This time of year, stories abound from various faiths, religious traditions, and secular customs. From the glow of Hanukkah candles to the communal meals of Kwanzaa, from the midnight Mass to the all-consuming fervor of Black Friday sales, tales of reflection and meaning flood the season. Yet one story endures as a favorite across generations: Charles Dickens’ *A Christmas Carol*. This timeless classic continues to inspire us, replayed and reimagined in countless forms each holiday season. Its appeal lies in its universal lessons, taught through the spectral visitations of the Ghosts of Christmas Past, Present, and Future, who guide Ebenezer Scrooge toward redemption.
But in today’s digital world, we face a different kind of haunting—one not of supernatural apparitions, but algorithmic spirits. As artificial intelligence (AI) grows increasingly central to our lives, it brings with it the ghosts of its own biases. These algorithmic spirits—the Ghosts of Past, Present, and Future Bias—offer reflective lessons, should we dare to confront them.
The Ghost of LLM Past: Bias Baked into the Training Data
Like the Ghost of Christmas Past, the bias embedded into Large Language Models (LLMs) reveals a history—a tapestry of human prejudice, ignorance, and oversight woven into the very fabric of the training data. These models, fed on vast swathes of human text from books, social media, and news articles, inherit the biases of their creators and curators. Every choice about what to include and exclude shapes the model’s worldview.
Consider the language used in historical texts. Colonialism, sexism, and racial prejudice often permeate these sources, reflecting the dominant power structures of their time. Even well-intentioned datasets can unintentionally amplify harmful stereotypes if they fail to account for the unequal representation of marginalized voices. Thus, when an LLM generates a response that perpetuates or reflects these biases, it is not a failure of the machine, but a mirror held up to humanity’s flawed past.
The Ghost of Algorithmic Past whispers a critical question: How do we ensure that the sins of yesterday do not become the foundation of tomorrow? This spectral presence invites us to examine the origins of our data and the assumptions that guided its collection. Yet the journey does not start with the machine; it starts within us. We must also look within our own brains to identify the hidden biases that shape our thinking before we can meaningfully examine the LLM. Without this introspection, we risk repeating historical inequities under the guise of progress.
The Ghost of Algorithmic Present: Bias in the Algorithms
If the Ghost of Algorithmic Past shows us where we came from, the Ghost of Algorithmic Present reveals how bias manifests in real time. Modern algorithms—used to recommend movies, approve loans, and even sentence criminals—are not neutral. Their core functions, to identify patterns and fill in blanks, are shaped by the priorities and blind spots of their designers. For instance, the patterns they identify might reflect societal inequalities, while the blanks they fill often perpetuate existing biases. These decisions are not just abstract calculations; they have tangible impacts on real lives.
Take, for example, facial recognition technology. Numerous studies have shown that these systems are significantly less accurate for individuals with darker skin tones. The bias is not just a statistical anomaly; it has real-world consequences, from false arrests to missed opportunities. Similarly, job recruitment algorithms trained on historical hiring data often reinforce gender and racial disparities, favoring candidates who fit the profile of past hires.
The Ghost of Algorithmic Present reveals the immediate harm caused by these biases, but it also poses a challenge: what are we doing about it *now*? In Dickens’ tale, Scrooge’s present-day behavior—his disregard for the poor and his obsession with profit—leads to suffering for those around him. So, too, do biased algorithms harm individuals and communities. The ghost’s lesson is clear: without accountability and intervention, the present will merely repeat the injustices of the past.
The Ghost of Generative AI Agents Future: Bias in the Outputs
Finally, the Ghost of Agentic Future emerges, a harbinger of what lies ahead if we fail to act. This spirit’s vision is bleak: algorithms that not only replicate historical biases but amplify them, creating a feedback loop that entrenches inequality. Imagine a world where AI systems predict criminal behavior based on biased data, where generative models flood the internet with content that reinforces stereotypes, and where decisions about healthcare, education, and employment are already guided by algorithms that privilege the few at the expense of the many.
One haunting example is predictive policing. In cities where algorithms are used to forecast crime, areas with historically high arrest rates—often marginalized neighborhoods—receive increased police scrutiny. This creates a vicious cycle: more policing leads to more arrests, which in turn reinforces the algorithm’s belief that these areas are high-crime zones. Blindly replacing humans with AI agents in such systems runs the risk of blindly perpetuating internalized LLM bias into future processes, procedures, policies and governance. The Ghost of Agentic Future warns that unchecked AI, even in content creation, messaging imagery and individual publications, could entrench systemic inequities, making them nearly impossible to dismantle.
Yet this ghost also offers a glimmer of hope. The future is not set in stone. Just as Scrooge awakens from his visions determined to change, so can we shape the trajectory of AI. This begins with introspection—recognizing and addressing the hidden biases within ourselves that influence how we build and interact with technology. Only then can we take proactive measures: diversifying the voices involved in AI development, critically evaluating our tools, establishing ethical guidelines, and investing in technologies that prioritize fairness and inclusivity. By looking inward while designing outward, we ensure that both the creators and the systems they build align with values of equity and accountability.
Lessons Learned from the Generative AI Spirits
The Dickensian journey through the Ghosts of Past, Present, and Future of Bias is not merely a tale of woe. It is an opportunity for redemption. These tools are now an integral part of human social evolution, shaping how we perceive the world, interact with one another, and make decisions. By confronting these algorithmic spirits, we must take meaningful steps toward mitigating bias both within ourselves and within the systems we create. Only then can we build AI that reflects our highest ideals rather than our deepest flaws.
To address the Ghost of Algorithmic Past, we must scrutinize and diversify our training data, ensuring that it represents a broad spectrum of experiences and perspectives. To combat the Ghost of Algorithmic Present, we need transparency and accountability in algorithm design, with rigorous testing to identify and mitigate bias before these systems are deployed. And to heed the warning of the Ghost of Algorithmic Future, we must adopt a forward-thinking approach, anticipating potential harms and creating safeguards to prevent them.
Like Ebenezer Scrooge, we stand at a crossroads. The choices we make today about how we design, train, and deploy algorithms will shape the world for generations to come. As individuals, we can take practical steps to address bias in our technological interactions. Start by questioning the systems you interact with daily: What assumptions are baked into your favorite apps or tools? Speak up about inequities you notice, whether it’s flagging bias in algorithmic outputs or advocating for fairness in your workplace’s use of AI tools. Educate yourself and others about the biases inherent in technology, and support organizations working to build more inclusive systems. By taking action today, we can create a future where technology continues to serve humans. Not the other way around.
Percipio Company is led by Matthew Cahill. His deep expertise in cognitive, social, and workplace biases is rooted in the belief that if you have a brain, you have bias®. He works with executives to reduce mental mistakes, strengthen workplace relationships & disrupt existing bias within current HR processes, meeting protocols and corporate policies. Matthew has demonstrated success with large clients like LinkedIn, Salesforce and dozens of small to mid-size companies looking to create more inclusive workplaces, work smarter, generate more revenue and move from bias to belonging®.
