AI Bias

Extracted Attributes

• Impacts

  Unfair outcomes, discrimination (race, gender, sexuality, ethnicity, socioeconomic status), privacy violations, reinforcement of social biases, exacerbation of inequalities, wrongful arrests, poor decision-making, legal liabilities, reputational damage, reduced AI accuracy, fostering mistrust

• Definition

  Systematic and repeatable harmful tendencies in computerized sociotechnical systems that create unfair outcomes, often by privileging one category over another in ways different from the intended function of the algorithm.

• Observed Areas

  Search engine results, social media platforms, criminal justice, healthcare, hiring, election outcomes, spread of online hate speech

• Primary Causes

  Algorithm design, data collection/selection/training, unanticipated system use, pre-existing cultural/social/institutional expectations, feature/label selection, technical limitations

• Alternative Names

  Algorithmic bias, Machine learning bias, Algorithm bias

• Challenges in Study

  Proprietary nature of algorithms, complexity of algorithms, dynamic and unrepeatable responses

• Sociological Concern

  Algorithms influencing society, politics, institutions; inaccurately projecting greater authority than human expertise (automation bias); displacing human responsibility for outcomes.

• Forms of Bias (2021 Survey)

  Historical bias, representation bias, measurement bias

• Least Biased LLM (All-In Podcast Discussion)

  Elon Musk's Grok

• Potential Sources of LLM Bias (All-In Podcast Discussion)

  DEI initiatives, biased training data

Timeline

• European Union's General Data Protection Regulation (GDPR) proposed, beginning to address algorithmic bias. (Source: Wikipedia)

  2018

• A survey identified multiple forms of algorithmic bias, including historical, representation, and measurement biases. (Source: Wikipedia)

  2021

• European Union's Artificial Intelligence Act proposed, addressing algorithmic bias. (Source: Wikipedia)

  2021

• European Union's Artificial Intelligence Act approved. (Source: Wikipedia)

  2024

• The All-In Podcast discusses a study that found biases in Large Language Models (LLMs) from companies like OpenAI, identifying Elon Musk's Grok as the least biased model among those discussed. (Source: Related Documents)

  Undated

Algorithmic bias

Algorithmic bias describes systematic and repeatable harmful tendency in a computerized sociotechnical system to create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm. Bias can emerge from many factors, including but not limited to the design of the algorithm or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected or used to train the algorithm. For example, algorithmic bias has been observed in search engine results and social media platforms. This bias can have impacts ranging from inadvertent privacy violations to reinforcing social biases of race, gender, sexuality, and ethnicity. The study of algorithmic bias is most concerned with algorithms that reflect "systematic and unfair" discrimination. This bias has only recently been addressed in legal frameworks, such as the European Union's General Data Protection Regulation (proposed 2018) and the Artificial Intelligence Act (proposed 2021, approved 2024). As algorithms expand their ability to organize society, politics, institutions, and behavior, sociologists have become concerned with the ways in which unanticipated output and manipulation of data can impact the physical world. Because algorithms are often considered to be neutral and unbiased, they can inaccurately project greater authority than human expertise (in part due to the psychological phenomenon of automation bias), and in some cases, reliance on algorithms can displace human responsibility for their outcomes. Bias can enter into algorithmic systems as a result of pre-existing cultural, social, or institutional expectations; by how features and labels are chosen; because of technical limitations of their design; or by being used in unanticipated contexts or by audiences who are not considered in the software's initial design. Algorithmic bias has been cited in cases ranging from election outcomes to the spread of online hate speech. It has also arisen in criminal justice, healthcare, and hiring, compounding existing racial, socioeconomic, and gender biases. The relative inability of facial recognition technology to accurately identify darker-skinned faces has been linked to multiple wrongful arrests of black men, an issue stemming from imbalanced datasets. Problems in understanding, researching, and discovering algorithmic bias persist due to the proprietary nature of algorithms, which are typically treated as trade secrets. Even when full transparency is provided, the complexity of certain algorithms poses a barrier to understanding their functioning. Furthermore, algorithms may change, or respond to input or output in ways that cannot be anticipated or easily reproduced for analysis. In many cases, even within a single website or application, there is no single "algorithm" to examine, but a network of many interrelated programs and data inputs, even between users of the same service. A 2021 survey identified multiple forms of algorithmic bias, including historical, representation, and measurement biases, each of which can contribute to unfair outcomes.

