About Me
Professional Background
  • Hannah Gehring, CPA, Data Analytics Product Manager
  • Former auditor with experience analyzing financial patterns
  • Master's in Accounting from University of Central Florida
Personal Interests
  • Passion for numbers, data, and storytelling through patterns
  • Curious about the intersection of technology and humanity
  • Singer-songwriter, stand-up comedian exploring creativity through multiple forms of self-expression
Journey Through Dataism
Dataism Defined
Exploring the philosophy and its real-world implications
Historical Context
A brief history about data and its use cases
Societal Effects
Examining data's influence on individuals and society
Finding Balance
Ethical considerations and the potentials ahead
The Philosophy of Dataism
A mindset or worldview where data flow becomes the supreme “value" and algorithms gain authority over human decision-making.
Authority Shift
From human intuition to algorithmic decision-making in everything from relationships, healthcare and personal taste.
  • Dating apps
  • Health wearables: Oura, Apple Watch
  • Netflix, TikTok, IG
Data as Ultimate Value
If the universe is understood as a giant system of data flows, then those that produce/process more data are considered more valuable. They “serve” the system better.
  • The gamification of life and its effect of continuously contributing to the data flow, quantifying value.
Humans as Algorithms
Redefinition of humanity as biochemical data processors rather than autonomous beings.
  • Human feelings or stories are less valuable than their data which is the source of “truth” fed into systems.
The Evolution of Data Use
1940s–1960s: Early Computation
  • Data primarily used for the census and government/military planning.
1970s–1980s: Database Era
  • Businesses begin leveraging data for finance, supply chain management, and operations.
1990s: Internet & E-Commerce
  • Widespread web adoption creates massive digital footprints.
  • Amazon and Google pioneer search, personalization, and online advertising using data.
  • Not yet tracking online behavior across the web.
2000s: Social Media & Surveillance Capitalism
  • Facebook, Twitter, and YouTube monetize personal interactions and user data.
  • Primarily used to predict behavior for targeted ads and dynamic pricing.
2010s: The Attention Economy
  • Ads were no longer contextual (based on what book you read) but behavioral (based on who you are, what you did, where you went).
  • Platforms optimize algorithms for engagement and time-on platform.
  • Dark pattern design
2020s: AI & Data-Driven Decision-Making
  • AI breakthroughs, including generative AI and machine learning, rely on colossal datasets.
  • Recommendation systems become primary user interface for most popular applications.
From Skinner Box to Smartphone
Modern app design draws heavily from behavioral psychology, specifically B.F. Skinner's operant conditioning experiments, to create habit-forming products.
Skinner Box Elements
  • Pigeon Action: Lever peck
  • Reward: Pellet (sometimes delivered, sometimes not)
  • Reinforcement: Variable schedule keeps pecking longer
  • Outcome: Pigeons conditioned to self-stimulate
Modern App Design
  • User Action: Participate on an application platform
  • Reward: Likes, comments, or views arrive (mood improves)
  • Reinforcement: Periodic notifications keep users checking compulsively
  • Outcome: Users conditioned to self-check apps when bored, anxious, or lonely
This parallel highlights how digital platforms leverage fundamental psychological principles to maximize engagement and data extraction, mirroring the classic experiments in behavioral conditioning.
Cambridge Analytica: Data Weaponized
The 2018 Cambridge Analytica scandal revealed the dark potential of personal data and algorithms, exposing how information collected from social media could be weaponized to influence political outcomes and undermine democratic processes.

A Massive Data Breach
Approximately 87 million Facebook profiles were exposed, with data harvested without explicit user consent. This data was then sold and utilized by Cambridge Analytica for political campaigns.
Weaponized Data
The incident exposed how personal information from social media could be leveraged not just for selling products, but for sophisticated psychological manipulation in the political arena.
Erosion of Trust
It highlighted a severe lack of transparency in algorithmic targeting and data handling, sparking a global debate on privacy, democracy, and the ethical responsibilities of tech platforms.
Regulatory Fallout
The scandal amplified the need for regulation and enforcement for GDPR in Europe, state-level privacy laws like CCPA in the US, and substantial fines for non-compliance, though a comprehensive federal privacy law in the US remains elusive.
Cambridge Analytica served as a stark wake-up call, demonstrating the urgent need for greater accountability and ethical frameworks in our data-driven world. It also showed an example of our choices being manipulated that interfered with the democratic process.
Data Bias
Algorithms reflect the data they're trained on. If these inputs contain historical or systemic prejudices, the outputs can amplify existing inequalities, leading to unfair or discriminatory outcomes.
