responsibility fairly.

This also supports ethical auditing - a process where independent reviewers assess whether an AI system meets safety, fairness, and accountability standards. Developers who embrace transparency are less likely to be scapegoated and more likely to contribute to responsible innovation.

Ethical Design as a Developer Duty

While developers may not be solely responsible for every machine decision, they do have a duty to embed ethical principles into their work. This includes:

• Bias mitigation: Actively testing for and reducing discriminatory patterns.
• Explainability: Ensuring models can be understood and interrogated.
• Robustness: Designing systems that handle edge cases and uncertainty.
• Fail-safes: Building mechanisms to detect and respond to errors.

These practices don’t eliminate risk, but they demonstrate a commitment to responsible development - and that matters when accountability is on the line.

Conclusion: Responsibility Without Blame

Developers are not omnipotent, but they are not innocent bystanders either. They occupy a critical position in the AI value chain, and their decisions have real-world consequences. Holding them accountable doesn’t mean blaming them for every failure - it means recognizing their influence and expecting ethical rigor.

In the age of intelligent machines, responsibility must evolve. It’s not about finding someone to blame - it’s about building systems, teams, and cultures that prioritize safety, fairness, and transparency from the ground up.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 74: Accountability in the Age of Intelligent Machines)

Prompt Engineering Series

Prompt: "write a post of 600 words on who should be held accountable for the decisions taken by machines"

Introduction

As Artificial Intelligence (AI) systems increasingly make decisions that affect human lives - from approving loans to diagnosing illnesses and driving cars - the question of accountability becomes urgent and complex. Who should be held responsible when a machine makes a mistake, causes harm, or acts in a way that defies ethical norms?

This isn’t just a legal or technical issue - it’s a moral one. Machines don’t possess intent, conscience, or moral agency. Yet their decisions can have real-world consequences. So who bears the burden of accountability?

The Human Chain of Responsibility

At the core of any machine decision lies a chain of human involvement. This includes:

• Developers: They design the algorithms, train the models, and define the parameters. If a machine behaves in a biased or harmful way due to flawed design, developers may bear partial responsibility.
• Organizations: Companies that deploy AI systems are responsible for how those systems are used. They choose the context, set the goals, and determine the level of oversight. If a bank uses an AI model that discriminates against certain applicants, the institution - not the machine - is accountable.
• Regulators: Governments and oversight bodies play a role in setting standards and enforcing compliance. If regulations are vague or outdated, accountability may be diffused or unclear.

Users: In some cases, end-users may misuse or misunderstand AI systems. For example, relying blindly on a chatbot for medical advice without verifying its accuracy could shift some responsibility to the user.

Can Machines Be Accountable?

Legally and philosophically, machines cannot be held accountable in the same way humans are. They lack consciousness, intent, and the capacity to understand consequences. However, some argue for a form of 'functional accountability' - where machines are treated as agents within a system, and their actions are traceable and auditable.

This leads to the concept of algorithmic transparency. If a machine’s decision-making process is documented and explainable, it becomes easier to assign responsibility. But many AI systems operate as 'black boxes', making it difficult to pinpoint where things went wrong.

The Problem of Diffused Blame

One of the biggest challenges is the diffusion of blame. In complex AI systems, responsibility is often spread across multiple actors. This can lead to a scenario where no one feels fully accountable - a phenomenon known as the 'responsibility gap'.

For example, if a self-driving car causes an accident, who is to blame? The manufacturer? The software developer? The owner? The data provider? Without clear frameworks, accountability becomes a game of finger-pointing.

Toward Ethical Accountability

To navigate this landscape, we need new models of accountability that reflect the realities of machine decision-making:

• Shared Responsibility: Recognize that accountability may be distributed across stakeholders. This requires collaboration and clear documentation at every stage of development and deployment.
• Ethical Design: Embed ethical principles into AI systems from the start. This includes fairness, transparency, and safety. Developers should anticipate potential harms and build safeguards.
• Legal Frameworks: Governments must update laws to reflect the role of AI in decision-making. This includes liability rules, consumer protections, and standards for algorithmic auditing.
• Human Oversight: Machines should not operate in isolation. Human-in-the-loop systems ensure that critical decisions are reviewed and validated by people.

