AI Bias refers to the presence of unfair and discriminatory outcomes in artificial intelligence systems, often stemming from biased training data or the algorithms themselves. It involves the reflection of existing societal biases in the decision-making processes of AI systems. It refers to AI systems that produce biased results that reflect and perpetuate human biases within a society, including historical and current social inequality.
What are the 2 main types of AI bias?
- Selection Bias: Occurs when the training data used to develop AI models is not representative of the diverse population it aims to serve, leading to skewed predictions.
- Algorithmic Bias: Arises from the design and coding of algorithms, where certain features or patterns are given more importance, resulting in biased outcomes.
Why bias in AI?
Bias in AI can lead to discriminatory and unfair outcomes, reinforcing existing prejudices and negatively impacting individuals or groups. Understanding and addressing AI bias is crucial for building ethical and equitable AI systems. One of the major problems is insufficient training data where some demographic groups are missing or underrepresented.
Who is the father of AI?
The term "father of AI" is often attributed to Alan Turing, a British mathematician, logician, and computer scientist. Turing made significant contributions to the development of theoretical computer science and laid the groundwork for AI.
How do you identify AI bias?
- Auditing Algorithms: Reviewing algorithms for biased patterns or features.
- Evaluating Training Data: Assessing whether training data is representative.
- Engaging Diverse Stakeholders: Involving individuals from diverse backgrounds in the development process.
Examples of AI bias
- Facial Recognition Gender Bias:Some facial recognition systems exhibit gender bias, with higher error rates for certain demographics, particularly women and people with darker skin tones.
- Biased Hiring Algorithms: AI algorithms used in hiring processes may inadvertently favour certain demographics, leading to biased recruitment outcomes.
- Criminal Justice Bias: Predictive policing algorithms have been criticized for perpetuating bias in law enforcement, leading to the over-policing of specific communities.
Related terms
- Algorithmic Bias: An error resulting from bad training data and poor programming that causes models to make prejudiced decisions.
- Implicit Bias: Bias that we are not conscious of and how it can affect an event’s outcomes.
- Sampling Bias: Bias that occurs when the training data is not representative or is selected without proper randomization.
Conclusion
In conclusion, AI bias poses significant challenges to the ethical deployment of artificial intelligence. The two main types of AI bias, selection bias and algorithmic bias, underscore the complexity of the issue.
The presence of bias in AI can lead to discriminatory outcomes in various applications, from facial recognition systems to hiring algorithms and predictive policing.
Understanding and identifying AI bias require a multifaceted approach, involving the auditing of algorithms, evaluating training data, and engaging diverse stakeholders in the development process. The examples provided highlight real-world instances of AI bias, emphasizing the need for ongoing scrutiny and corrective measures.
References
- https://research.aimultiple.com/ai-bias/
- https://www.ibm.com/blog/shedding-light-on-ai-bias-with-real-world-examples/
- https://www.weforum.org/agenda/2021/07/ai-machine-learning-bias-discrimination/
- https://mostly.ai/blog/why-bias-in-ai-is-a-problem
- https://www.analytixlabs.co.in/blog/father-of-artificial-intelligence/