Web Search Results

• What is AI Bias? - Understanding Its Impact, Risks, and ...

  AI bias refers to situations where an AI system produces systematically prejudiced results due to flaws in the machine learning process. This bias often originate from the data used for training, the design of the algorithm, or even the objectives it’s programmed to achieve. AI bias frequently mirrors societal inequalities, leading to discrimination against certain groups based on factors like race, gender, or socioeconomic status. [...] AI bias occurs when artificial intelligence systems produce unfair or prejudiced outcomes due to issues with the data, algorithms, or objectives they’re trained on. Unlike human bias, AI bias is often harder to detect but can have far-reaching consequences, affecting key business operations and public trust. This article explores what AI bias is, how it manifests, and why addressing it is essential to ensure fairness, trust, and compliance with emerging regulations. ## What is AI Bias? [...] AI bias occurs when machine learning algorithms produce prejudiced outcomes due to flawed data, biased algorithms, or skewed objectives. For enterprises, AI bias can lead to poor decision-making, legal liabilities, and reputational damage, particularly in areas like hiring, lending, or healthcare. #### 2. How can we detect and mitigate bias in AI systems?

• What is AI bias? Causes, effects, and mitigation strategies

  What Is # What is AI bias? Artificial intelligence bias, or AI bias, refers to systematic discrimination embedded within AI systems that can reinforce existing biases, and amplify discrimination, prejudice, and stereotyping. Published on October 29, 2024 AI ## Bias in AI explained Bias in AI models typically arises from two sources: the design of models themselves and the training data they use. [...] AI bias can come from several sources that can affect the fairness and reliability of AI systems: Data bias: Biases present in the data used to train AI models can lead to biased outcomes. If the training data predominantly represents certain demographics or contains historical biases, the AI will reflect these imbalances in its predictions and decisions. [...] The impacts of AI bias can be widespread and profound. If left unaddressed, AI bias can deepen social inequalities, reinforce stereotypes, and break laws. Societal inequalities: AI bias can exacerbate existing societal inequalities by disproportionately affecting marginalized communities, leading to further economic and social disparity.

• What Is AI Bias? | IBM

  Artificial Intelligence # What is AI bias? ## Authors James Holdsworth Content Writer ## What is AI bias? AI bias, also called machine learning bias or algorithm bias, refers to the occurrence of biased results due to human biases that skew the original training data or AI algorithm—leading to distorted outputs and potentially harmful outcomes. [...] When AI bias goes unaddressed, it can impact an organization’s success and hinder people’s ability to participate in the economy and society. Bias reduces AI’s accuracy, and therefore its potential. Businesses are less likely to benefit from systems that produce distorted results. And scandals resulting from AI bias could foster mistrust among people of color, women, people with disabilities, the LGBTQ community, or other marginalized groups. [...] ## Real-world examples and risks When AI makes a mistake due to bias—such as groups of people denied opportunities, misidentified in photos or punished unfairly—the offending organization suffers damage to its brand and reputation. At the same time, the people in those groups and society as a whole can experience harm without even realizing it. Here are a few high-profile examples of disparities and bias in AI and the harm they can cause.

• 14 Real AI Bias Examples & Mitigation Guide

  August 19, 2025 # AI Bias: 14 Real AI Bias Examples & Mitigation Guide Medha Mehta & ## What Exactly is AI Bias? AI bias refers to systematic and unfair discrimination in the outputs of an artificial intelligence system due to biased data, algorithms, or assumptions. In simple terms, if an AI is trained on data that reflects human or societal prejudices (like racism, sexism, etc.), it can learn and reproduce those same biases in its decisions or predictions.

• AI bias: exploring discriminatory algorithmic decision-making ...

  AI Bias is when the output of a machine-learning model can lead to the discrimination against specific groups or individuals. These tend to be groups that have been historically discriminated against and marginalised based on gender, social class, sexual orientation or race, but not in all cases. This could be because of prejudiced assumptions in the process of developing the model, or non-representative, inaccurate or simply wrong training data. It is important to highlight that bias means a [...] 8. Algorithmic bias. Algorithmic bias is when the bias is not actually in the input data and is created by the algorithm . This article, as it is common in AI ethics literature, will concentrate on the problematic cases in which the outcome of bias may lead to discrimination by AI-based automated decision-making environments and an awareness of the different types can be helpful. Algorithmic decision-making that discriminates and the problem with data