Final Thought: Accountability Is Human

Ultimately, accountability for machine decisions must remain a human responsibility. Machines are tools - powerful, complex, and sometimes opaque - but they are not moral agents. As we entrust them with more authority, we must also take greater care in how we design, deploy, and monitor their actions.

The future of AI is not just about smarter machines - it’s about wiser humans. And that begins with owning the consequences of the technologies we create.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 58: The Exploitable Weaknesses of AI - Risks and Challenges)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on the weaknesses of AI that can be exploited the most"

Introduction

Artificial intelligence (AI) has revolutionized industries, automating tasks, optimizing workflows, and enhancing decision-making. However, despite its advancements, AI is not infallible. Like any technology, it has vulnerabilities that can be exploited - whether by cybercriminals, unethical actors, or even unintended biases within its own algorithms. Understanding these weaknesses is crucial for ensuring responsible AI development and mitigating risks.

1. Bias in AI Models

AI systems learn from data, and if that data contains biases, the AI will inherit them. This can lead to discriminatory outcomes in hiring, lending, law enforcement, and healthcare. For example, AI-driven hiring tools have been found to favor certain demographics over others due to biased training data.

Exploiting bias in AI can be dangerous, as it can reinforce societal inequalities and lead to unfair decision-making. Organizations must actively audit AI models to ensure fairness and eliminate biases.

2. Lack of Transparency and Explainability

Many AI models operate as "black boxes," meaning their decision-making processes are not easily understood. This lack of transparency makes it difficult to detect errors, biases, or unethical behavior.

Cybercriminals and unethical actors can exploit this weakness by manipulating AI systems without detection. For example, adversarial attacks - where subtle changes to input data deceive AI models - can cause AI-powered security systems to misidentify threats or allow unauthorized access.

3. Vulnerability to Cyber Attacks

AI systems are susceptible to cyber threats, including data poisoning, model inversion, and adversarial attacks. Hackers can manipulate AI models by injecting malicious data, causing them to make incorrect predictions or decisions.

For instance, AI-driven fraud detection systems can be tricked into ignoring fraudulent transactions if attackers manipulate the training data. AI-powered facial recognition systems can also be deceived using deepfake technology, allowing unauthorized individuals to bypass security measures.

4. Over-Reliance on AI Decisions

As AI becomes more integrated into daily life, there is a risk of over-reliance on automated decision-making. Businesses, governments, and individuals may trust AI-generated insights without questioning their validity.

This can be exploited by bad actors who manipulate AI-generated recommendations for financial gain, political influence, or misinformation campaigns. AI-driven content moderation systems, for example, can be tricked into allowing harmful content while censoring legitimate discussions.

5. Ethical and Privacy Concerns

AI systems often process vast amounts of personal data, raising concerns about privacy and ethical use. If AI models are not properly secured, sensitive information can be exposed or misused.

Cybercriminals can exploit AI-driven data collection to conduct identity theft, surveillance, or targeted scams. AI-powered chatbots and virtual assistants, if compromised, can be used to extract confidential information from users.

Mitigating AI Weaknesses

While AI has vulnerabilities, responsible development and governance can minimize risks.

 Organizations must:

• Conduct regular audits to detect and eliminate biases.
• Improve AI transparency and explainability to ensure ethical decision-making.
• Strengthen cybersecurity measures to protect AI models from attacks.
• Maintain human oversight to prevent over-reliance on AI-generated decisions.
• Implement strict data privacy regulations to safeguard user information.

Conclusion

AI is a powerful tool, but its weaknesses must be acknowledged and addressed. By understanding how AI can be exploited, businesses and policymakers can take proactive steps to ensure ethical, secure, and responsible AI deployment.

