Showing posts with label bias. Show all posts
Showing posts with label bias. Show all posts

09 July 2025

🤖〽️Prompt Engineering: Copilot Unabridged (Part 46: Understanding AI Governance - Balancing Innovation and Responsibility)

Prompt Engineering Series
Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI governance works"

Introduction

Artificial intelligence (AI) is rapidly transforming industries, societies, and economies worldwide. Its immense potential has led to groundbreaking advancements in healthcare, finance, transportation, and more. However, with these capabilities come risks - bias in AI models, privacy concerns, and misuse of AI-powered systems. To address these challenges, AI governance has emerged as a critical framework for ensuring responsible AI development and deployment.

What is AI Governance?

AI governance refers to the policies, laws, regulations, and ethical frameworks that guide AI development and usage. It encompasses a broad spectrum of considerations, including data privacy, security, accountability, transparency, and fairness. The goal is to balance the rapid advancement of AI technology with societal norms and ethical principles.

Governance mechanisms differ across regions and industries, but they typically involve collaboration between governments, tech companies, academic researchers, and civil society groups. The underlying challenge in AI governance is ensuring AI systems benefit humanity while mitigating risks such as bias, discrimination, and security vulnerabilities.

Key Principles of AI Governance

Several fundamental principles shape AI governance frameworks across the globe:
Transparency: AI systems should be understandable and explainable. Black-box models, where the decision-making process remains obscure, can lead to concerns regarding bias and accountability.

Explainability helps foster trust among users and regulators.

  • Accountability: Organizations developing and deploying AI must take responsibility for their systems’ behavior. This includes ensuring ethical use, addressing unintended consequences, and establishing mechanisms for legal recourse when AI causes harm.
  • Privacy and Data Protection: AI systems rely on vast amounts of data, raising concerns about privacy breaches and misuse. Strong governance frameworks require compliance with data protection laws such as GDPR in Europe, ensuring users have control over their personal information.
  • Bias and Fairness: AI can inherit biases from training data, leading to discriminatory outcomes. Ethical AI governance emphasizes fairness, reducing disparities in AI-driven decisions affecting hiring, law enforcement, healthcare, and financial services.
  • Security and Safety: As AI applications expand, cybersecurity threats, deepfake technology, and AI-driven autonomous weapons become pressing concerns. Governance frameworks must enforce security protocols to prevent malicious use of AI systems.

Global AI Governance Initiatives

Different nations and organizations are approaching AI governance in diverse ways:

  • European Union (EU): The EU’s Artificial Intelligence Act seeks to regulate AI based on risk categories. High-risk applications, such as biometric identification and critical infrastructure management, face stricter requirements, while lower-risk systems have minimal oversight.
  • United States: The U.S. government has taken a more hands-off approach, emphasizing AI innovation while promoting ethical guidelines through the National Institute of Standards and Technology (NIST) AI Risk Management Framework. States such as California have begun implementing stricter AI policies, particularly regarding data privacy.
  • China: China has introduced comprehensive AI laws emphasizing security, data control, and algorithmic regulation. The country focuses on AI governance that aligns with state interests while fostering technological leadership in AI innovation.
  • United Nations (UN) & Industry Collaborations: The UNESCO AI Ethics Framework and initiatives like the Partnership on AI bring together global stakeholders to promote responsible AI development. Large tech firms, including Microsoft and Google, have also created internal AI governance structures to align their AI systems with ethical standards.

Challenges in AI Governance

While governance frameworks are evolving, challenges remain:

  • Regulatory Complexity: AI development is global, but governance laws vary widely, making international collaboration essential yet difficult.
  • Balancing Innovation and Regulation: Striking the right balance between enabling innovation and imposing regulations is crucial to avoid stifling progress.
  • Enforcement: Ensuring companies adhere to AI regulations requires oversight and accountability mechanisms, which can be difficult to implement.

