What Is The Alignment Problem? Alignment Problem In A Nutshell

The alignment problem was popularised by author Brian Christian in his 2020 book The Alignment Problem: Machine Learning and Human Values. In the book, Christian outlines the challenges of ensuring AI models capture “our norms and values, understand what we mean or intend, and, above all, do what we want.” The alignment problem describes the problems associated with building powerful artificial intelligence systems that are aligned with their operators.

Understanding the alignment problem

Artificial intelligence has come a long way in recent years, with humankind now creating machines that can perform remarkable feats.

But after six decades of intensive research and development, aligning AI systems with human goals and values remains an elusive task.

With every major field of artificial intelligence trying to replicate human intelligence, problems invariably arise when developers expect AI to act with the rationality and logic of a person.

Growing interest in machine and deep learning has meant the algorithms underpinning everything from baseball games to oil supply chains are being digitized.

This process is helped by high-speed internet, cloud computing, the internet of things (IoT), mobile devices, and a plethora of emerging technologies that collect data on anything and everything.

While machine learning algorithms scale well with the availability of data and computing resources, they are nonetheless complex mathematical functions comparing observations to programmed outcomes.

In other words, artificial intelligence is only as robust as the data used to train it.

When training data is poor quality or simply insufficient, algorithmic output suffers. This scenario represents the essence of the alignment problem.

Real-world examples of the alignment problem

In his book, Christian explains several cases where machine learning algorithms have caused embarrassing and sometimes damaging failures. 

They include:

Google Photos

An algorithm used by the search engine giant in facial recognition software tagged people with dark skin as gorillas.

Had Google trained the algorithm with more examples of people with dark skin, the failure could have been avoided.

Amazon Recruitment

Amazon’s recruitment tool once used artificial intelligence to give job candidates a score between one and five stars.

In theory, this would allow the company to identify promising candidates amongst hundreds of resumes.

However, the model was trained to vet applicants by observing patterns in resumes submitted over a decade-long period.

Since most applications came from men, the algorithm automatically disqualified female applicants as a result.

Real-world examples of the alignment problem were also mentioned by author Cathy O’Neil in her book Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.

In the book, O’Neil explained how blind faith in algorithms caused pervasive damage to many aspects of consumer life. Some examples include:

  • Credit scoring systems that wrongfully penalize people.
  • Recidivism algorithms give defendants of a certain race or ethnicity a heavier prison sentence.
  • Teacher-scoring algorithms reward teachers who game the system and terminate honest, high-performing teachers.
  • Trade algorithms that make billions of dollars profit at the expense of low-income classes and so-called “mom and pop” investors.

Key takeaways:

  • The alignment problem describes the problems associated with building powerful artificial intelligence systems that are aligned with their operators. The concept was popularised by Brian Christian in his book The Alignment Problem: Machine Learning and Human Values.
  • At the core of the alignment problem is poor quality or insufficient algorithm training data. With data now being logged in almost every aspect of daily life, there is a higher likelihood of algorithms making poor decisions because of an overreliance on their mathematical functions.
  • The alignment problem resulted in Google facial recognition models classifying people with darker skin as gorillas, while a similar mishap at Amazon caused its recruitment algorithm to ignore female applicants. Blind faith in algorithms has also resulted in arguably more sinister and pervasive consequences for the average consumer.

Connected Concepts


DevOps refers to a series of practices performed to perform automated software development processes. It is a conjugation of the term “development” and “operations” to emphasize how functions integrate across IT teams. DevOps strategies promote seamless building, testing, and deployment of products. It aims to bridge a gap between development and operations teams to streamline the development altogether.


DevSecOps is a set of disciplines combining development, security, and operations. It is a philosophy that helps software development businesses deliver innovative products quickly without sacrificing security. This allows potential security issues to be identified during the development process – and not after the product has been released in line with the emergence of continuous software development practices.

Continuous Intelligence

The business intelligence models have transitioned to continuous intelligence, where dynamic technology infrastructure is coupled with continuous deployment and delivery to provide continuous intelligence. In short, the software offered in the cloud will integrate with the company’s data, leveraging on AI/ML to provide answers in real-time to current issues the organization might be experiencing.

Continuous Integration

Continuous Integration/Continuous Deployment (CI/CD) introduces automation into the stages of app development to frequently deliver to customers. CI/CD introduces continuous automation and monitoring throughout the app lifecycle, from testing to delivery and then deployment.


Machine Learning Ops (MLOps) describes a suite of best practices that successfully help a business run artificial intelligence. It consists of the skills, workflows, and processes to create, run, and maintain machine learning models to help various operational processes within organizations.


RevOps – short for Revenue Operations – is a framework that aims to maximize the revenue potential of an organization. RevOps seeks to align these departments by giving them access to the same data and tools. With shared information, each then understands their role in the sales funnel and can work collaboratively to increase revenue.


AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.


Ad Ops – also known as Digital Ad Operations – refers to systems and processes that support digital advertisements’ delivery and management. The concept describes any process that helps a marketing team manage, run, or optimize ad campaigns, making them an integrating part of the business operations.

OpenAI Organizational Structure

OpenAI is an artificial intelligence research laboratory that transitioned into a for-profit organization in 2019. The corporate structure is organized around two entities: OpenAI, Inc., which is a single-member Delaware LLC controlled by OpenAI non-profit, And OpenAI LP, which is a capped, for-profit organization. The OpenAI LP is governed by the board of OpenAI, Inc (the foundation), which acts as a General Partner. At the same time, Limited Partners comprise employees of the LP, some of the board members, and other investors like Reid Hoffman’s charitable foundation, Khosla Ventures, and Microsoft, the leading investor in the LP.

OpenAI Business Model

OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemium model. Microsoft also commercializes opener products through its commercial partnership.


OpenAI and Microsoft partnered up from a commercial standpoint. The history of the partnership started in 2016 and consolidated in 2019, with Microsoft investing a billion dollars into the partnership. It’s now taking a leap forward, with Microsoft in talks to put $10 billion into this partnership. Microsoft, through OpenAI, is developing its Azure AI Supercomputer while enhancing its Azure Enterprise Platform and integrating OpenAI’s models into its business and consumer products (GitHub, Office, Bing).

Stability AI Business Model

Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.

Stability AI Ecosystem


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