Disclaimer: The whole text was generated by Copilot (under Windows 10) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 28: The Gray Areas of AI - Navigating Ethical and Practical Uncertainty)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on the gray areas of AI"

Introduction

Artificial Intelligence (AI) is transforming industries, automating processes, and reshaping human interactions. While AI offers immense benefits, it also presents gray areas - unclear ethical, legal, and societal dilemmas that challenge our understanding of responsible AI development. These uncertainties raise questions about bias, accountability, transparency, and the role of AI in decision-making.

1. AI and Bias: The Challenge of Fairness

One of the most debated gray areas in AI is bias in algorithms. AI models learn from historical data, but if that data contains racial, gender, or socioeconomic biases, AI can reinforce discrimination rather than eliminate it.

For example, AI-powered hiring systems have been found to favor certain demographics based on biased training data. Similarly, facial recognition technology has lower accuracy rates for people with darker skin tones, leading to misidentifications.

While AI developers strive to reduce bias, achieving complete fairness remains an ongoing challenge.

2. AI and Accountability: Who Is Responsible?

AI-driven decisions impact finance, healthcare, law enforcement, and hiring, but when AI makes mistakes, who is accountable?

• If an AI-powered medical diagnosis tool misidentifies a disease, is the hospital, developer, or AI itself responsible?
• If an autonomous vehicle causes an accident, should the manufacturer or AI system be held liable?
• If AI-driven financial algorithms trigger market instability, who takes responsibility?

The lack of clear accountability creates legal and ethical uncertainty, making AI governance a complex issue.

3. AI and Transparency: The "Black Box" Problem

Many AI models operate as black boxes, meaning their decision-making processes are opaque and difficult to interpret. This raises concerns about:

• Trust in AI-driven decisions, especially in high-stakes fields like healthcare and criminal justice.
• Lack of explainability, making it hard for users to understand AI-generated outcomes.
• Potential manipulation, where AI systems could be exploited without clear oversight.

Developers are working on explainable AI (XAI) to improve transparency, but full interpretability remains a challenge.

4. AI and Employment: Automation vs. Human Labor

AI-driven automation is replacing jobs in manufacturing, customer service, finance, and transportation. While AI creates new opportunities, it also raises concerns about:

• Mass unemployment, as AI replaces human workers.
• Economic inequality, where AI benefits corporations but disadvantages low-income workers.
• The need for reskilling, requiring workers to adapt to AI-driven industries.

Balancing AI automation with human labor sustainability is a critical challenge for policymakers and businesses.

5. AI and Ethics: The Moral Dilemmas of AI Decision-Making

AI lacks human intuition, morality, and ethical reasoning, yet it is increasingly used in legal judgments, hiring decisions, and medical diagnoses. Ethical concerns include:

• Should AI make life-or-death decisions in healthcare?
• Can AI-driven surveillance infringe on privacy rights?
• Should AI-generated content be regulated to prevent misinformation?

Without clear ethical guidelines, AI’s role in decision-making and governance remains a gray area.

Conclusion: AI’s Future Requires Ethical Oversight

AI’s gray areas - bias, accountability, transparency, employment impact, and ethical dilemmas - highlight the need for responsible AI development and regulation. Governments, businesses, and researchers must collaborate to define ethical AI standards, ensuring AI serves humanity without unintended harm.

Disclaimer: The whole text was generated by Copilot at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 23: How AI Can Be Tamed: Ensuring Responsible Development and Use)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI can be tamed"

Introduction

Artificial Intelligence (AI) is advancing at an unprecedented pace, reshaping industries, automating processes, and influencing decision-making. While AI offers immense benefits, its rapid growth raises concerns about ethical risks, bias, security threats, and autonomy. To ensure AI remains a beneficial tool rather than an uncontrollable force, society must take proactive steps to tame AI through regulation, ethical frameworks, and technological safeguards.

1. Establishing Ethical AI Guidelines

One of the most effective ways to tame AI is by implementing ethical frameworks that guide its development and usage. Ethical AI principles should include:

• Transparency: AI systems must be explainable, ensuring users understand how decisions are made.
• Fairness: AI models should be trained on diverse datasets to prevent bias and discrimination.
• Accountability: Developers and organizations must take responsibility for AI-driven decisions.