The Future of AI Governance

AI governance will continue to evolve as AI capabilities expand. Ethical AI development, global cooperation, and transparent policies will play a crucial role in shaping a future where AI benefits society responsibly. Initiatives promoting AI auditing, fairness assessments, and bias reduction will become integral to AI governance frameworks.

Governance is not about restricting AI; rather, it’s about steering its trajectory toward ethical, secure, and beneficial use. By integrating ethics, accountability, and oversight into AI development, we can maximize AI’s potential while mitigating risks, ensuring its contributions to humanity remain positive.

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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04 July 2025

🤖〽️Prompt Engineering: Copilot Unabridged (Part 41: How AI Can Play Devil’s Advocate - Challenging Assumptions and Expanding Perspectives)

Prompt Engineering Series
Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI can play devil's advocate"

Introduction

Artificial Intelligence (AI) is often seen as a tool for efficiency, automation, and problem-solving. However, one of its most intriguing capabilities is its ability to play devil’s advocate - challenging assumptions, questioning biases, and presenting alternative viewpoints. By acting as a skeptical counterbalance, AI can help individuals and organizations think critically, refine arguments, and explore diverse perspectives.

1. What Does It Mean to Play Devil’s Advocate?

Playing devil’s advocate means arguing against a prevailing opinion or assumption, even if one does not personally agree with the opposing stance. This approach is valuable in:

  • Debates and discussions, where opposing viewpoints strengthen arguments.
  • Decision-making, ensuring all possibilities are considered.
  • Problem-solving, where unconventional perspectives lead to innovative solutions.

AI, with its ability to analyze vast amounts of data and generate counterarguments, is uniquely positioned to take on this role.

2. How AI Challenges Confirmation Bias

One of AI’s most important functions as a devil’s advocate is breaking the confirmation bias loop - the tendency for people to seek out information that supports their existing beliefs while ignoring contradictory evidence. AI can:

  • Identify logical inconsistencies in arguments.
  • Present alternative viewpoints, even if they challenge popular opinions.
  • Encourage critical thinking by questioning assumptions.

By disrupting confirmation bias, AI helps individuals and organizations make more informed and balanced decisions.

3. AI in Decision-Making and Policy Development

AI-driven devil’s advocacy is particularly useful in policy-making, business strategy, and ethical debates. Some applications include:

  • Corporate decision-making: AI can highlight risks and alternative strategies before executives finalize plans.
  • Legal and ethical discussions: AI can present opposing viewpoints in debates about regulations and governance.
  • Scientific research: AI can challenge hypotheses, ensuring rigorous testing and validation.

By forcing individuals to consider alternative perspectives, AI enhances objectivity and rational decision-making.

4. AI’s Role in Amplifying Minority Voices

AI can also serve as a mediator for underrepresented perspectives, ensuring that minority viewpoints are heard in group discussions. AI-driven devil’s advocate systems:

  • Reduce social influence biases, preventing dominant voices from overshadowing others.
  • Encourage diverse perspectives, fostering more inclusive decision-making.
  • Improve psychological safety, allowing individuals to express dissenting opinions anonymously.
  • This approach ensures that critical discussions remain balanced and representative.

5. The Challenges of AI as a Devil’s Advocate

While AI can challenge assumptions, it also faces limitations:

  • AI models may reinforce biases if trained on skewed data.
  • AI lacks true human intuition, making some counterarguments overly rigid.
  • AI’s responses depend on prompts, requiring careful input to generate meaningful opposition.

To maximize AI’s effectiveness as a devil’s advocate, developers must ensure diverse training data and refine AI’s ability to engage in nuanced discussions.

Conclusion: AI as a Catalyst for Critical Thinking

AI’s ability to challenge assumptions, disrupt biases, and amplify diverse perspectives makes it a powerful tool for critical thinking and informed decision-making. By playing devil’s advocate, AI helps individuals and organizations refine arguments, explore alternative viewpoints, and make more balanced choices.

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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03 July 2025

🤖〽️Prompt Engineering: Copilot Unabridged (Part 40: How Disruptive Is AI as a Technology?)

