- Systemic bias in tech refers to the unjust practices, rules and results that disproportionately impact people from underrepresented communities.
- There are several types of tech data bias, including algorithmic bias, representation bias and design bias.
- Companies can take several steps to mitigate bias in tech, including implementing inclusive hiring practices and creating ethical guidelines and training programs.
As society continues to advance digitally, access to technology and other digital resources has become an essential part of everyday life. If harnessed properly, technology can help empower people and improve society. However, technology and digital literacy — and the opportunities that come with them — are not fully accessible to everyone and can even harm some. This issue, known as systemic bias in tech, perpetuates the digital divide, systemic racism and other longstanding inequities for marginalized communities.
Ahead, we discuss the definition of systemic bias in tech, the different types and the current state of bias in tech. In addition, we highlight the potential consequences of systemic bias in tech and how companies can mitigate them.
What Is Systemic Bias in Tech?
Systemic bias in tech is a multifaceted issue that refers to the unfair and discriminatory practices, rules, results and structures that negatively impact people from underrepresented communities in relation to technology. This bias can occur in different forms, including unjust datasets, prejudiced algorithms, inequitable hiring and advancement opportunities.
Types of Bias in Tech
Bias in tech shows up in two primary categories: human bias and data bias. Human bias, also known as conscious or unconscious bias, can include preconceived prejudices, stereotypes or systemic inequities the creators of new technologies may have as individuals.
These human biases can create data bias by influencing how new technology creators collect, interpret and label data. The biases can then impact data functionality or results. Some of the most common types of data bias include algorithmic bias, representation bias and decision bias.
- Algorithmic bias: This form of bias stems from inputting skewed or limited data into a new technology while it is being developed. Implementing skewed data commonly leads to recurring errors that create inequitable results.
- Representation bias: Representation bias happens when groups of people are disproportionately represented in datasets. This influence leads to unfair outcomes in machine learning and inequitable decisions.
- Design bias: This form of bias represents the discriminatory actions or beliefs that are embedded in algorithms or technologies throughout the design process. A biased design commonly results in unintentional inequities or discriminatory outcomes.
The Current State of Systemic Bias in Tech
For the last few decades, the tech industry has continued to lag in progress in terms of diversity efforts and even representation in the job force. This issue is not limited to one group. The tech industry lacks equitable representation of various groups, including people of different genders, races and socio-economic backgrounds.
According to a 2023 study from McKinsey & Company, only 3% of executives in the technology industry are Black. In addition, 2022 data from Zippia reveals that while females account for 47% of the U.S. workforce, they represent just 28% of tech workers.
An increasing amount of discrimination exacerbates the diversity gap in the tech industry. A study conducted by Dice shows that out of 2,500 tech workers surveyed, 24% reported experiences of racial discrimination, which is up from 18% the year before. The study shows that cases of gender discrimination increased to 26% in 2022 compared to 21% in 2021.
The tech diversity gap is closely tied to systemic bias, including systemic racial bias, in tech. New technologies and algorithms are a direct reflection of the perspectives and biases of those creating them. A lack of diversity in the tech workforce can lead to a variety of potential consequences.
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The Potential Consequences of Bias in Tech
Systemic bias in tech can cause discriminatory outcomes, marginalize underrepresented communities and limit business opportunities at companies. While developments in new technologies, such as artificial intelligence (AI), are moving society forward, these technologies also have exhibited systemic bias. This includes the misclassification of people from different racial backgrounds as animals and bias in facial recognition systems.
Below, we explore the consequences of tech bias in more detail.
- Healthcare disparities: Bias in tech can perpetuate healthcare disparities by shaping algorithms used in medical decisions, which can lead to unfair treatments and diagnoses for people from underrepresented communities. A prime example occurred in 2022 when Apple was sued over claims that the blood oxygen sensor on its watch products is biased against individuals with darker skin tones.
- Economic impact: This form of tech bias can contribute to substantial economic inequities, which can hinder fair access to employment opportunities, credit and other financial services. Ultimately, this can lead to income disparities and limit the overall economic mobility of underrepresented communities.
- Reinforcement of stereotypes: Tech bias can emphasize negative stereotypes by spreading biases in algorithms, which can lead to the creation of technologies that reinforce prejudices. For example, Bloomberg experimented using the AI image generator Stable Diffusion to develop images depicting different job titles. When they prompted the generator to develop images for traditionally high-paying and low-paying jobs, many images for high-paying jobs depicted people with lighter skin. On the flip side, the generator returned images of workers with primarily darker skin tones for images of lower-paying jobs.
How Companies Can Address Bias in Tech
In recent years, tech companies like Google have made promises to prioritize equity in their products and throughout their organizations. While this is encouraging, organizations need to do more to establish meaningful change. Below are five ways that companies can begin to address bias in tech.
- Implement diverse and inclusive hiring practices
- Incorporate bias detection and mitigation tools
- Create ethical guidelines and standards
- Develop annual training programs to raise awareness
- Be transparent about how tech or algorithms work
To learn more about topics, such as systemic bias in tech, systemic racism in education, examples of systemic racism and others, follow philanthropist and entrepreneur Robert F. Smith on LinkedIn.