Biased Diagnostics
A machine-learning tool for diagnosing women's health issues showed lower accuracy for Black, Asian, and Hispanic women, risking unequal treatment. (UF Health, 2023)
Hiring Discrimination
AI résumé screening models consistently rated Black men lower than women and white applicants, even for otherwise identical candidates. (VoxDev, 2023)
Healthcare Algorithm Flaws
Risk-scoring systems underestimated minority patients' needs by using spending data as a proxy for health, prompting calls for bias-mitigation. (Health Affairs, 2023)
Public Service Bias
An AI model used by English councils described women’s health problems as less severe than men’s, affecting service decisions. (BMC Med Inform Deci Mak, 2025)
Societal & Individual Ramifications
Distorted Public Discourse
Algorithms reward emotionally charged content (including misinformation). Research shows outrage and moral-emotional language increase virality on social media (Brady et al., PNAS, 2017).
Performative Social Interaction
Likes and shares condition behavior by offering dopamine rewards.
Normalization of Surveillance
Everyday acceptance of tracking is part of how platforms extract and monetize attention
Mental Health Strain
Heavy social media use correlates with higher risk of anxiety, depression, and suicidal ideation in adolescents (Keles et al., BMC Public Health, 2020).
Doomscrolling is linked to greater anxiety and reduced life satisfaction (Oeldorf-Hirsch et al., Technology, Mind, and Behavior, 2022).
Reduced Collective Focus
Digital multitasking and short-form media are associated with greater inattentive behaviors (Wang et al., Frontiers in Psychology, 2025).
Cultural Shift in Values
Attention metrics replace traditional value signals: popularity > expertise as described in Tim Wu’s The Attention Merchants.
Living in the Network : Obligation, Not Choice
1
Mandatory Engagement
Participation in modern society, including work and social connection, now implicitly demands continuous data generation. Opting out leads to invisibility and lost opportunities.
2
Value Redefined
Algorithms and platform design shift how we decide what to do. Behavior is no longer guided by mindfulness or intrinsic meaning, but by the pursuit of variable rewards. Likes, follows, streaks. What matters is not the depth of the action, but the data it produces and the feedback it generates.
3
The Price of Silence
Within dataism, the system rewards only the visible. A lack of data contribution can result in exclusion from influence and perceived relevance. Career visibility, social exclusions.
Why We Still Can Love Data
Beyond the risks, data offers transformative potential to significantly improve various aspects of our lives, driving progress and convenience.
Medical Breakthroughs
Predictive analytics in diagnostics and personalized treatments are saving lives, like AI detecting cancer earlier than humans.
Enhanced Safety & Efficiency
Data helps predict natural disasters, optimize traffic flow, and manage essential public resources more efficiently.
Accelerating Knowledge
Algorithms uncover complex patterns in climate data, genomics, and astronomy, driving scientific advancements impossible for humans alone.
Daily Convenience
From navigation apps and fraud detection to smart assistants, data makes our daily tasks safer and more convenient.
These applications highlight data's immense power to drive progress and create a better future when harnessed responsibly and ethically.
Dataism: A Double-Edged Belief
At its core, Dataism proposes that the universe is a flow of data, and human value is determined by our contribution to this flow to help optimize the system and gain it’s benefits. For now, there is a flaw in this logic…
Data is Not Neutral
Data inherently reflects our history of capitalism, racism, and inequality. It is a mirror of an imperfect past, not a pristine, objective truth.
Algorithms Amplify Bias & Capitalist Incentives
When algorithms are trained on biased data or optimized for profit, they don’t correct flaws, they reproduce and scale existing injustices.
Breakthroughs vs. Distortions
While data fuels incredible advancements, uncritical use distorts truth, builds echo chambers, and deepens societal divides.
We already live inside Dataism. The challenge is not whether to generate data, but whether we let data define our meaning or use it to build a better future.
Building a Humanity-Centric Data Future
To ensure data serves humanity, we must champion these principles:
1
Ethical Design
Integrate transparency, fairness and social well-being directly into algorithms and data systems, prioritizing human dignity over efficiency and profit.
2
Data Literacy
Empower every individual to critically analyze, question, and challenge data-driven systems, fostering active engagement rather than passive consumption.
3
Philosophical Vigilance
Maintain a clear understanding that data is a powerful tool, not an infallible truth, always asking what is gained and what is lost when quantitative metrics overshadow human stories.
4
Shared Responsibility
Cultivate a collective commitment to balance technological innovation with accountability, ensuring that data genuinely serves the well-being of all humanity, not just a select few.
Religion once said: "Sacrifice your will to God."
Humanism said: "Follow your own will."
Dataism says: "Sacrifice both, what matters is keeping the data flowing."
When data decides what’s best, is life still truly ours to live?