Prompt Engineering Series
Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how disruptive is AI as technology"

Introduction

Artificial Intelligence (AI) is one of the most transformative and disruptive technologies of the modern era. It is reshaping industries, automating processes, and redefining human interactions with machines. While AI brings efficiency, innovation, and economic growth, it also raises concerns about job displacement, ethical dilemmas, and security risks. Understanding AI’s disruptive nature is crucial for navigating its impact on society.

1. AI’s Disruption Across Industries

AI is revolutionizing multiple sectors, leading to both opportunities and challenges:

  • Healthcare: AI-powered diagnostics improve accuracy, but automation may reduce the need for human radiologists and pathologists.
  • Finance: AI-driven trading algorithms optimize investments, yet they also introduce risks of market instability.
  • Retail: AI personalizes shopping experiences, but automation threatens traditional retail jobs.
  • Manufacturing: AI-powered robotics enhance efficiency, yet they replace human labor in factories.

AI’s ability to streamline operations and reduce costs makes it highly disruptive, forcing industries to adapt or risk obsolescence.

2. AI’s Impact on Employment and Workforce Dynamics

One of AI’s most significant disruptions is its effect on employment. While AI creates new job opportunities in data science, AI development, and cybersecurity, it also eliminates traditional roles in sectors like customer service, transportation, and manufacturing.

  • Automation replaces repetitive tasks, reducing demand for human workers.
  • AI-driven hiring processes change recruitment dynamics, making job searches more competitive.
  • Reskilling becomes essential, as workers must adapt to AI-integrated industries.

Governments and businesses must invest in workforce retraining to mitigate AI-induced unemployment.

3. AI’s Ethical and Security Challenges

AI’s disruptive nature extends beyond economics - it raises ethical concerns and security risks:

  • Bias in AI algorithms: AI models trained on biased data can reinforce discrimination in hiring, healthcare, and law enforcement.
  • Privacy concerns: AI-driven surveillance and data collection raise questions about personal security.
  • Cybersecurity threats: AI-powered hacking tools exploit vulnerabilities, making digital security more complex.

Without strong ethical guidelines and security measures, AI’s disruption could lead to unintended consequences.

4. AI’s Role in Decision-Making and Governance

AI is increasingly used in government policies, legal judgments, and corporate strategies, raising concerns about autonomy and accountability:

  • AI-driven governance: Governments use AI for predictive policing and policy analysis, but transparency is crucial.
  • AI in legal systems: AI assists in legal research, yet its role in judicial decisions remains controversial.
  • AI-powered corporate decision-making: Businesses rely on AI for strategic planning, but human oversight is necessary.

Balancing AI automation with human judgment is essential for ethical governance.

Conclusion: AI’s Disruption Requires Responsible Management

AI is undeniably disruptive, reshaping industries, employment, ethics, and governance. While its advancements bring efficiency and innovation, they also introduce challenges that require proactive solutions. Governments, businesses, and researchers must develop ethical AI policies, invest in workforce adaptation, and ensure AI remains a tool for progress rather than an unchecked force.

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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21 June 2025

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

Prompt Engineering Series
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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20 June 2025

🤖〽️Prompt Engineering: Copilot Unabridged (Part 27: The Negative Impact of AI on the Economy)

Prompt Engineering Series
Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI can impact the economy negatively"

Introduction

Artificial Intelligence (AI) is transforming industries, automating processes, and reshaping the global economy. While AI offers efficiency, innovation, and productivity gains, it also presents significant economic risks that could disrupt labor markets, widen inequality, and create financial instability. Understanding these challenges is crucial for ensuring AI’s development aligns with sustainable economic growth.

1. AI-Driven Job Displacement and Unemployment

One of the most pressing concerns about AI’s impact on the economy is job displacement. AI-powered automation is replacing human labor in various industries, including:

  • Manufacturing: AI-driven robotics reduce the need for factory workers.
  • Retail and customer service: AI chatbots and self-checkout systems replace human employees.
  • Finance: AI automates investment decisions, reducing demand for financial analysts.