By embedding ethical considerations into AI development, we can prevent unintended consequences and ensure AI aligns with human values.

2. Regulating AI to Prevent Misuse

Governments and institutions must enforce AI regulations to prevent harmful applications. Key regulatory measures include:

• Data protection laws: Ensuring AI respects privacy and security standards.
• AI auditing requirements: Regular assessments to detect bias and ethical violations.
• Restrictions on autonomous weapons: Preventing AI from making life-or-death decisions without human oversight.

Without proper regulation, AI could be exploited for unethical purposes, making legal frameworks essential for responsible AI governance.

3. Controlling AI’s Energy Consumption

AI requires massive computational power, leading to concerns about energy consumption and environmental impact. To tame AI’s energy demands, researchers are exploring:

• Efficient AI models that reduce processing power without sacrificing performance.
• Renewable energy sources to power AI-driven data centers.
• Optimized algorithms that minimize unnecessary computations.

By making AI more energy-efficient, we can reduce its environmental footprint while maintaining technological progress.

4. Using Blockchain to Enhance AI Security

Blockchain technology offers a potential solution for taming AI’s security risks. By integrating AI with blockchain, we can:

• Ensure data integrity: Blockchain prevents unauthorized modifications to AI training data.
• Enhance transparency: AI decisions can be recorded on a decentralized ledger for accountability.
• Improve security: Blockchain encryption protects AI systems from cyber threats.

Combining AI with blockchain could reduce risks associated with AI manipulation and bias, making AI more trustworthy.

5. Addressing Bias in AI Models

AI systems often inherit biases from their training data, leading to unfair outcomes in hiring, healthcare, and law enforcement. Instead of eliminating bias entirely, researchers suggest controlling bias to achieve fairness. Strategies include:

• Diverse training datasets that represent multiple perspectives.
• Bias detection algorithms that flag discriminatory patterns.
• Human oversight to ensure AI decisions align with ethical standards.

By taming AI bias, we can create more equitable AI systems that serve all communities fairly.

Conclusion: AI Must Be Guided, Not Feared

AI is not inherently dangerous, but without proper oversight, it can amplify risks related to bias, security, and autonomy. By implementing ethical guidelines, regulations, energy-efficient practices, blockchain security, and bias control, society can tame AI and ensure it remains a force for good.

The future of AI depends on how we choose to shape its development - whether as a beneficial tool or an unchecked force.

Disclaimer: The whole text was generated by Copilot at the first attempt.

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🧭Business Intelligence: Perspectives (Part 16: On the Cusps of Complexity)

Business Intelligence Series

We live in a complex world, which makes it difficult to model and work with the complex models that attempt to represent it. Thus, we try to simplify it to the degree that it becomes processable and understandable for us, while further simplification is needed when we try to depict it by digital means that make it processable by machines, respectively by us. Whenever we simplify something, we lose some aspects, which might be acceptable in many cases, but create issues in a broader number of ways.

With each layer of simplification results a model that addresses some parts while ignoring some parts of it, which restricts models’ usability to the degree that makes them unusable. The more one moves toward the extremes of oversimplification or complexification, the higher the chances for models to become unusable.

This aspect is relevant also in what concerns the business processes we deal with. Many processes are oversimplified to the degree that we track the entry and exit points, respectively the quantitative aspects we are interested in. In theory this information should be enough when answering some business questions, though might be insufficient when one dives deeper into processes. One can try to approximate, however there are high chances that such approximations deviate too much from the value approximated, which can lead to strange outcomes.

Therefore, when a date or other values are important, organizations consider adding more fields to reflect the implemented process with higher accuracy. Unfortunately, unless we save a history of all the important changes in the data, it becomes challenging to derive the snapshots we need for our analyses. Moreover, it is more challenging to obtain consistent snapshots. There are systems which attempt to obtain such snapshots through the implementation of the processes, though also this approach involves some complexity and other challenges.

