The Erosion of Human Decision-Making

Masterclass on Emerging Literacies

To discuss the impact of Big Data and AI on human liberty and decision-making.

In sociology and technology, B × C × D = AHH! is a famous conceptual formula coined by historian Yuval Noah Harari at the World Economic Forum. It warns against the rise of digital dictatorships and the ability to control human behavior.

The variables stand for:

B = Biological knowledge
C = Computing power
D = Data
AHH! = The ability to hack humans

Key Takeaways

• The Human Hacking Formula (b x c x d = ahh): The convergence of Biological knowledge, Computing power, and Data creates algorithms that can understand and manipulate humans better than we understand ourselves, eroding free will.

• AI's Ethical Advantage: AI can make more consistent ethical decisions than humans. It can be programmed to follow rules (like Kant) with the precision of an expert (like Schumacher), overcoming human biases and emotional failures.

• The Pendulum Swings to Dictatorship: AI makes centralized data processing vastly more efficient. This could reverse the 20th-century advantage of democracies, enabling 21st-century digital dictatorships with total surveillance and control.

• The Teacher's New Role: Teachers must shift from information processors to thought-provokers, preparing students for a future where philosophy and critical thinking are essential skills for navigating AI's ethical and societal challenges.

Topics

The Human Hacking Formula

• The formula b x c x d = ahh (Biological knowledge + Computing power + Data = Algorithms) creates systems that can understand and manipulate humans better than we understand ourselves.

• This erodes free will, as feelings are revealed to be hackable biochemical algorithms for survival, not a source of ultimate authority.

• Authority is shifting from human feelings to big data algorithms, making governments and traditional systems increasingly irrelevant.

The Erosion of Human Decision-Making

• Over-reliance on AI (e.g., Google Maps) atrophies human skills like navigation, leading to incidents like the Japanese tourists driving into the ocean.

• AI could soon make major life decisions (careers, relationships) more effectively than humans, rendering traditional concepts like free markets and democratic elections obsolete.

• This future requires a new model for human life, as it ceases to be a "drama of decision-making."

AI's Ethical Advantage: The Schumacher-Kant Team

• AI can make more consistent ethical decisions than humans, who often fail to act on their principles under pressure.

  • Example: The Princeton Theological Seminary experiment showed students, despite discussing the "Good Samaritan" parable, ignored a person in distress when told they were late for a lecture.

• AI can be programmed to follow ethical guidelines without emotion or instinct, creating a "Schumacher-Kant team" (driving skill + philosophical ethics).

• This transforms ethical theory into a practical engineering problem, where philosophers could be sued for the real-world consequences of their code.

The Threat of Digital Dictatorship

• The real danger of AI is not rebellion, but blind obedience to its human masters.

• Military Application: Killer robots could prevent atrocities driven by human emotion (e.g., My Lai massacre) but would also execute ruthless orders without hesitation (e.g., Srebrenica).

• Surveillance Risk: Powerful surveillance algorithms enable total control.

  • Example: The Israeli security forces' use of AI in the West Bank, where a translation error led to a Palestinian's arrest.

• Authoritarian Advantage: AI makes centralized data processing vastly more efficient.

  • Example: A government that mandates sharing all medical data could gain a decisive advantage in research over societies that protect privacy.

• This could reverse the 20th-century advantage of democracies, leading to a "digital dictatorship" where resistance is impossible.

The Teacher's New Role

• Teachers must shift from information processors to thought-provokers, preparing students for a future where philosophy and critical thinking are essential.

• Key Takeaways for Educators:

  1. Philosophy is a science: The humanities/science distinction is obsolete; philosophy is now a practical skill for engineering ethical AI.

  2. Decisions for the greater good: AI's ability to make consistent ethical choices highlights the need for humans to define and prioritize collective well-being.

  3. Harari is a futurist: His 12-year-old predictions are becoming reality, demonstrating the urgency for educators to prepare students for a future that is already arriving.

Next Steps

• Sandeep Dutt: Continue the reading of 21 Lessons for the 21st Century in the next masterclass (Aug 29, 5:30 PM).

• All Participants:

  • Reflect on the session's themes, especially the BCD=AHH formula and the ethical implications of AI.

  • Consider attending Brinda Ma'am's new book club for Educated by Tara Westover (starting Aug 29, 3:30 PM). 

    
    

    
    

    FATHOM AI assisted post.

To Copy or Not to Copy Part 1

Learning Forward Saturday

The meeting centred on reflections and discussions on adolescent behaviour, particularly the chapter “Right vs. Wrong” and the upcoming chapter “To Copy vs. Not to Copy.” Sneha Pundir shared reflections on alienation, emphasising that punitive measures such as detention can exacerbate disconnection. Anurag Tripathi’s reflection underscored the importance of empathy and trust in schools to prevent high-risk behaviours. The discussion also addressed the impact of peer pressure and the need for healthy coping mechanisms. Roma’s story in the chapter illustrated the importance of honesty and effective learning techniques, such as associating information with personal milestones.

Using AI to identify out-of-school girls

Read and Lead Thursday

To discuss the book’s chapter on using AI to identify out-of-school girls, from Every Last Girl by Safeena Husain.

Key Takeaways

• Inefficient Saturation Model: The initial door-to-door survey was unsustainable, taking 6 years per district and wasting resources on villages with no out-of-school girls.

• AI-Powered Precision: A machine learning model trained on 10 years of data from ~1M households now predicts high-need villages, increasing the average number of girls found per village from 18 to 42.

• Concentration of Need: A key insight revealed that ~40% of out-of-school girls live in just 5% of villages, making targeted intervention feasible.