While AI creates new jobs in data science, software engineering, and AI ethics, it eliminates traditional roles, leading to mass unemployment and economic instability.

2. Widening Economic Inequality

AI’s integration into businesses benefits corporations that can afford advanced AI systems, but it disadvantages smaller companies and low-income workers. AI-driven automation:

  • Increases profits for large corporations, widening the gap between wealthy and struggling businesses.
  • Reduces wages for workers, as AI replaces human labor.
  • Limits economic mobility, making it harder for displaced workers to find new opportunities.

Without proper regulation, AI could exacerbate income inequality, leading to social unrest and economic polarization.

3. AI’s Role in Financial Market Instability

AI-driven algorithms dominate stock trading, investment strategies, and financial predictions. While AI enhances efficiency, it also introduces risks:

  • AI-powered trading systems can trigger market crashes due to rapid, automated decision-making.
  • AI-driven financial models may misinterpret economic trends, leading to poor investment choices.
  • Over-reliance on AI in banking could create vulnerabilities in financial security.

AI’s influence on financial markets requires strict oversight to prevent economic instability.

4. AI’s Impact on Tax Revenue and Public Services

As AI replaces human workers, governments face declining tax revenue, affecting public services such as:

  • Healthcare: Reduced funding for hospitals and medical programs.
  • Education: Limited resources for schools and universities.
  • Infrastructure: Less investment in transportation and urban development.

Without a restructured tax system, AI-driven automation could weaken government funding, impacting essential services.

5. Ethical and Regulatory Challenges in AI Economics

AI’s rapid expansion outpaces regulatory frameworks, leading to:

  • Unregulated AI-driven monopolies, where a few corporations control AI technologies.
  • Privacy concerns, as AI collects vast amounts of consumer data.
  • Bias in AI decision-making, affecting hiring, lending, and economic policies.

Governments must implement AI regulations to ensure fair competition and ethical economic practices.

Conclusion: AI’s Economic Risks Require Careful Management

While AI offers efficiency and innovation, its negative economic impact - job displacement, inequality, financial instability, tax revenue decline, and regulatory challenges - must be addressed. Governments, businesses, and researchers must develop policies that balance AI’s benefits with economic sustainability, ensuring AI serves society rather than disrupting it.

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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08 April 2024

🧭Business Intelligence: Why Data Projects Fail to Deliver Real-Life Impact (Part III: Failure through the Looking Glass)

Business Intelligence
Business Intelligence Series

There’s a huge volume of material available on project failure – resources that document why individual projects failed, why in general projects fail, why project members, managers and/or executives think projects fail, and there seems to be no other more rewarding activity at the end of a project than to theorize why a project failed, the topic culminating occasionally with the blaming game. Success may generate applause, though it's failure that attracts and stirs the most waves (irony, disapproval, and other similar behavior) and everybody seems to be an expert after the consumed endeavor. 

The mere definition of a project failure – not fulfilling project’s objectives within the set budget and timeframe - is a misnomer because budgets and timelines are estimated based on the information available at the beginning of the project, the amount of uncertainty for many projects being considerable, and data projects are no exceptions from it. The higher the uncertainty the less probable are the two estimates. Even simple projects can reveal uncertainty especially when the broader context of the projects is considered. 

Even if it’s not a common practice, one way to cope with uncertainty is to add a tolerance for the estimates, though even this practice probably will not always accommodate the full extent of the unknown as the tolerances are usually small. The general expectation is to have an accurate and precise landing, which for big or exploratory projects is seldom possible!

Moreover, the assumptions under which the estimates hold are easily invalidated in praxis – resources’ availability, first time right, executive’s support to set priorities, requirements’ quality, technologies’ maturity, etc. If one looks beyond the reasons why projects fail in general, quite often the issues are more organizational than technological, the lack of knowledge and experience being some of the factors. 

