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AI Biases and discrimination, AI processing - Coggle Diagram

- - - - Datasets with under or over-represented groups could potentially lead to bias
        
        For example
        
        training data for a facial recognition algorithm that over-represents white people may create errors when attempting facial recognition for people of color. Similarly, security data that includes information gathered in geographic areas that are predominantly black could create racial bias in AI tools used by police.
      - Bias can also result from how the training data is labeled.
        
        For example
        
        AI recruiting tools that use inconsistent labeling or exclude or over-represent certain characteristics could eliminate qualified job applicants from consideration.
- - - - are weaknesses or gaps in the organization’s internal processes around AI systems that can lead to bias
        
        such as
        
        Nondiverse teams
        
        Often, because data science and engineering teams working on the development of AI systems lack diversity, they do not have the required knowledge of bias potential in a variety of contexts. For example, a team primarily consisting of White males may not be effective in identifying bias against women of color.
        
        Lack of Focus on Bias Identification
        
        The data scientists or engineers assigned to build AI systems sometimes lack the social science skills to identify bias. AI domain experts tend to place more focus on technical performance and optimization of an AI system than on analyzing potential biases in the data and processing methods. Given the fast pace of development in the emerging technologies industry space, there are also competitive pressures on organizations to operationalize their AI systems quickly, sacrificing bias mitigation efforts for quicker delivery.
        
        Nonidentification of Sensitive Data Attributes and Related Correlations
        
        It is important to identify and define the sensitive data attributes—such as gender and race—that can drive bias in an AI system. However, developing AI systems that ignore such sensitive attributes does not guarantee bias-free processing if related correlations are not addressed
        
        Unclear policies
        
        AI development processes are relatively new for many organizations, and many of those adopting AI technologies are first timers. Traditional organizational policies and procedures do not cover certain key aspects of the AI system development process, such as bias identification and removal.
    - - can influence the AI building process, but they are beyond the organization’s control
        
        such as
        
        Biased Real-World Data
        
        When the test data used to train the AI algorithm are taken from real-world examples and data created by humans, the bias that exists in humans is transferred to the AI system because it uses the real-world data to train itself. The real-world data may not include fair scenarios for all population groups. For example, there may be overrepresentation of certain ethnic groups in the real-world data, which may skew the AI system’s results.
        
        Lack of Detailed Guidance or Frameworks for Bias Identification
        
        Several Countries have begun to regulate AI systems.7 Many professional bodies and international organizations have developed their own versions of AI frameworks. However, these frameworks are still in nascent stages and provide only high-level principles and goals. It is sometimes difficult to tailor them to create actionable procedures and controls for an organization-specific AI system.
        
        Biased Third-Party AI Systems
        
        It is common for an organization to outsource some components of its AI system development to third parties. In such cases, the organization may not thoroughly validate the system for bias due to their reliance on the third parties.