Looking at the way business processes are implemented (see ERP, CRM and other similar systems), the systems track the created, modified and a few other dates that allow only limited perspectives. The fields typically provide the perspectives we need for data analysis. For many processes, it would be interesting to track other events and maybe other values taken in between.

There is theoretical potential in tracking more detailed data, but also a complexity that’s difficult to transpose into useful information about the processes themselves. Despite tracking more data and the effort involved in such activities, processes can still behave like black boxes, especially when we have no or minimal information about the processes implemented in Information Systems.

There’s another important aspect - even if systems provide similar implementations of similar processes, the behavior of users can make an important difference. The best example is the behavior of people entering the relevant data only when a process closes and ignoring the steps happening in between (dates, price or quantity changes).

There is a lot of missing data/information not tracked by such a system, especially in what concerns users’ behavior. It’s true that such behavior can be tracked to some degree, though that happens only when data are modified physically. One can suppose that there are many activities happening outside of the system.

The data gathered represents only the projection of certain events, which might not represent accurately and completely the processes or users’ behavior. We have the illusion of transparency, though we work with black boxes. There can be a lot of effort happening outside of these borders.  

Fortunately, we can handle oversimplified processes and data maintenance, though one can but wonder how many important things can be found beyond the oversimplifications we work with, respectively what we miss in the process. 

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🧭Business Intelligence: Perspectives (Part 9: Dashboards Are Dead & Other Crap)

Business Intelligence Series

I find annoying the posts that declare that a technology is dead, as they seem to seek the sensational and, in the end, don't offer enough arguments for the positions taken; all is just surfing though a few random ideas. Almost each time I klick on such a link I find myself disappointed. Maybe it's just me - having too great expectations from ad-hoc experts who haven't understood the role of technologies and their lifecycle.

At least until now dashboards are the only visual tool that allows displaying related metrics in a consistent manner, reflecting business objectives, health, or other important perspective into an organization's performance. More recently notebooks seem to be getting closer given their capabilities of presenting data visualizations and some intermediary steps used to obtain the data, though they are still far away from offering similar capabilities. So, from where could come any justification against dashboard's utility? Even if I heard one or two expert voices saying that they don't need KPIs for managing an organization, organizations still need metrics to understand how the organization is doing as a whole and taken on parts. 

Many argue that the design of dashboards is poor, that they don't reflect data visualization best practices, or that they are too difficult to navigate. There are so many books on dashboard and/or graphic design that is almost impossible not to find such a book in any big library if one wants to learn more about design. There are many resources online as well, though it's tough to fight with a mind's stubbornness in showing no interest in what concerns the topic. Conversely, there's also lot of crap on the social networks that qualify after the mainstream as best practices. 

Frankly, design is important, though as long as the dashboards show the right data and the organization can guide itself on the respective numbers, the perfectionists can say whatever they want, even if they are right! Unfortunately, the numbers shown in dashboards raise entitled questions and the reasons are multiple. Do dashboards show the right numbers? Do they focus on the objectives or important issues? Can the number be trusted? Do they reflect reality? Can we use them in decision-making? 

There are so many things that can go wrong when building a dashboard - there are so many transformations that need to be performed, that the chances of failure are high. It's enough to have several blunders in the code or data visualizations for people to stop trusting the data shown.

Trust and quality are complex concepts and there’s no standard path to address them because they are a matter of perception, which can vary and change dynamically based on the situation. There are, however, approaches that allow to minimize this. One can start for example by providing transparency. For each dashboard provide also detailed reports that through drilldown (or also by running the reports separately if that’s not possible) allow to validate the numbers from the report. If users don’t trust the data or the report, then they should pinpoint what’s wrong. Of course, the two sources must be in synch, otherwise the validation will become more complex.

There are also issues related to the approach - the way a reporting tool was introduced, the way dashboards flooded the space, how people reacted, etc. Introducing a reporting tool for dashboards is also a matter of strategy, tactics and operations and the various aspects related to them must be addressed. Few organizations address this properly. Many organizations work after the principle "build it and they will come" even if they build the wrong thing!

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