Conversely, many projects will not get approved if the estimates don’t look positive, and therefore people are pressured in one way or another to make the numbers fit the expectations. Some projects, given their importance, need to be done even if the numbers don’t look good or can’t be quantified correctly. Other projects represent people’s subsistence on the job, respectively people's self-occupation to create motion, though they can occasionally have also a positive impact for the organizations. These kinds of aspects almost never make it in statistics or surveys. Neither do the big issues people are afraid to talk about. Where to consider that in the light of politics and office’s grapevine the facts get distorted!

Data projects reflect all the symptoms of failure projects have in general, though when words like AI, Statistics or Machine Learning are used, the chances for failure are even higher given that the respective fields require a higher level of expertise, the appropriate use of technologies and adherence to the scientific process for the results to be valid. If projects can benefit from general recipes, respectively established procedures and methods, their range of applicability decreases when the mentioned areas are involved. 

Many data projects have an exploratory nature – seeing what’s possible - and therefore a considerable percentage will not reach production. Moreover, even those that reach that far might arrive to be stopped or discarded sooner or later if they don’t deliver the expected value, and probably many of the models created in the process are biased, irrelevant, or incorrectly apply the theory. Where to add that the mere use of tools and algorithms is not Data Science or Data Analysis. 

The challenge for many data projects is to identify which Project Management (PM) best practices to consider. Following all or no practices at all just increases the risks of failure!

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05 March 2024

🧭Business Intelligence: Data Culture (Part I: Generative AI - No Silver Bullet)

Business Intelligence
Business Intelligence Series

Talking about holy grails in Data Analytics, another topic of major importance for an organization’s "infrastructure" is data culture, that can be defined as the collective beliefs, values, behaviors, and practices of an organization’s employees in harnessing the value of data for decision-making, operations, or insight. Rooted in data literacy, data culture is an extension of an organization’s culture in respect to data that acts as enabler in harnessing the value of data. It’s about thinking critically about data and how data is used to create value. 

The current topic was suggested by PowerBI.tips’s webcast from today [3] and is based on Brent Dykes’ article from Forbes ‘Why AI Isn’t Going to Solve All Your Data Culture Problems’ [1]. Dykes’ starting point for the discussion is Wavestone's annual data executive survey based on which the number of companies that reported they had "created a data-driven organization" rose sharply from 23.9 percent in 2023 to 48.1 percent in 2024 [2]. The report’s authors concluded that the result is driven by the adoption of Generative AI, the capabilities of OpenAI-like tools to generate context-dependent meaningful text, images, and other content in response to prompts. 

I agree with Dykes that AI technologies can’t be a silver bullet for an organization data culture given that AI either replaces people’s behaviors or augments existing ones, being thus a substitute and not a cure [1]. Even for a disruptive technology like Generative AI, it’s impossible to change so much employees’ mindset in a so short period of time. Typically, a data culture matures over years with sustained effort. Therefore, the argument that the increase is due to respondent’s false perception is more than plausible. There’s indeed a big difference between thinking about an organization as being data-driven and being data-driven. 

The three questions-based evaluation considered in the article addresses this difference, thinking vs. being. Changes in data culture don’t occur just because some people or metrics say so, but when people change their mental models based on data, when the interpersonal relations change, when the whole dynamics within the organization changes (positively). If people continue the same behavior and practices, then there are high chances that no change occurred besides the Brownian movement in a confined space of employees, that’s just chaotic motion.  

Indeed, a data culture should encourage the discovery, exploration, collaboration, discussions [1] respectively knowledge sharing and make people more receptive and responsive about environmental or circumstance changes. However, just involving leadership and having things prioritized and funded is not enough, no matter how powerful the drive. These can act as enablers, though more important is to awaken and guide people’s interest, working on people’s motivation and supporting the learning process through mentoring. No amount of brute force can make a mind move and evolve freely unless the mind is driven by an inborn curiosity!