• “Combination Therapy”: The model’s success was amplified by aligning with government policies (RTE, Beti Bachao), which addressed systemic barriers such as school access and son preference.

Topics

The Problem: Inefficient Saturation Strategy

• The initial strategy was a door-to-door survey in 13 districts, aiming for 100% saturation.

• Inefficiencies:

  • Slow: Estimated 6 years per district (e.g., 100k homes).

  • Wasted Resources: ~20% of villages had no out-of-school girls.

  • Overwhelmed Teams: Some villages had 100+ girls for one volunteer.

• The Goal: Find a faster, more cost-effective way to reach high-need villages.

The Solution: AI-Powered Prediction Model

• The Idea: ID Insight proposed using a machine learning model to predict where out-of-school girls live.

• The Data Goldmine: 10 years of survey data from ~1M households provided the necessary training data.

• Predictor Variables: The model was trained on 313 indicators from the Indian Census and UDISE, including:

  • Parent literacy, caste, income, and household size.

  • School proximity, infrastructure, and enrollment rates.

• Human-Machine Synergy: The model guides volunteers to high-need villages, but human intelligence is still required for on-the-ground work and for navigating real-world challenges (e.g., mountainous terrain, difficult officials).

The Impact: Precision Targeting & “Combination Therapy”

• Initial Test: The model found 50–100% more girls than the human-led survey in the same villages.

• Improved Efficiency: The average number of girls found per village increased from 18 to 42.

• Strategic Focus: The model enables targeting the 5% of villages that contain ~40% of all out-of-school girls, making the problem manageable.

• “Combination Therapy”: The model’s success was accelerated by aligning with government policies that addressed systemic issues:

  • RTE (Right to Education): Mandated schools within 1km, removing distance as a barrier.

  • Rajasthan Govt. Scheme: Offered financial incentives for girls’ enrollment.

  • Beti Bachao, Beti Padhao: Tackled son preference and elevated the value of girls’ education.

Discussion: Human Cost & Ethical AI

• Human Cost: The group discussed the emotional impact of reading about girls named “Falthu” (useless), reflecting deep-seated societal devaluation.

• Ethical AI: The model was praised as a positive example of AI use, in which technology amplifies human impact rather than replacing it.

Next Steps

• Brinda: Resolve the Zoom background issue with the IT team.

• All: Meet next Thursday to continue reading the book.

FATHOM AI-generated notes.

Moral Development And Reasoning

Brewing Knowledge Fridays

November 14, 2025  

To discuss moral development, socialisation, and effective teaching strategies.

Key Takeaways

• Kohlberg's theory is culturally limited. It fails to capture non-Western moral principles (e.g., ahimsa) and may measure verbal justification rather than actual moral development, explaining why few individuals reach its "post-conventional" stage.
• Empathy is innate, but guilt is a learned conflict. Research shows young children are born with empathy, but causing harm creates a conflict between helping and self-distress (the root of guilt), which is shaped by adult discipline.
• "Other-oriented reasoning" is the most effective teaching tool. It fosters empathy, works even when adults are absent, and promotes reconciliation. In contrast, "power assertion" (through threats and punishment) is counterproductive, fostering anger and dependency.
• Moral development is shaped by social context. Children in "toxic social environments" may learn that power, not fairness, dominates. However, strong community ties can provide a buffer, fostering moral strength even in poverty.

Topics

• Kohlberg's Theory & Cultural Limitations
• Kohlberg's theory, based on the "Heinz dilemma," proposes a universal sequence of moral reasoning stages:
• Pre-conventional: Self-interest, fear of punishment.
• Conventional: Upholding laws, social order.
• Post-conventional: Abstract ethical principles.

Critiques:

Cultural Bias: Fails to capture non-Western principles.

Jyotsna Vasudev & Raymond Hummel found that Indians use principles like ahimsa (sacredness of life) and prioritise social issues over individual lives.

Methodological Flaw: May measure verbal justification, not actual moral development.

This explains why few people worldwide reach the "post-conventional" stage.

Innate Elements of Morality

Evolutionary arguments suggest humans have both aggressive (e.g., grabbing toys) and cooperative instincts.

Empathy: Present from birth; babies match emotional expressions and cry in response to others' distress.

Marion Radke-Yarrow's Study (1-year-olds):

Children increasingly responded to distress with pro-social acts (hugging, helping) and empathic concern.

When children caused the distress, they still helped but also showed more enjoyment, aggression, and self-distress (crying).

Conclusion: Early helping stems from empathy, not remorse. The conflict between empathy and causing harm is the root of guilt.

Socialization & Internalization

Internalisation: The process by which external rules become internal values.

Effective Discipline (Grusec & Goodnow):

Requires accurate perception and acceptance of the adult's viewpoint.

Must be flexible (contingent on situation/temperament) but not inconsistent.

"Other-oriented reasoning" is most effective. It builds empathy and encourages repair.

Ineffective Discipline ("Power Assertion"):

Threats, force, or withdrawing affection.

Negative Outcomes: Insecurity, anger, aggression, and dependency on the adult's presence for moral behaviour.

Social Context & Moral Development

William Arsenio's Research ("Toxic Social Environments"):

Children in disadvantaged Western contexts may learn that power, not fairness, dominates, leading to aggression.

Robert Coles's Research (Poverty):

Found moral strength (e.g., not wronging others, caring for the helpless) in children from Brazilian favelas.

Hypothesis: Strong family and community ties can provide a buffer against the negative effects of social injustice.

Next Steps

All Participants:

• Reflect on personal experiences with "other-oriented reasoning" in adult relationships.
• Consider the factors that prevent students from reporting a teacher's scoring error.

Neelashi M:

Continue reading the book, "What did you ask at school today?" in the next session.

AI-generated notes