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Training Data Bias
                 Training  data  bias  occurs  when  the  data  used  to  develop  AI  systems  is
                 unrepresentative, incomplete, or skewed. If the data reflects existing prejudices
                 or excludes certain groups, the AI system may learn and perpetuate these biases.
                 AI systems make decisions based on data they are trained on, so it is crucial to
                 check datasets for bias. One way to do this is to examine whether certain groups
                 are over- or under-represented in the data. For example, a medical AI system
                 trained on data from male patients may not perform well for female patients,
                 leading to misdiagnoses.
                 Similarly, an AI system used for loan approvals might be biased if the training
                 data primarily includes applicants from affluent neighbourhoods, disregarding those from poorer areas. Bias can also
                 occur in data labelling.


                                                Algorithmic Bias
                                                Algorithmic bias refers to the bias that may exist in the design, implementation,
                                                and outcomes of algorithms used in AI systems. This bias can result in unfair or
                                                discriminatory outcomes, often reflecting the prejudices or limitations of the data
                                                used to train the algorithm, as well as the assumptions and decisions made by
                                                developers during the algorithm’s creation.
                                                For example, if an AI algorithm used in hiring processes is trained on historical
                                                data  that  reflects  biased  hiring  decisions,  such  as  favouring  one  demographic
                                                group over another, the algorithm may perpetuate these biases when making new
                                                hiring recommendations.
                 Addressing algorithmic bias requires careful consideration of the data used to train algorithms, as well as transparency
                 and accountability in the design and implementation processes. Techniques such as bias detection, fairness-aware
                 algorithms, and diverse data collection can help mitigate algorithmic bias and promote more equitable outcomes in
                 AI systems.


                 Cognitive Bias
                 Cognitive bias refers to systematic patterns of deviation from rationality or objectivity
                 in judgment or decision-making. These biases are often influenced by factors such
                 as emotions, personal experiences, and social norms, leading individuals to make
                 judgments or decisions that may not be entirely rational or impartial.
                 For example, imagine a person who strongly believes that climate change is not real.
                 When presented with scientific evidence supporting climate change, they might
                 dismiss it or interpret it in a way that aligns with their preconceived notion, while
                 ignoring or downplaying evidence to the contrary. This individual’s confirmation
                 bias  prevents  them  from  objectively  considering  added  information  and  may
                 reinforce their existing beliefs, even in the face of contradictory evidence.
                 Cognitive biases can impact various aspects of life, including personal relationships, business decisions, and societal
                 perceptions. Recognising and understanding these biases is essential for making more informed and rational decisions,
                 both individually and collectively. Strategies such as critical thinking, mindfulness, and seeking diverse perspectives
                 can help mitigate the influence of cognitive biases.



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