Driving a self-driving car doesn’t make one a better driver. Technology should challenge people and expand their understanding of how data can be used in different contexts rather than give solutions based on a mass of texts available as input. This is how people grow meaningfully and how an organization’s culture expands. Readily available answers make people become dull and dependent on technology, which in the long-term can create more problems. Technology can solve problems when used creatively, when problems and their context are properly understood, and the solutions customized accordingly.

Unfortunately, for many organizations data culture will be just a topic to philosophy about. Data culture implies a change of mindset, perception, mental models, behavior, and practices based on data and not only consulting the data to confirm one’s biases on how the business operates!

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Resources:
[1] Forbes (2024) Why AI Isn’t Going To Solve All Your Data Culture Problems, by Brent Dykes (link)
[2] Wavestone (2024) 2024 Data and AI Leadership Executive Survey (link)
[3] Power BI tips (2024) Ep.299: AI & Data Culture Problems (link)

17 December 2018

🔭Data Science: Bias (Just the Quotes)

"The human mind can hardly remain entirely free from bias, and decisive opinions are often formed before a thorough examination of a subject from all its aspects has been made." (Helena P. Blavatsky, "The Secret Doctrine", 1888)

"The classification of facts, the recognition of their sequence and relative significance is the function of science, and the habit of forming a judgment upon these facts unbiased by personal feeling is characteristic of what may be termed the scientific frame of mind." (Karl Pearson, "The Grammar of Science", 1892)

"It may be impossible for human intelligence to comprehend absolute truth, but it is possible to observe Nature with an unbiased mind and to bear truthful testimony of things seen." (Sir Richard A Gregory, "Discovery, Or, The Spirit and Service of Science", 1916)

"Scientific discovery, or the formulation of scientific theory, starts in with the unvarnished and unembroidered evidence of the senses. It starts with simple observation - simple, unbiased, unprejudiced, naive, or innocent observation - and out of this sensory evidence, embodied in the form of simple propositions or declarations of fact, generalizations will grow up and take shape, almost as if some process of crystallization or condensation were taking place. Out of a disorderly array of facts, an orderly theory, an orderly general statement, will somehow emerge." (Sir Peter B Medawar, "Is the Scientific Paper Fraudulent?", The Saturday Review, 1964)

"Errors may also creep into the information transfer stage when the originator of the data is unconsciously looking for a particular result. Such situations may occur in interviews or questionnaires designed to gather original data. Improper wording of the question, or improper voice inflections. and other constructional errors may elicit nonobjective responses. Obviously, if the data is incorrectly gathered, any graph based on that data will contain the original error - even though the graph be most expertly designed and beautifully presented." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"Numbers have undoubted powers to beguile and benumb, but critics must probe behind numbers to the character of arguments and the biases that motivate them." (Stephen J Gould, "An Urchin in the Storm: Essays About Books and Ideas", 1987)

"But our ways of learning about the world are strongly influenced by the social preconceptions and biased modes of thinking that each scientist must apply to any problem. The stereotype of a fully rational and objective ‘scientific method’, with individual scientists as logical (and interchangeable) robots, is self-serving mythology." (Stephen J Gould, "This View of Life: In the Mind of the Beholder", "Natural History", Vol. 103, No. 2, 1994)

"Under conditions of uncertainty, both rationality and measurement are essential to decision-making. Rational people process information objectively: whatever errors they make in forecasting the future are random errors rather than the result of a stubborn bias toward either optimism or pessimism. They respond to new information on the basis of a clearly defined set of preferences. They know what they want, and they use the information in ways that support their preferences." (Peter L Bernstein, "Against the Gods: The Remarkable Story of Risk", 1996)

"A smaller model with fewer covariates has two advantages: it might give better predictions than a big model and it is more parsimonious (simpler). Generally, as you add more variables to a regression, the bias of the predictions decreases and the variance increases. Too few covariates yields high bias; this called underfitting. Too many covariates yields high variance; this called overfitting. Good predictions result from achieving a good balance between bias and variance. […] fiding a good model involves trading of fit and complexity." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Self-selection bias occurs when people choose to be in the data - for example, when people choose to go to college, marry, or have children. […] Self-selection bias is pervasive in 'observational data', where we collect data by observing what people do. Because these people chose to do what they are doing, their choices may reflect who they are. This self-selection bias could be avoided with a controlled experiment in which people are randomly assigned to groups and told what to do." (Gary Smith, "Standard Deviations", 2014)

"Self-selection bias occurs when we compare people who made different choices without thinking about why they made these choices. […] Our conclusions would be more convincing if choice was removed […]" (Gary Smith, "Standard Deviations", 2014)

"We naturally draw conclusions from what we see […]. We should also think about what we do not see […]. The unseen data may be just as important, or even more important, than the seen data. To avoid survivor bias, start in the past and look forward." (Gary Smith, "Standard Deviations", 2014)

"We live in a world with a surfeit of information at our service. It is our choice whether we seek out data that reinforce our biases or choose to look at the world in a critical, rational manner, and allow reality to bend our preconceptions. In the long run, the truth will work better for us than our cherished fictions." (Razib Khan, "The Abortion Stereotype", The New York Times, 2015)

"A popular misconception holds that the era of Big Data means the end of a need for sampling. In fact, the proliferation of data of varying quality and relevance reinforces the need for sampling as a tool to work efficiently with a variety of data, and minimize bias. Even in a Big Data project, predictive models are typically developed and piloted with samples." (Peter C Bruce & Andrew G Bruce, "Statistics for Data Scientists: 50 Essential Concepts", 2016)

"Bias is error from incorrect assumptions built into the model, such as restricting an interpolating function to be linear instead of a higher-order curve. [...] Errors of bias produce underfit models. They do not fit the training data as tightly as possible, were they allowed the freedom to do so. In popular discourse, I associate the word 'bias' with prejudice, and the correspondence is fairly apt: an apriori assumption that one group is inferior to another will result in less accurate predictions than an unbiased one. Models that perform lousy on both training and testing data are underfit." (Steven S Skiena, "The Data Science Design Manual", 2017)

"Bias occurs normally when the model is underfitted and has failed to learn enough from the training data. It is the difference between the mean of the probability distribution and the actual correct value. Hence, the accuracy of the model is different for different data sets (test and training sets). To reduce the bias error, data scientists repeat the model-building process by resampling the data to obtain better prediction values." (Umesh R Hodeghatta & Umesha Nayak, "Business Analytics Using R: A Practical Approach", 2017)

"High-bias models typically produce simpler models that do not overfit and in those cases the danger is that of underfitting. Models with low-bias are typically more complex and that complexity enables us to represent the training data in a more accurate way. The danger here is that the flexibility provided by higher complexity may end up representing not only a relationship in the data but also the noise. Another way of portraying the bias-variance trade-off is in terms of complexity v simplicity." (Jesús Rogel-Salazar, "Data Science and Analytics with Python", 2017) 

"If either bias or variance is high, the model can be very far off from reality. In general, there is a trade-off between bias and variance. The goal of any machine-learning algorithm is to achieve low bias and low variance such that it gives good prediction performance. In reality, because of so many other hidden parameters in the model, it is hard to calculate the real bias and variance error. Nevertheless, the bias and variance provide a measure to understand the behavior of the machine-learning algorithm so that the model model can be adjusted to provide good prediction performance." (Umesh R Hodeghatta & Umesha Nayak, "Business Analytics Using R: A Practical Approach", 2017)

"The human brain always concocts biases to aid in the construction of a coherent mental life, exclusively suitable for an individual’s personal needs." (Abhijit Naskar, "We Are All Black: A Treatise on Racism", 2017)

"The tension between bias and variance, simplicity and complexity, or underfitting and overfitting is an area in the data science and analytics process that can be closer to a craft than a fixed rule. The main challenge is that not only is each dataset different, but also there are data points that we have not yet seen at the moment of constructing the model. Instead, we are interested in building a strategy that enables us to tell something about data from the sample used in building the model." (Jesús Rogel-Salazar, "Data Science and Analytics with Python", 2017) 

"When we have all the data, it is straightforward to produce statistics that describe what has been measured. But when we want to use the data to draw broader conclusions about what is going on around us, then the quality of the data becomes paramount, and we need to be alert to the kind of systematic biases that can jeopardize the reliability of any claims." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

"We over-fit when we go too far in adapting to local circumstances, in a worthy but misguided effort to be ‘unbiased’ and take into account all the available information. Usually we would applaud the aim of being unbiased, but this refinement means we have less data to work on, and so the reliability goes down. Over-fitting therefore leads to less bias but at a cost of more uncertainty or variation in the estimates, which is why protection against over-fitting is sometimes known as the bias/variance trade-off." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

"Any machine learning model is trained based on certain assumptions. In general, these assumptions are the simplistic approximations of some real-world phenomena. These assumptions simplify the actual relationships between features and their characteristics and make a model easier to train. More assumptions means more bias. So, while training a model, more simplistic assumptions = high bias, and realistic assumptions that are more representative of actual phenomena = low bias." (Imran Ahmad, "40 Algorithms Every Programmer Should Know", 2020)

"If the data that go into the analysis are flawed, the specific technical details of the analysis don’t matter. One can obtain stupid results from bad data without any statistical trickery. And this is often how bullshit arguments are created, deliberately or otherwise. To catch this sort of bullshit, you don’t have to unpack the black box. All you have to do is think carefully about the data that went into the black box and the results that came out. Are the data unbiased, reasonable, and relevant to the problem at hand? Do the results pass basic plausibility checks? Do they support whatever conclusions are drawn?" (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"If you study one group and assume that your results apply to other groups, this is extrapolation. If you think you are studying one group, but do not manage to obtain a representative sample of that group, this is a different problem. It is a problem so important in statistics that it has a special name: selection bias. Selection bias arises when the individuals that you sample for your study differ systematically from the population of individuals eligible for your study." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"A well-known theorem called the 'no free lunch' theorem proves exactly what we anecdotally witness when designing and building learning systems. The theorem states that any bias-free learning system will perform no better than chance when applied to arbitrary problems. This is a fancy way of stating that designers of systems must give the system a bias deliberately, so it learns what’s intended. As the theorem states, a truly bias- free system is useless." (Erik J Larson, "The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do", 2021)

"Machine learning bias is typically understood as a source of learning error, a technical problem. […] Machine learning bias can introduce error simply because the system doesn’t 'look' for certain solutions in the first place. But bias is actually necessary in machine learning - it’s part of learning itself." (Erik J Larson, "The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do", 2021)

"To accomplish their goals, what are now called machine learning systems must each learn something specific. Researchers call this giving the machine a 'bias'. […] A bias in machine learning means that the system is designed and tuned to learn something. But this is, of course, just the problem of producing narrow problem-solving applications." (Erik J Larson, "The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do", 2021)

"Any time you run regression analysis on arbitrary real-world observational data, there’s a significant risk that there’s hidden confounding in your dataset and so causal conclusions from such analysis are likely to be (causally) biased." (Aleksander Molak, "Causal Inference and Discovery in Python", 2023)

"Science is the search for truth, that is the effort to understand the world: it involves the rejection of bias, of dogma, of revelation, but not the rejection of morality." (Linus Pauling)

"Facts and values are entangled in science. It's not because scientists are biased, not because they are partial or influenced by other kinds of interests, but because of a commitment to reason, consistency, coherence, plausibility and replicability. These are value commitments." (Alva Noë)

"A scientist has to be neutral in his search for the truth, but he cannot be neutral as to the use of that truth when found. If you know more than other people, you have more responsibility, rather than less." (Charles P Snow)

More quotes on "Bias" at the-web-of-knowledge.blogspot.com

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